Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one...Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one of the most important technologies in network security detection.The accuracy of network intrusion detection has reached higher accuracy so far.However,these methods have very low efficiency in network intrusion detection,even the most popular SOM neural network method.In this paper,an efficient and fast network intrusion detection method was proposed.Firstly,the fundamental of the two different methods are introduced respectively.Then,the selforganizing feature map neural network based on K-means clustering(KSOM)algorithms was presented to improve the efficiency of network intrusion detection.Finally,the NSLKDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than Kmeans method.The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration.展开更多
Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annu...Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.展开更多
Haze-fog,which is an atmospheric aerosol caused by natural or man-made factors,seriously affects the physical and mental health of human beings.PM2.5(a particulate matter whose diameter is smaller than or equal to 2.5...Haze-fog,which is an atmospheric aerosol caused by natural or man-made factors,seriously affects the physical and mental health of human beings.PM2.5(a particulate matter whose diameter is smaller than or equal to 2.5 microns)is the chief culprit causing aerosol.To forecast the condition of PM2.5,this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM2.5.Since the meteorological data and air pollutes data are typical time series data,it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network(SSHL-LSTMNN)containing memory capability to implement the prediction.However,the number of neurons in the hidden layer is difficult to decide unless manual testing is operated.In order to decide the best structure of the neural network and improve the accuracy of prediction,this paper employs a self-organizing algorithm,which uses Information Processing Capability(IPC)to adjust the number of the hidden neurons automatically during a learning phase.In a word,to predict PM2.5 concentration accurately,this paper proposes the SSHL-LSTMNN to predict PM2.5 concentration.In the experiment,not only the hourly precise prediction but also the daily longer-term prediction is taken into account.At last,the experimental results reflect that SSHL-LSTMNN performs the best.展开更多
Combining with the characters of the practicing qualification personnel in construction market,evaluation method based on the self-organizing neural network is brought out to analyze the credit classification of the p...Combining with the characters of the practicing qualification personnel in construction market,evaluation method based on the self-organizing neural network is brought out to analyze the credit classification of the practicing qualification personnel. And the impact factors on the credit classification of the practicing qualification personnel,such as the number of neurons,the training steps,the dimension of neurons and the field of winning neurons are studied. Then a self-organizing competitive neural network is built. At last,a case study is conducted by taking practicing qualification personnel as an example. The research result reveals that the method can efficiently evaluate the credit of the practicing qualification personnel;thus,it could provide scientific advice to the construction enterprise to prevent relevant discreditable behaviors of some practicing qualification personnel.展开更多
The artificial neural networks (ANNs), among different soft computing methodologies are widely used to meet the challenges thrown by the main objectives of data mining classification techniques, due to their robust, p...The artificial neural networks (ANNs), among different soft computing methodologies are widely used to meet the challenges thrown by the main objectives of data mining classification techniques, due to their robust, powerful, distributed, fault tolerant computing and capability to learn in a data-rich environment. ANNs has been used in several fields, showing high performance as classifiers. The problem of dealing with non numerical data is one major obstacle prevents using them with various data sets and several domains. Another problem is their complex structure and how hands to interprets. Self-Organizing Map (SOM) is type of neural systems that can be easily interpreted, but still can’t be used with non numerical data directly. This paper presents an enhanced SOM structure to cope with non numerical data. It used DNA sequences as the training dataset. Results show very good performance compared to other classifiers. For better evaluation both micro-array structure and their sequential representation as proteins were targeted as dataset accuracy is measured accordingly.展开更多
The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on s...The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on self-organizing mapping neural network and deep feature matching.In this model,word vector,two-way LSTM,2D neural network and other deep learning models are used to extract the semantic matching features of question-answer pairs.Self-organizing mapping(SOM)is used to classify and identify the sentence feature.The attention mechanism-based neural machine translation model is taken as the baseline system.The experimental results show that this framework significantly improves the adequacy of English-Chinese machine translation and achieves better results than the traditional attention mechanism-based English-Chinese machine translation model.展开更多
A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF ...A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.展开更多
Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this pap...Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.展开更多
Electric load forecasting has been a major area of research in the last decade since the production of accurate short-term forecasts for electricity loads has proven to be a key to success for many of the decision mak...Electric load forecasting has been a major area of research in the last decade since the production of accurate short-term forecasts for electricity loads has proven to be a key to success for many of the decision makers in the energy sector, from power generation to operation of the system. The objective of this research is to analyze the capacity of the MLP (multilayer perceptron neural network) versus SOM (self-organizing map neural network) for short-term load forecasting. The MLP is one of the most commonly used networks. It can be used for classification problems, model construction, series forecasting and discrete control. On the other hand, the SOM is a type of artificial neural network that is trained using unsupervised data to produce a low-dimensional, discretized representation of an input space of training samples in a cell map. Historical data of real global load demand were used for the research. Both neural models provide good prediction results, but the results obtained with the SOM maps are markedly better Also the main advantage of SOM maps is that they reach good results as a network unsupervised. It is much easier to train and interpret the results.展开更多
The local visual motion detection mechanism used in the visual systems of primatescan only sense the motion component oriented perpendicularly to the contrast gradient of thebrightness pattern.But the visual system of...The local visual motion detection mechanism used in the visual systems of primatescan only sense the motion component oriented perpendicularly to the contrast gradient of thebrightness pattern.But the visual system of higher animals can adaptively determine the actualdirection of motion through a learning process.In this paper a multilayered feedforward neuralnetwork model for perception of visual motion is presented.This model employs W.Reichardt’selementary motion detectors array and T.Kohonen’s self-organizing feature map.We explored theself-organizing principles for perception of visual motion.The computer simulations show thatthis neural network is able to recognize the true direction of motion through an unsupervisedlearning process.In addition,the neurons with the same or similar motion direction selectivitytend to appear in“functional columns”which seem to be qualitatively similar to the corticalmotion columns observed by electrophysiological and cytohistochemical studies in certain higherareas such as MT.It proves that motion-detection by spatio-temporal coherences,mapping,co-operation,competition,and Hebb rule may be the basic principles for the self-organization ofvisual motion perception networks.展开更多
Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification result...Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification results can be obtained with the neural network method through getting knowledge from environments and adjusting the parameter (or weight) step by step by a specific measurement. This paper focuses on the double-layer structured Kohonen self-organizing feature map (SOFM), for which all neurons within the two layers are linked one another and those of the competition layers are linked as well along the sides. Therefore, the self-adapting learning ability is improved due to the effective competition and suppression in this method. The SOFM has become a hot topic in the research area of remote sensing data classi-fication. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a new satellite-borne remote sensing instrument with three 15-m resolution bands and three 30-m resolution bands at the near infrared. The ASTER data of Dagang district, Tianjin Munici-pality is used as the test data in this study. At first, the wavelet fusion is carried out to make the spatial resolutions of the ASTER data identical; then, the SOFM method is applied to classifying the land cover types. The classification results are compared with those of the maximum likeli-hood method (MLH). As a consequence, the classification accuracy of SOFM increases about by 7% in general and, in particular, it is almost as twice as that of the MLH method in the town.展开更多
The paper deals with a new VQ+DPCM+DCT algorithm based on Self-Organizing Feature Maps(SOFM) algorithm for image coding. In addition. a Frequency sensitive SOFM (FSOFM) has been also devel-oped. Simulation results sh...The paper deals with a new VQ+DPCM+DCT algorithm based on Self-Organizing Feature Maps(SOFM) algorithm for image coding. In addition. a Frequency sensitive SOFM (FSOFM) has been also devel-oped. Simulation results show that a very good visual quality of the coded image at 0.252 bits/pixel is obtained.展开更多
When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes t...When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.展开更多
Vegetation classification is an important topic in plant ecology and many quantitative techniques for classification have been developed in the field.The artificial neural network is a comparatively new tool for data ...Vegetation classification is an important topic in plant ecology and many quantitative techniques for classification have been developed in the field.The artificial neural network is a comparatively new tool for data analysis.The self-organizing feature map(SOFM)is powerful tool for clustering analysis.SOFM has been applied to many research fields and it was applied to the classification of plant communities in the Pangquangou Nature Reserve in the present work.Pangquangou Nature Reserve,located at 37°20′–38°20′ N,110°18′–111°18′ E,is a part of the Luliang Mountain range.Eighty-nine samples(quadrats)of 10 m×10 m for forest,4 m×4 m for shrubland and 1 m×1 m for grassland along an elevation gradient,were set up and species data was recorded in each sample.After discussion of the mathematical algorism,clustering technique and the procedure of SOFM,the classification was carried out by using NNTool box in MATLAB(6.5).As a result,the 89 samples were clustered into 13 groups representing 13 types of plant communities.The characteristics of each community were described.The result of SOFM classification was identical to the result of fuzzy c-mean clustering and consistent with the distribution patterns of vegetation in the study area and shows significant ecological meanings.This suggests that SOFM may clearly describe the ecological relationships between plant communities and it is a very effective quantitative technique in plant ecology research.展开更多
This work introduces a deep-learning network,i.e.,multi-input self-organizing-map ResNet(MISR),for modeling refining units comprised of two reactors and a separation train.The model is comprised of self-organizing-map...This work introduces a deep-learning network,i.e.,multi-input self-organizing-map ResNet(MISR),for modeling refining units comprised of two reactors and a separation train.The model is comprised of self-organizing-map and the neural network parts.The self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network part.In the neural network part,residual blocks enhance the convergence and accuracy,ensuring that the structure will not be overfitted easily.Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the products.The results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model,thus leading to more accurate optimization of the hydrocracker operation.Moreover,the MISR model has smoother error convergence than the previous model.Optimal operating conditions have been determined via multi-round-particle-swarm and differential evolution algorithms.Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.展开更多
Detecting communities from complex networks is an important issue and has attracted attention of researchers in many fields. It is relevant to social tasks, biological inquiries, and technological problems since vario...Detecting communities from complex networks is an important issue and has attracted attention of researchers in many fields. It is relevant to social tasks, biological inquiries, and technological problems since various networks exist in these systems. This paper proposes a new self-organizing map (SOM) based approach to community detection. By adopting a new operation and a new weightupdating scheme, a complex network can be organized into dense subgraphs according to the topological connection of each node by the SOM algorithm. Extensive numerical experiments show that the performance of the SOM algorithm is good. It can identify communities more accurately than existing methods. This method can be used to detect communities not only in undirected networks, but also in directed networks and bipartite networks.展开更多
Due to the fact that the emergency medicine distribution is vital to the quick response to urgent demand when an epidemic occurs, the optimal vaccine distribution approach is explored according to the epidemic diffusi...Due to the fact that the emergency medicine distribution is vital to the quick response to urgent demand when an epidemic occurs, the optimal vaccine distribution approach is explored according to the epidemic diffusion rule and different urgency degrees of affected areas with the background of the epidemic outbreak in a given region. First, the SIQR (susceptible, infected, quarantined,recovered) epidemic model with pulse vaccination is introduced to describe the epidemic diffusion rule and obtain the demanded vaccine in each pulse. Based on the SIQR model, the affected areas are clustered by using the self-organizing map (SOM) neutral network to qualify the results. Then, a dynamic vaccine distribution model is formulated, incorporating the results of clustering the affected areas with the goals of both reducing the transportation cost and decreasing the unsatisfied demand for the emergency logistics network. Numerical study with twenty affected areas and four distribution centers is carried out. The corresponding numerical results indicate that the proposed approach can make an outstanding contribution to controlling the affected areas with a relatively high degree of urgency, and the comparison results prove that the performance of the clustering method is superior to that of the non-clustering method on controlling epidemic diffusion.展开更多
The mechanism underlying neurogenesis during embryonic spinal cord development involves a specific ligand/receptor interaction,which may be help guide neuroengineering to boost stem cell-based neural regeneration for ...The mechanism underlying neurogenesis during embryonic spinal cord development involves a specific ligand/receptor interaction,which may be help guide neuroengineering to boost stem cell-based neural regeneration for the structural and functional repair of spinal cord injury.Herein,we hypothesized that supplying spinal cord defects with an exogenous neural network in the NT-3/fibroin-coated gelatin sponge(NF-GS)scaffold might improve tissue repair efficacy.To test this,we engineered tropomyosin receptor kinase C(TrkC)-modified neural stem cell(NSC)-derived neural network tissue with robust viability within an NF-GS scaffold.When NSCs were genetically modified to overexpress TrkC,the NT-3 receptor,a functional neuronal population dominated the neural network tissue.The pro-regenerative niche allowed the long-term survival and phenotypic maintenance of the donor neural network tissue for up to 8 weeks in the injured spinal cord.Additionally,host nerve fibers regenerated into the graft,making synaptic connections with the donor neurons.Accordingly,motor function recovery was significantly improved in rats with spinal cord injury(SCI)that received TrkC-modified NSC-derived neural network tissue transplantation.Together,the results suggested that transplantation of the neural network tissue formed in the 3D bioactive scaffold may represent a valuable approach to study and develop therapies for SCI.展开更多
A wheeled mobile mechanism with a passive and/or active linkage mechanism for rough terrain environment is developed and evaluated. The wheeled mobile mechanism which has high mobility in rough terrain needs sophistic...A wheeled mobile mechanism with a passive and/or active linkage mechanism for rough terrain environment is developed and evaluated. The wheeled mobile mechanism which has high mobility in rough terrain needs sophisticated system to adapt various environments. We focus on the development of a switching controller system for wheeled mobile robots in rough terrain. This system consists of two sub-systems: an environment recognition system using link angles and an adaptive control system. In the environment recognition system, we introduce a Self-Organizing Map (SOM) for clustering link angles. In the adaptive controllers, we introduce neural networks to calculate the inverse model of the wheeled mobile robot. The environment recognition system can recognize the environment in which the robot travels, and the adjustable controllers are tuned by experimental results for each environment. The dual sub-system switching controller system is experimentally evaluated. The system recognizes its environment and adapts by switching the adjustable controllers. This system demonstrates superior performance to a well-tuned single PID controller.展开更多
The purpose of reverse engineering is to convert a large point cloud into a CAD model. In reverse engineering, the key issue is segmentation, i.e. studying how to subdivide the point cloud into smaller regions, where ...The purpose of reverse engineering is to convert a large point cloud into a CAD model. In reverse engineering, the key issue is segmentation, i.e. studying how to subdivide the point cloud into smaller regions, where each of them can be approximated by a single surface. Segmentation is relatively simple, if regions are bounded by sharp edges and small blends; problems arise when smoothly connected regions need to be separated. In this paper, a modified self-organizing feature map neural network (SOFM) is used to solve segmentation problem. Eight dimensional feature vectors (3-dimensional coordinates, 3-dimensional normal vectors, Gaussian curvature and mean curvature) are taken as input for SOFM. The weighted Euclidean distance measure is used to improve segmentation result. The method not only can deal with regions bounded by sharp edges, but also is very efficient to separating smoothly connected regions. The segmentation method using SOFM is robust to noise, and it operates directly on the point cloud. An examples is given to show the effect of SOFM algorithm.展开更多
文摘Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one of the most important technologies in network security detection.The accuracy of network intrusion detection has reached higher accuracy so far.However,these methods have very low efficiency in network intrusion detection,even the most popular SOM neural network method.In this paper,an efficient and fast network intrusion detection method was proposed.Firstly,the fundamental of the two different methods are introduced respectively.Then,the selforganizing feature map neural network based on K-means clustering(KSOM)algorithms was presented to improve the efficiency of network intrusion detection.Finally,the NSLKDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than Kmeans method.The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration.
基金supported by the National Key R&D Program of China (GrantN o.2016YFC0401407)National Natural Science Foundation of China (Grant Nos. 51479003 and 51279006)
文摘Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.
文摘Haze-fog,which is an atmospheric aerosol caused by natural or man-made factors,seriously affects the physical and mental health of human beings.PM2.5(a particulate matter whose diameter is smaller than or equal to 2.5 microns)is the chief culprit causing aerosol.To forecast the condition of PM2.5,this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM2.5.Since the meteorological data and air pollutes data are typical time series data,it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network(SSHL-LSTMNN)containing memory capability to implement the prediction.However,the number of neurons in the hidden layer is difficult to decide unless manual testing is operated.In order to decide the best structure of the neural network and improve the accuracy of prediction,this paper employs a self-organizing algorithm,which uses Information Processing Capability(IPC)to adjust the number of the hidden neurons automatically during a learning phase.In a word,to predict PM2.5 concentration accurately,this paper proposes the SSHL-LSTMNN to predict PM2.5 concentration.In the experiment,not only the hourly precise prediction but also the daily longer-term prediction is taken into account.At last,the experimental results reflect that SSHL-LSTMNN performs the best.
文摘Combining with the characters of the practicing qualification personnel in construction market,evaluation method based on the self-organizing neural network is brought out to analyze the credit classification of the practicing qualification personnel. And the impact factors on the credit classification of the practicing qualification personnel,such as the number of neurons,the training steps,the dimension of neurons and the field of winning neurons are studied. Then a self-organizing competitive neural network is built. At last,a case study is conducted by taking practicing qualification personnel as an example. The research result reveals that the method can efficiently evaluate the credit of the practicing qualification personnel;thus,it could provide scientific advice to the construction enterprise to prevent relevant discreditable behaviors of some practicing qualification personnel.
文摘The artificial neural networks (ANNs), among different soft computing methodologies are widely used to meet the challenges thrown by the main objectives of data mining classification techniques, due to their robust, powerful, distributed, fault tolerant computing and capability to learn in a data-rich environment. ANNs has been used in several fields, showing high performance as classifiers. The problem of dealing with non numerical data is one major obstacle prevents using them with various data sets and several domains. Another problem is their complex structure and how hands to interprets. Self-Organizing Map (SOM) is type of neural systems that can be easily interpreted, but still can’t be used with non numerical data directly. This paper presents an enhanced SOM structure to cope with non numerical data. It used DNA sequences as the training dataset. Results show very good performance compared to other classifiers. For better evaluation both micro-array structure and their sequential representation as proteins were targeted as dataset accuracy is measured accordingly.
文摘The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on self-organizing mapping neural network and deep feature matching.In this model,word vector,two-way LSTM,2D neural network and other deep learning models are used to extract the semantic matching features of question-answer pairs.Self-organizing mapping(SOM)is used to classify and identify the sentence feature.The attention mechanism-based neural machine translation model is taken as the baseline system.The experimental results show that this framework significantly improves the adequacy of English-Chinese machine translation and achieves better results than the traditional attention mechanism-based English-Chinese machine translation model.
基金Projects(60974031,60704011,61174128)supported by the National Natural Science Foundation of China
文摘A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.
基金Supported in part by the National Natural Science Foundation of China (No.60272046, No.60102011), Na-tional High Technology Project of China (No.2002AA143010), Natural Science Foundation of Jiangsu Province (No.BK2001042), and the Foundation for Excellent Doctoral Dissertation of Southeast Univer-sity (No.YBJJ0412).
文摘Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.
文摘Electric load forecasting has been a major area of research in the last decade since the production of accurate short-term forecasts for electricity loads has proven to be a key to success for many of the decision makers in the energy sector, from power generation to operation of the system. The objective of this research is to analyze the capacity of the MLP (multilayer perceptron neural network) versus SOM (self-organizing map neural network) for short-term load forecasting. The MLP is one of the most commonly used networks. It can be used for classification problems, model construction, series forecasting and discrete control. On the other hand, the SOM is a type of artificial neural network that is trained using unsupervised data to produce a low-dimensional, discretized representation of an input space of training samples in a cell map. Historical data of real global load demand were used for the research. Both neural models provide good prediction results, but the results obtained with the SOM maps are markedly better Also the main advantage of SOM maps is that they reach good results as a network unsupervised. It is much easier to train and interpret the results.
基金Supported in part by the National Natural Science Foundation of China National Laboratory of Pattern Recognition,Institute of Automation,Academia Sinica.
文摘The local visual motion detection mechanism used in the visual systems of primatescan only sense the motion component oriented perpendicularly to the contrast gradient of thebrightness pattern.But the visual system of higher animals can adaptively determine the actualdirection of motion through a learning process.In this paper a multilayered feedforward neuralnetwork model for perception of visual motion is presented.This model employs W.Reichardt’selementary motion detectors array and T.Kohonen’s self-organizing feature map.We explored theself-organizing principles for perception of visual motion.The computer simulations show thatthis neural network is able to recognize the true direction of motion through an unsupervisedlearning process.In addition,the neurons with the same or similar motion direction selectivitytend to appear in“functional columns”which seem to be qualitatively similar to the corticalmotion columns observed by electrophysiological and cytohistochemical studies in certain higherareas such as MT.It proves that motion-detection by spatio-temporal coherences,mapping,co-operation,competition,and Hebb rule may be the basic principles for the self-organization ofvisual motion perception networks.
文摘Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification results can be obtained with the neural network method through getting knowledge from environments and adjusting the parameter (or weight) step by step by a specific measurement. This paper focuses on the double-layer structured Kohonen self-organizing feature map (SOFM), for which all neurons within the two layers are linked one another and those of the competition layers are linked as well along the sides. Therefore, the self-adapting learning ability is improved due to the effective competition and suppression in this method. The SOFM has become a hot topic in the research area of remote sensing data classi-fication. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a new satellite-borne remote sensing instrument with three 15-m resolution bands and three 30-m resolution bands at the near infrared. The ASTER data of Dagang district, Tianjin Munici-pality is used as the test data in this study. At first, the wavelet fusion is carried out to make the spatial resolutions of the ASTER data identical; then, the SOFM method is applied to classifying the land cover types. The classification results are compared with those of the maximum likeli-hood method (MLH). As a consequence, the classification accuracy of SOFM increases about by 7% in general and, in particular, it is almost as twice as that of the MLH method in the town.
文摘The paper deals with a new VQ+DPCM+DCT algorithm based on Self-Organizing Feature Maps(SOFM) algorithm for image coding. In addition. a Frequency sensitive SOFM (FSOFM) has been also devel-oped. Simulation results show that a very good visual quality of the coded image at 0.252 bits/pixel is obtained.
基金supported by the AG600 project of AVIC General Huanan Aircraft Industry Co.,Ltd.
文摘When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.
基金This study was supported by the National Natural Science Foundation(Grant No.30070140)the Teachers’Foundation of the Education Ministry of China.
文摘Vegetation classification is an important topic in plant ecology and many quantitative techniques for classification have been developed in the field.The artificial neural network is a comparatively new tool for data analysis.The self-organizing feature map(SOFM)is powerful tool for clustering analysis.SOFM has been applied to many research fields and it was applied to the classification of plant communities in the Pangquangou Nature Reserve in the present work.Pangquangou Nature Reserve,located at 37°20′–38°20′ N,110°18′–111°18′ E,is a part of the Luliang Mountain range.Eighty-nine samples(quadrats)of 10 m×10 m for forest,4 m×4 m for shrubland and 1 m×1 m for grassland along an elevation gradient,were set up and species data was recorded in each sample.After discussion of the mathematical algorism,clustering technique and the procedure of SOFM,the classification was carried out by using NNTool box in MATLAB(6.5).As a result,the 89 samples were clustered into 13 groups representing 13 types of plant communities.The characteristics of each community were described.The result of SOFM classification was identical to the result of fuzzy c-mean clustering and consistent with the distribution patterns of vegetation in the study area and shows significant ecological meanings.This suggests that SOFM may clearly describe the ecological relationships between plant communities and it is a very effective quantitative technique in plant ecology research.
基金supported by the National Natural Science Fund for Distinguished Young Scholars(Grant No.61725301)the National Natural Science Foundation of China(Basic Science Center Program:Grant No.61988101)+1 种基金International(Regional)Cooperation and Exchange Project(Grant No.61720106008)General Program(Grant No.61873093).
文摘This work introduces a deep-learning network,i.e.,multi-input self-organizing-map ResNet(MISR),for modeling refining units comprised of two reactors and a separation train.The model is comprised of self-organizing-map and the neural network parts.The self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network part.In the neural network part,residual blocks enhance the convergence and accuracy,ensuring that the structure will not be overfitted easily.Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the products.The results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model,thus leading to more accurate optimization of the hydrocracker operation.Moreover,the MISR model has smoother error convergence than the previous model.Optimal operating conditions have been determined via multi-round-particle-swarm and differential evolution algorithms.Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.
基金This research is supported by the National Natural Science Foundation of China under Grant Nos 10631070, 60873205, 10701080, and the Beijing Natural Science Foundation under Grant No. 1092011. It is also partially supported by the Foundation of Beijing Education Commission under Grant No. SM200910037005, the Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (PHR201006217), and the Foundation of WYJD200902.
文摘Detecting communities from complex networks is an important issue and has attracted attention of researchers in many fields. It is relevant to social tasks, biological inquiries, and technological problems since various networks exist in these systems. This paper proposes a new self-organizing map (SOM) based approach to community detection. By adopting a new operation and a new weightupdating scheme, a complex network can be organized into dense subgraphs according to the topological connection of each node by the SOM algorithm. Extensive numerical experiments show that the performance of the SOM algorithm is good. It can identify communities more accurately than existing methods. This method can be used to detect communities not only in undirected networks, but also in directed networks and bipartite networks.
基金The National Natural Science Foundation of China (No.70671021)
文摘Due to the fact that the emergency medicine distribution is vital to the quick response to urgent demand when an epidemic occurs, the optimal vaccine distribution approach is explored according to the epidemic diffusion rule and different urgency degrees of affected areas with the background of the epidemic outbreak in a given region. First, the SIQR (susceptible, infected, quarantined,recovered) epidemic model with pulse vaccination is introduced to describe the epidemic diffusion rule and obtain the demanded vaccine in each pulse. Based on the SIQR model, the affected areas are clustered by using the self-organizing map (SOM) neutral network to qualify the results. Then, a dynamic vaccine distribution model is formulated, incorporating the results of clustering the affected areas with the goals of both reducing the transportation cost and decreasing the unsatisfied demand for the emergency logistics network. Numerical study with twenty affected areas and four distribution centers is carried out. The corresponding numerical results indicate that the proposed approach can make an outstanding contribution to controlling the affected areas with a relatively high degree of urgency, and the comparison results prove that the performance of the clustering method is superior to that of the non-clustering method on controlling epidemic diffusion.
基金the Chinese National Natural Science Foundation of China(81891003)the National Key R&D Program of China(2017YFA0104700)and the 111 Project for Academic Exchange Program(B13037)to Y.S.Zeng+2 种基金the Foundation of Guangdong Province(2017B020210012)to Y.S.Zeng and X.Zengthe Co-innovation Foundation of Guangzhou City(201704020221)to Y.S.Zeng,X.Zeng,and G.Lithe Start-up Foundation of Guangdong Province(Grant No.2018A030310113)to G.Li.
文摘The mechanism underlying neurogenesis during embryonic spinal cord development involves a specific ligand/receptor interaction,which may be help guide neuroengineering to boost stem cell-based neural regeneration for the structural and functional repair of spinal cord injury.Herein,we hypothesized that supplying spinal cord defects with an exogenous neural network in the NT-3/fibroin-coated gelatin sponge(NF-GS)scaffold might improve tissue repair efficacy.To test this,we engineered tropomyosin receptor kinase C(TrkC)-modified neural stem cell(NSC)-derived neural network tissue with robust viability within an NF-GS scaffold.When NSCs were genetically modified to overexpress TrkC,the NT-3 receptor,a functional neuronal population dominated the neural network tissue.The pro-regenerative niche allowed the long-term survival and phenotypic maintenance of the donor neural network tissue for up to 8 weeks in the injured spinal cord.Additionally,host nerve fibers regenerated into the graft,making synaptic connections with the donor neurons.Accordingly,motor function recovery was significantly improved in rats with spinal cord injury(SCI)that received TrkC-modified NSC-derived neural network tissue transplantation.Together,the results suggested that transplantation of the neural network tissue formed in the 3D bioactive scaffold may represent a valuable approach to study and develop therapies for SCI.
文摘A wheeled mobile mechanism with a passive and/or active linkage mechanism for rough terrain environment is developed and evaluated. The wheeled mobile mechanism which has high mobility in rough terrain needs sophisticated system to adapt various environments. We focus on the development of a switching controller system for wheeled mobile robots in rough terrain. This system consists of two sub-systems: an environment recognition system using link angles and an adaptive control system. In the environment recognition system, we introduce a Self-Organizing Map (SOM) for clustering link angles. In the adaptive controllers, we introduce neural networks to calculate the inverse model of the wheeled mobile robot. The environment recognition system can recognize the environment in which the robot travels, and the adjustable controllers are tuned by experimental results for each environment. The dual sub-system switching controller system is experimentally evaluated. The system recognizes its environment and adapts by switching the adjustable controllers. This system demonstrates superior performance to a well-tuned single PID controller.
基金Supported by the National Natural Science Foundation of China(60573177), the Aeronautical Science Foundation of China (04H53059) , the natural Science Foundation of Henan Province (200510078010) and Youth Science Foundation at North China Institute of Water Conservancy and Hydroelectric Power(HSQJ2004003)
文摘The purpose of reverse engineering is to convert a large point cloud into a CAD model. In reverse engineering, the key issue is segmentation, i.e. studying how to subdivide the point cloud into smaller regions, where each of them can be approximated by a single surface. Segmentation is relatively simple, if regions are bounded by sharp edges and small blends; problems arise when smoothly connected regions need to be separated. In this paper, a modified self-organizing feature map neural network (SOFM) is used to solve segmentation problem. Eight dimensional feature vectors (3-dimensional coordinates, 3-dimensional normal vectors, Gaussian curvature and mean curvature) are taken as input for SOFM. The weighted Euclidean distance measure is used to improve segmentation result. The method not only can deal with regions bounded by sharp edges, but also is very efficient to separating smoothly connected regions. The segmentation method using SOFM is robust to noise, and it operates directly on the point cloud. An examples is given to show the effect of SOFM algorithm.