CLUSTERING PROPERTIES OF FUZZY KOHONEN'S SELF-ORGANIZING FEATURE MAPS

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.

The Testing Intelligence System Based on Factor Models and Self-Organizing Feature Maps

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.

Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection

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.

Feature Extraction of Kernel Regress Reconstruction for Fault Diagnosis Based on Self-organizing Manifold Learning

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.

English-Chinese Neural Machine Translation Based on Self-organizing Mapping Neural Network and Deep Feature Matching

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.

Visualization of amino acid composition differences between processed protein from different animal species by self-organizing feature maps

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.

Self-organizing feature map neural network classification of the ASTER data based on wavelet fusion

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.

Pattern recognition of messily grown nanowire morphologies applying multi-layer connected self-organized feature maps

Multi-layer connected self-organizing feature maps(SOFMs) and the associated learning procedure were proposed to achieve efficient recognition and clustering of messily grown nanowire morphologies. The network is made up by several paratactic 2-D SOFMs with inter-layer connections. By means of Monte Carlo simulations, virtual morphologies were generated to be the training samples. With the unsupervised inner-layer and inter-layer learning, the neural network can cluster different morphologies of messily grown nanowires and build connections between the morphological microstructure and geometrical features of nanowires within. Then, the as-proposed networks were applied on recognitions and quantitative estimations of the experimental morphologies. Results show that the as-trained SOFMs are able to cluster the morphologies and recognize the average length and quantity of the messily grown nanowires within. The inter-layer connections between winning neurons on each competitive layer have significant influence on the relations between the microstructure of the morphology and physical parameters of the nanowires within.

Pattern recognition of seismogenic nodes using Kohonen selforganizing map: example in west and south west of Alborz region in Iran

Pattern recognition of seismic and mor- phostructural nodes plays an important role in seismic hazard assessment. This is a known fact in seismology that tectonic nodes are prone areas to large earthquake and have this potential. They are identified by morphostructural analysis. In this study, the Alborz region has considered as studied case and locations of future events are forecast based on Kohonen Self-Organized Neural Network. It has been shown how it can predict the location of earthquake, and identifies seismogenic nodes which are prone to earthquake of M5.5＋ at the West of Alborz in Iran by using International Institute Earthquake Engineering and Seismology earthquake catalogs data. First, the main faults and tectonic lineaments have been identified based on MZ （land zoning method） method. After that, by using pattern recognition, we generalized past recorded events to future in order to show the region of probable future earthquakes. In other word, hazardous nodes have determined among all nodes by new catalog generated Self-organizing feature maps （SOFM）. Our input data are extracted from catalog, consists longitude and latitude of past event between 1980-2015 with magnitude larger or equal to 4.5. It has concluded node D1 is candidate for big earthquakes in comparison with other nodes and other nodes are in lower levels of this potential.

基于涌现自组织映射的油气层识别及分析

当样本数多、数据维数高时,利用Kohonen自组织特征映射聚类后相邻类簇间容易发生大面积重叠,导致聚类和可视化的效果降低.利用Ultsch涌现自组织特征映射神经网络对测井数据进行聚类,而后分别通过分量图、U矩阵和P矩阵在超环面上进行可视化,并对其结果进行比较分析.该模型可克服Kohonen自组织特征映射的上述缺陷,优化聚类结果.借助该模型进行测井数据的聚类分析与可视化,可为岩性识别提供参考.

基于涌现自组织映射的聚类分析与可视化处理

Kohonen自组织特征映射可实现高维模式空间到低维拓扑结构的映射,借此可进行模式聚类分析及高维数据的二维可视化。但当输入样本数目较多、复杂度较大时,采用KSOM将使相邻类簇间发生大面积重叠,降低聚类效果。本文通过利用涌现自组织特征映射神经网络对数据进行聚类分析,并通过无边界U矩阵实现可视化功能。测试结果表明,借助ESOM模型进行数据的聚类分析与可视化在诸多方面表现出优越的性能。

基于无人机序列影像的应急快速成图技术

小型无人机所拍摄的影像不经过处理或只经过简单拼接处理就可以使用,但存在着变形大、定位精度差、可用信息少等缺点,不能充分发挥无人机的作用。本文将无人机影像和地形图结合在一起,形成一种新的影像图产品——无人机应急影像图,并给出了无人机应急影像图制作的流程和关键技术的实现。

Self-organizing dual clustering considering spatial analysis and hybrid distance measures

Dual clustering performs object clustering in both spatial and non-spatial domains that cannot be dealt with well by traditional clustering methods.However,recent dual clustering research has often omitted spatial outliers,subjectively determined the weights of hybrid distance measures,and produced diverse clustering results.In this study,we first redefined the dual clustering problem and related concepts to highlight the clustering criteria.We then presented a self-organizing dual clustering algorithm (SDC) based on the self-organizing feature map and certain spatial analysis operations,including the Voronoi diagram and polygon aggregation and amalgamation.The algorithm employs a hybrid distance measure that combines geometric distance and non-spatial similarity,while the clustering spectrum analysis helps to determine the weight of non-spatial similarity in the measure.A case study was conducted on a spatial database of urban land price samples in Wuhan,China.SDC detected spatial outliers and clustered the points into spatially connective and attributively homogenous sub-groups.In particular,SDC revealed zonal areas that describe the actual distribution of land prices but were not demonstrated by other methods.SDC reduced the subjectivity in dual clustering.

Turnout fault diagnosis based on DBSCAN/PSO-SOM

In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is proposed.Firstly,the three-phase current curve of the switch machine recorded by the micro-computer monitoring system is dealt with segmentally and then the feature parameters of the three-phase current are calculated according to the action principle of the switch machine.Due to the high dimension of initial features,the DBSCAN algorithm is used to separate the sensitive features of fault diagnosis and construct the diagnostic sensitive feature set.Then,the particle swarm optimization(PSO)algorithm is used to adjust the weight of SOM network to modify the rules to avoid“dead neurons”.Finally,the PSO-SOM network fault classifier is designed to complete the classification and diagnosis of the samples to be tested.The experimental results show that this method can judge the fault mode of switch control circuit with less training samples,and the accuracy of fault diagnosis is higher than that of traditional SOM network.

Balanced multiple weighted linear discriminant analysis and its application to visual process monitoring

Visual process monitoring is important in complex chemical processes.To address the high state separation of industrial data,we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis(BMWLDA).Then,we combine BMWLDA with self-organizing map(SOM)for visual monitoring of industrial operation processes.BMWLDA can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature vectors.When the discriminative feature vectors are used as the input to SOM,the training result of SOM can differentiate industrial operation states clearly.This function improves the performance of visual monitoring.Continuous stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis,approximate pairwise accuracy criterion,max–min distance analysis,maximum margin criterion,and local Fisher discriminant analysis.In addition,the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.

CLUSTERING OF DOA DATA IN RADAR PULSE BASED ON SOFM AND CDBW

Clustering is the main method of deinterleaving of radar pulse using multi-parameter.However,the problem in clustering of radar pulses lies in finding the right number of clusters.To solve this problem,a method is proposed based on Self-Organizing Feature Maps(SOFM) and Composed Density between and within clusters(CDbw).This method firstly extracts the feature of Direction Of Arrival(DOA) data by SOFM using the characteristic of DOA parameter,and then cluster of SOFM.Through computing the cluster validity index CDbw,the right number of clusters is found.The results of simulation show that the method is effective in sorting the data of DOA.

An improved de-interleaving algorithm of radar pulses based on SOFM with self-adaptive network topology

As a core part of the electronic warfare(EW) system,de-interleaving is used to separate interleaved radar signals. As interleaved radar pulses become more complex and denser, intelligent classification of radar signals has become very important. The self-organizing feature map(SOFM) is an excellent artificial neural network, which has huge advantages in intelligent classification of complex data. However, the de-interleaving process based on SOFM is faced with the problems that the initialization of the map size relies on prior information and the network topology cannot be adaptively adjusted. In this paper, an SOFM with self-adaptive network topology(SANT-SOFM) algorithm is proposed to solve the above problems. The SANT-SOFM algorithm first proposes an adaptive proliferation algorithm to adjust the map size, so that the initialization of the map size is no longer dependent on prior information but is gradually adjusted with the input data. Then,structural optimization algorithms are proposed to gradually optimize the topology of the SOFM network in the iterative process,constructing an optimal SANT. Finally, the optimized SOFM network is used for de-interleaving radar signals. Simulation results show that SANT-SOFM could get excellent performance in complex EW environments and the probability of getting the optimal map size is over 95% in the absence of priori information.

A Modified SOFM Method for Point Cloud Segmentation in Reverse Engineering

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.

Structural choice based on knowledge discovery system

Structural choice is a significant decision having an important influence on structural function, social economics, structural reliability and construction cost. A Case Based Reasoning system with its retrieval part constructed with a KDD subsystem, is put forward to make a decision for a large scale engineering project. A typical CBR system consists of four parts: case representation, case retriever, evaluation, and adaptation. A case library is a set of parameterized excellent and successful structures. For a structural choice, the key point is that the system must be able to detect the pattern classes hidden in the case library and classify the input parameters into classes properly. That is done by using the KDD Data Mining algorithm based on Self Organizing Feature Maps (SOFM), which makes the whole system more adaptive, self organizing, self learning and open.

Application of self-organizing neural networks to classification of plant communities in Pangquangou Nature Reserve, North China

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.