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Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection 被引量:5
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作者 Ling Tan Chong Li +1 位作者 Jingming Xia Jun Cao 《Computers, Materials & Continua》 SCIE EI 2019年第7期275-288,共14页
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
关键词 K-means clustering self-organizing feature map neural network network security intrusion detection NSL-KDD data set
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CLUSTERING PROPERTIES OF FUZZY KOHONEN'S SELF-ORGANIZING FEATURE MAPS 被引量:3
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作者 彭磊 胡征 《Journal of Electronics(China)》 1995年第2期124-133,共10页
A new clustering algorithm called fuzzy self-organizing feature maps is introduced. It can process not only the exact digital inputs, but also the inexact or fuzzy non-digital inputs, such as natural language inputs. ... A new clustering algorithm called fuzzy self-organizing feature maps is introduced. It can process not only the exact digital inputs, but also the inexact or fuzzy non-digital inputs, such as natural language inputs. Simulation results show that the new algorithm is superior to original Kohonen’s algorithm in clustering performance and learning rate. 展开更多
关键词 self-organizing feature mapS FUZZY sets MEMBERSHIP measure FUZZINESS mea-sure
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The Testing Intelligence System Based on Factor Models and Self-Organizing Feature Maps
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作者 A.S. Panfilova L.S. Kuravsky 《Journal of Mathematics and System Science》 2013年第7期353-358,共6页
Presented is a new testing system based on using the factor models and self-organizing feature maps as well as the method of filtering undesirable environment influence. Testing process is described by the factor mode... Presented is a new testing system based on using the factor models and self-organizing feature maps as well as the method of filtering undesirable environment influence. Testing process is described by the factor model with simplex structure, which represents the influences of genetics and environmental factors on the observed parameters - the answers to the questions of the test subjects in one case and for the time, which is spent on responding to each test question to another. The Monte Carlo method is applied to get sufficient samples for training self-organizing feature maps, which are used to estimate model goodness-of-fit measures and, consequently, ability level. A prototype of the system is implemented using the Raven's Progressive Matrices (Advanced Progressive Matrices) - an intelligence test of abstract reasoning. Elimination of environment influence results is performed by comparing the observed and predicted answers to the test tasks using the Kalman filter, which is adapted to solve the problem. The testing procedure is optimized by reducing the number of tasks using the distribution of measures to belong to different ability levels after performing each test task provided the required level of conclusion reliability is obtained. 展开更多
关键词 self-organizing feature maps intelligence testing Kalman filter
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Feature Extraction of Kernel Regress Reconstruction for Fault Diagnosis Based on Self-organizing Manifold Learning 被引量:3
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作者 CHEN Xiaoguang LIANG Lin +1 位作者 XU Guanghua LIU Dan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2013年第5期1041-1049,共9页
The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddi... The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings,such as manifold learning.However,these methods are all based on manual intervention,which have some shortages in stability,and suppressing the disturbance noise.To extract features automatically,a manifold learning method with self-organization mapping is introduced for the first time.Under the non-uniform sample distribution reconstructed by the phase space,the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention.After that,the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional representation.Finally,the signal is reconstructed by the kernel regression.Several typical states include the Lorenz system,engine fault with piston pin defect,and bearing fault with outer-race defect are analyzed.Compared with the LTSA and continuous wavelet transform,the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified.A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed. 展开更多
关键词 feature extraction manifold learning self-organize mapping kernel regression local tangent space alignment
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Waterlogging risk assessment based on self-organizing map(SOM)artificial neural networks:a case study of an urban storm in Beijing 被引量:2
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作者 LAI Wen-li WANG Hong-rui +2 位作者 WANG Cheng ZHANG Jie ZHAO Yong 《Journal of Mountain Science》 SCIE CSCD 2017年第5期898-905,共8页
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
关键词 Waterlogging risk assessment self-organizing map(som) neural network Urban storm
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Fault diagnosis of rocket engine ground testing bed with self-organizing maps(SOMs) 被引量:1
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作者 朱宁 冯志刚 王祁 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2009年第2期204-208,共5页
To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on self-organizing map(SOM)is proposed.The SOM projects the multidimensional ground testing b... To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on self-organizing map(SOM)is proposed.The SOM projects the multidimensional ground testing bed data into a two-dimensional map.Visualization of the SOM is used to cluster the ground testing bed data.The out map of the SOM is divided to several regions.Each region is represented for one fault mode.The fault mode of testing data is determined according to the region of their labels belonged to.The method is evaluated using the testing data of a liquid-propellant rocket engine ground testing bed with sixteen fault states.The results show that it is a reliable and effective method for fault diagnosis with good visualization property. 展开更多
关键词 fault diagnosis self-organizing map (som U-matrix VISUALIZATION
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Software Reusability Classification and Predication Using Self-Organizing Map (SOM)
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作者 Amjad Hudaib Ammar Huneiti Islam Othman 《Communications and Network》 2016年第3期179-192,共14页
Due to rapid development in software industry, it was necessary to reduce time and efforts in the software development process. Software Reusability is an important measure that can be applied to improve software deve... Due to rapid development in software industry, it was necessary to reduce time and efforts in the software development process. Software Reusability is an important measure that can be applied to improve software development and software quality. Reusability reduces time, effort, errors, and hence the overall cost of the development process. Reusability prediction models are established in the early stage of the system development cycle to support an early reusability assessment. In Object-Oriented systems, Reusability of software components (classes) can be obtained by investigating its metrics values. Analyzing software metric values can help to avoid developing components from scratch. In this paper, we use Chidamber and Kemerer (CK) metrics suite in order to identify the reuse level of object-oriented classes. Self-Organizing Map (SOM) was used to cluster datasets of CK metrics values that were extracted from three different java-based systems. The goal was to find the relationship between CK metrics values and the reusability level of the class. The reusability level of the class was classified into three main categorizes (High Reusable, Medium Reusable and Low Reusable). The clustering was based on metrics threshold values that were used to achieve the experiments. The proposed methodology succeeds in classifying classes to their reusability level (High Reusable, Medium Reusable and Low Reusable). The experiments show how SOM can be applied on software CK metrics with different sizes of SOM grids to provide different levels of metrics details. The results show that Depth of Inheritance Tree (DIT) and Number of Children (NOC) metrics dominated the clustering process, so these two metrics were discarded from the experiments to achieve a successful clustering. The most efficient SOM topology [2 × 2] grid size is used to predict the reusability of classes. 展开更多
关键词 Component Based System Development (CBSD) Software Reusability Software Metrics CLASSIFICATION self-organizing map (som)
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English-Chinese Neural Machine Translation Based on Self-organizing Mapping Neural Network and Deep Feature Matching
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作者 Shu Ma 《IJLAI Transactions on Science and Engineering》 2024年第3期1-8,共8页
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
关键词 Chinese-English translation model self-organizing mapping neural network Deep feature matching Deep learning
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基于mRMR-SOM的异步电机轴承故障诊断研究
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作者 刘文 周智勇 蔡巍 《机电工程》 北大核心 2024年第1期90-98,共9页
针对异步电机轴承故障诊断问题,提出了一种融合最大相关最小冗余特征选择算法(mRMR)和自组织映射神经网络(SOM)的故障诊断方法,并将其应用于轴承故障诊断的不同阶段。首先,在实验室环境下搭建了异步电机故障诊断试验平台,在不同电机状... 针对异步电机轴承故障诊断问题,提出了一种融合最大相关最小冗余特征选择算法(mRMR)和自组织映射神经网络(SOM)的故障诊断方法,并将其应用于轴承故障诊断的不同阶段。首先,在实验室环境下搭建了异步电机故障诊断试验平台,在不同电机状态下分别采集振动、电流和电压信号,利用统计学方法获取了高维混合特征集;然后,以互信息为背景,利用mRMR根据特征与状态标签间的相关性和特征间的冗余性,筛选了具备强区分能力的特征,以避免计算冗余和后验诊断性能下降;最后,采用SOM对异步电机健康和轴承故障状态进行了分类识别,验证了SOM对异步电机轴承故障诊断的有效性,以及mRMR对故障诊断结果的影响。研究结果表明:基于mRMR-SOM的异步电机轴承故障诊断方法能够准确地区分健康和故障状态,测试集分类准确率达到89%;使用mRMR特征筛选能够将154维特征降低至17维,缩短23.5%的网络收敛时间,并将分类准确率由89%提升至98%;试验结果验证了基于mRMR-SOM的异步电机轴承故障诊断方法对于异步电机轴承故障诊断问题的有效性,且证实其具备良好的诊断效果。 展开更多
关键词 自组织映射神经网络 最大相关最小冗余特征选择算法 互信息 特征降维 特征选择 神经网络算法 U矩阵
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Intraseasonal variability of the equatorial Pacific Ocean and its relationship with ENSO based on Self-Organizing Maps analysis
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作者 FENG Junqiao WANG Fujun +1 位作者 WANG Qingye HU Dunxin 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2020年第4期1108-1122,共15页
We investigated the intraseasonal variability of equatorial Pacific subsurface temperature and its relationship with El Nino-Southern Oscillation(ENSO) using Self-Organizing Maps(SOM) analysis.Variation in intraseason... We investigated the intraseasonal variability of equatorial Pacific subsurface temperature and its relationship with El Nino-Southern Oscillation(ENSO) using Self-Organizing Maps(SOM) analysis.Variation in intraseasonal subsurface temperature is mainly found along the thermocline.The SOM patterns concentrate in basin-wide seesaw or sandwich structures along an east-west axis.Both the seesaw and sandwich SOM patterns oscillate with periods of 55 to 90 days,with the sequence of them showing features of equatorial intraseasonal Kelvin wave,and have marked interannual variations in their occurrence frequencies.Further examination shows that the interannual variability of the SOM patterns is closely related to ENSO;and maxima in composite interannual variability of the SOM patterns are located in the central Pacific during CP El Nino and in the eastern Pacific during EP El Nino.The se results imply that some of the ENSO forcing is manife sted through changes in the occurrence frequency of intraseasonal patterns,in which the change of the intraseasonal Kelvin wave plays an important role. 展开更多
关键词 intraseasonal variability equatorial Pacific El Niño-Southern Oscillation(ENSO) self-organizing maps(som)
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Coastal Water Quality Assessment by Self-Organizing Map
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作者 牛志广 张宏伟 张颖 《Transactions of Tianjin University》 EI CAS 2005年第6期446-451,共6页
A new approach to coastal water quality assessment was put forward through study on self-organizing map ( SOM ). Firstly, the water quality data of Bohai Bay from 1999 to 2002 were prepared. Then, a set of software ... A new approach to coastal water quality assessment was put forward through study on self-organizing map ( SOM ). Firstly, the water quality data of Bohai Bay from 1999 to 2002 were prepared. Then, a set of software for coastal water quality assessment was developed based on the batch version algorithm of SOM and SOM toolbox in MATLAB environment. Furthermore. the training results of SOM could be analyzed with single water quality indexes, the value of N : PC atomic ratio) and the eutrophication index E so that the data were clustered into five different pollution types using k-means clustering method. Finally, it was realized that the monitoring data serial trajectory could be tracked and the new data be classified and assessed automatically. Through application it is found that this study helps to analyze and assess the coastal water quality by several kinds of graphics, which offers an easy decision support for recognizing pollution status and taking corresponding measures. 展开更多
关键词 self-organizing map som coastal marine water quality assessment pollution types
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Visualization of Pareto Solutions by Spherical Self-Organizing Map and It’s acceleration on a GPU
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作者 Masato Yoshimi Takuya Kuhara +2 位作者 Kaname Nishimoto Mitsunori Miki Tomoyuki Hiroyasu 《Journal of Software Engineering and Applications》 2012年第3期129-137,共9页
In this study, we visualize Pareto-optimum solutions derived from multiple-objective optimization using spherical self-organizing maps (SOMs) that lay out SOM data in three dimensions. There have been a wide range of ... In this study, we visualize Pareto-optimum solutions derived from multiple-objective optimization using spherical self-organizing maps (SOMs) that lay out SOM data in three dimensions. There have been a wide range of studies involving plane SOMs where Pareto-optimal solutions are mapped to a plane. However, plane SOMs have an issue that similar data differing in a few specific variables are often placed at far ends of the map, compromising intuitiveness of the visualization. We show in this study that spherical SOMs allow us to find similarities in data otherwise undetectable with plane SOMs. We also implement and evaluate the performance using parallel sphere processing with several GPU environments. 展开更多
关键词 self-organizing map som SPHERICAL GPU PARETO-OPTIMAL Solutions GPU ACCELERATION
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Study of TSP based on self-organizing map
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作者 宋锦娟 白艳萍 胡红萍 《Journal of Measurement Science and Instrumentation》 CAS 2013年第4期353-360,共8页
Self-organizing map(SOM) proposed by Kohonen has obtained certain achievements in solving the traveling salesman problem(TSP).To improve Kohonen SOM,an effective initialization and parameter modification method is dis... Self-organizing map(SOM) proposed by Kohonen has obtained certain achievements in solving the traveling salesman problem(TSP).To improve Kohonen SOM,an effective initialization and parameter modification method is discussed to obtain a faster convergence rate and better solution.Therefore,a new improved self-organizing map(ISOM)algorithm is introduced and applied to four traveling salesman problem instances for experimental simulation,and then the result of ISOM is compared with those of four SOM algorithms:AVL,KL,KG and MSTSP.Using ISOM,the average error of four travelingsalesman problem instances is only 2.895 0%,which is greatly better than the other four algorithms:8.51%(AVL),6.147 5%(KL),6.555%(KG) and 3.420 9%(MSTSP).Finally,ISOM is applied to two practical problems:the Chinese 100 cities-TSP and102 counties-TSP in Shanxi Province,and the two optimal touring routes are provided to the tourists. 展开更多
关键词 self-organizing maps (som traveling salesman problem (TSP) neural networkDocument code:AArticle ID:1674-8042(2013)04-0353-08
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Algorithm for Solving Traveling Salesman Problem Based on Self-Organizing Mapping Network
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作者 朱江辉 叶航航 +1 位作者 姚莉秀 蔡云泽 《Journal of Shanghai Jiaotong university(Science)》 EI 2024年第3期463-470,共8页
Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from ... Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter selection.This paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic algorithms.Simulations show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm accuracy.Compared with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP. 展开更多
关键词 traveling salesman problem(TSP) self-organizing mapping(som) combinatorial optimization neu-ral network
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Vibration Feature Fusion for State Evaluation of Machinery 被引量:1
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作者 李康 林习良 +1 位作者 胡湘江 蔡自刚 《Journal of Donghua University(English Edition)》 EI CAS 2015年第2期244-247,共4页
To overcome the problem that a single feature can not reflect the state of machinery in different stages,a method of vibration feature fusion based on self-organizing map(SOM) is presented.Minimum quantization error(M... To overcome the problem that a single feature can not reflect the state of machinery in different stages,a method of vibration feature fusion based on self-organizing map(SOM) is presented.Minimum quantization error(MQE) is obtained unsupervised based on SOM network.And trend information of the MQE curve is extracted by the wavelet packet to enhance state differentiating.Experimental flat is designed for bearing accelerating fatigue.And experimental results show that the method of vibration feature fusion based on SOM can reflect the state of machinery in different stages effectively. 展开更多
关键词 wavelet overcome organizing quantization accelerating neighbor machinery behave packet restrain
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Self-organizing feature map neural network classification of the ASTER data based on wavelet fusion 被引量:7
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作者 HASI Bagan MA Jianwen LI Qiqing HAN Xiuzhen LIU Zhili 《Science China Earth Sciences》 SCIE EI CAS 2004年第7期651-658,共8页
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
关键词 classification WAVELET fusion self-organizing NEURAL network feature map (SOFM) ASTER data.
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Self-Organizing Map Based Quality Assessment for Resistance Spot Welding with Featured Electrode Displacement Signals
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作者 王双园 贡亮 刘成良 《Journal of Shanghai Jiaotong university(Science)》 EI 2012年第6期673-678,共6页
To classify the quality of the resistance spot welding process, a relationship between the welder electrode displacement curve characteristics and the weld shear force has been explored. Eleven statistical features of... To classify the quality of the resistance spot welding process, a relationship between the welder electrode displacement curve characteristics and the weld shear force has been explored. Eleven statistical features of the displacement signMs are extracted to represent the welding quality. Self-organizing map (SOM) neural networks have been employed to discover their quantitative relationship. In order to identify the influence of various displacement curve features, all of the available combinations have been used as inputs for SOM neural networks. Further we analyze the impact of each feature on the classification results, yielding the best quality-indicative combination of characteristics. There is no determinant relationship between the welding quality and the level of expulsion rate. The quality of welding is most impacted by the maximum electrode displacement, the span of welding process and the centroid of the electrode displacement curve. The experiments show that SOM is feasible to assess the welding quality and can render the visualized intuitive evaluation results. 展开更多
关键词 resistance spot welding self-organizing map (som quality assessment electrode displacement
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自组织映射神经网络(SOM)降尺度方法对江淮流域逐日降水量的模拟评估 被引量:13
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作者 周璞 江志红 《气候与环境研究》 CSCD 北大核心 2016年第5期512-524,共13页
利用1961-2002年ERA-40逐日再分析资料和江淮流域56个台站逐日观测降水量资料,引入基于自组织映射神经网络(Self-Organizing Maps,简称SOM)的统计降尺度方法,对江淮流域夏季(6-8月)逐日降水量进行统计建模与验证,以考察SOM对中国东... 利用1961-2002年ERA-40逐日再分析资料和江淮流域56个台站逐日观测降水量资料,引入基于自组织映射神经网络(Self-Organizing Maps,简称SOM)的统计降尺度方法,对江淮流域夏季(6-8月)逐日降水量进行统计建模与验证,以考察SOM对中国东部季风降水和极端降水的统计降尺度模拟能力。结果表明,SOM通过建立主要天气型与局地降水的条件转换关系,能够再现与观测一致的日降水量概率分布特征,所有台站基于概率分布函数的Brier评分(Brier Score)均近似为0,显著性评分(Significance Score)全部在0.8以上;模拟的多年平均降水日数、中雨日数、夏季总降水量、日降水强度、极端降水阈值和极端降水贡献率区域平均的偏差都低于11%;并且能够在一定程度上模拟出江淮流域夏季降水的时间变率。进一步将SOM降尺度模型应用到BCCCSM1.1(m)模式当前气候情景下,评估其对耦合模式模拟结果的改善能力。发现降尺度显著改善了模式对极端降水模拟偏弱的缺陷,对不同降水指数的模拟较BCC-CSM1.1(m)模式显著提高,降尺度后所有台站6个降水指数的相对误差百分率基本在20%以内,偏差比降尺度前减小了40%-60%;降尺度后6个降水指数气候场的空间相关系数提高到0.9,相对标准差均接近1.0,并且均方根误差在0.5以下。表明SOM降尺度方法显著提高日降水概率分布,特别是概率分布曲线尾部特征的模拟能力,极大改善了模式对极端降水场的模拟能力,为提高未来预估能力提供了基础。 展开更多
关键词 统计降尺度 som(self-organizing maps) 江淮流域 极端降水
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Visualization of amino acid composition differences between processed protein from different animal species by self-organizing feature maps
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作者 Xingfan ZHOU Zengling YANG +1 位作者 Longjian CHEN Lujia HAN 《Frontiers of Agricultural Science and Engineering》 2016年第2期171-180,共10页
Amino acids are the dominant organic components of processed animal proteins,however there has been limited investigation of differences in their composition between various protein sources.Information on these differ... Amino acids are the dominant organic components of processed animal proteins,however there has been limited investigation of differences in their composition between various protein sources.Information on these differences will not only be helpful for their further utilization but also provide fundamental information for developing species-specific identification methods.In this study,self-organizing feature maps(SOFM) were used to visualize amino acid composition of fish meal,and meat and bone meal(MBM) produced from poultry,ruminants and swine.SOFM display the similarities and differences in amino acid composition between protein sources and effectively improve data transparency.Amino acid composition was shown to be useful for distinguishing fish meal from MBM due to their large concentration differences between glycine,lysine and proline.However,the amino acid composition of the three MBMs was quite similar.The SOFM results were consistent with those obtained by analysis of variance and principal component analysis but more straightforward.SOFM was shown to have a robust sample linkage capacity and to be able to act as a powerful means to link different sample for further data mining. 展开更多
关键词 self-organizing feature maps VISUALIZATION processed animal proteins(PAPs) amino acid
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SOM神经网络和C-均值法在负荷分类中的应用 被引量:15
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作者 王文生 王进 王科文 《电力系统及其自动化学报》 CSCD 北大核心 2011年第4期36-39,共4页
负荷时变性和分散性已经成为制约负荷模型推广应用的主要因素,而负荷特性分类则是解决这个问题的有效途径。文中提出基于SOM神经网络的C-均值聚类算法的新的负荷分类方法:以负荷模型参数作为负荷动态特性分类特征向量,应用SOM神经网络... 负荷时变性和分散性已经成为制约负荷模型推广应用的主要因素,而负荷特性分类则是解决这个问题的有效途径。文中提出基于SOM神经网络的C-均值聚类算法的新的负荷分类方法:以负荷模型参数作为负荷动态特性分类特征向量,应用SOM神经网络对初始训练样本进行分类,将获得的聚类数目和各类中心点作为C-均值算法的初始输入进一步聚类。最后通过动模实验的分类结果表明该方法可自动获取分类数,应用于负荷特性分类研究中具有较强的实用性和有效性。 展开更多
关键词 电力系统 负荷建模 负荷特性分类 自组织特征映射 som神经网络 C-均值法
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