To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised...To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised structure to the supervised structure.Meanwhile,the hybrid particle swarm optimization(H-PSO)was used to optimize the connection weights,after using adaptive inheritance mode(AIM)based on the elite strategy,and adaptive detecting response mechanism(ADRM),HPSO could guide the particles adaptively jumping out of the local solution space,and ensure obtaining the global optimal solution with higher probability.So the optimized S-Kohonen network could overcome the problems of non-identifiability for recognizing the unknown samples,and the non-uniqueness for classification results existing in traditional Kohonen(T-Kohonen)network.The comparison study on the GE90 engine borescope image texture feature recognition is carried out,the research results show that:the optimized S-Kohonen network has a strong ability of practical application in the classification fault diagnosis;the classification accuracy is higher than the common neural network model.展开更多
The self-organizing Kohonen network is a fast-learning neural network used to deal with classification, clustering, interpretation and so on. On the basis of dynamics as well as kinematics of seismic reflected wave, s...The self-organizing Kohonen network is a fast-learning neural network used to deal with classification, clustering, interpretation and so on. On the basis of dynamics as well as kinematics of seismic reflected wave, small fault can be automatically recognized by using the self-organizing Kohonen artificial neural network. The experimental results indicate that this technique is feasible and has high accuracy. It is expected to become an effective method for recognizing small faults.展开更多
The key methods of detection and classification of the electroencephalogram(EEG) used in recent years are introduced . Taking EEG for example, the design plan of Kohonen neural network system based on detection and cl...The key methods of detection and classification of the electroencephalogram(EEG) used in recent years are introduced . Taking EEG for example, the design plan of Kohonen neural network system based on detection and classification of complex signals is proposed, and both the network design and signal processing are analyzed, including pre-processing of signals, extraction of signal features, classification of signal and network topology, etc.展开更多
This paper presents an unsupervised range image segmentation based on Kohonen neural network. At first, the derivative and partial derivative of each point are calculated and the normal in each points is gotten. With ...This paper presents an unsupervised range image segmentation based on Kohonen neural network. At first, the derivative and partial derivative of each point are calculated and the normal in each points is gotten. With the character vectors including normal and range value, self-organization map is introduced to cluster. The normal analysis is used to eliminate over-segmentation and the last result is gotten. This method avoid selecting original seeds and uses fewer samples, moreover computes rapidly. The experiment shows the better performance.展开更多
拒绝服务(Denial of Service,DoS)是企图使其预期用户的一台主机或其他网络资源不可用,如临时或无限期地中断或暂停连接到因特网主机的服务.为了有效地阻止DoS攻击,首先需要提高DoS攻击检测的准确性,提出一种基于改进Kohonen网络的DoS...拒绝服务(Denial of Service,DoS)是企图使其预期用户的一台主机或其他网络资源不可用,如临时或无限期地中断或暂停连接到因特网主机的服务.为了有效地阻止DoS攻击,首先需要提高DoS攻击检测的准确性,提出一种基于改进Kohonen网络的DoS攻击检测算法.该方法通过对DoS攻击原始数据的预处理,为后续数据处理的方便和保证程序运行时加快收敛奠定必要的基础,采用检测结果的正确率作为该算法的评价指标,采用SOM学习算法是把高维空间的输入数据映射到低维神经网络上,并且保持原来的拓扑次序,然后建立S-Kohonen(Supervised-Kohonen)神经网络检测模型.实验结果表明,与传统的Kohonen方法相比,S-Kohonen网络具有更好的检测性能.展开更多
基金Joint Funds of the National Natural Science Foundation of China(NSAF)(No.U1330130)General Program of Civil Aviation Flight University of China(No.J2015-39)
文摘To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised structure to the supervised structure.Meanwhile,the hybrid particle swarm optimization(H-PSO)was used to optimize the connection weights,after using adaptive inheritance mode(AIM)based on the elite strategy,and adaptive detecting response mechanism(ADRM),HPSO could guide the particles adaptively jumping out of the local solution space,and ensure obtaining the global optimal solution with higher probability.So the optimized S-Kohonen network could overcome the problems of non-identifiability for recognizing the unknown samples,and the non-uniqueness for classification results existing in traditional Kohonen(T-Kohonen)network.The comparison study on the GE90 engine borescope image texture feature recognition is carried out,the research results show that:the optimized S-Kohonen network has a strong ability of practical application in the classification fault diagnosis;the classification accuracy is higher than the common neural network model.
文摘The self-organizing Kohonen network is a fast-learning neural network used to deal with classification, clustering, interpretation and so on. On the basis of dynamics as well as kinematics of seismic reflected wave, small fault can be automatically recognized by using the self-organizing Kohonen artificial neural network. The experimental results indicate that this technique is feasible and has high accuracy. It is expected to become an effective method for recognizing small faults.
文摘The key methods of detection and classification of the electroencephalogram(EEG) used in recent years are introduced . Taking EEG for example, the design plan of Kohonen neural network system based on detection and classification of complex signals is proposed, and both the network design and signal processing are analyzed, including pre-processing of signals, extraction of signal features, classification of signal and network topology, etc.
文摘This paper presents an unsupervised range image segmentation based on Kohonen neural network. At first, the derivative and partial derivative of each point are calculated and the normal in each points is gotten. With the character vectors including normal and range value, self-organization map is introduced to cluster. The normal analysis is used to eliminate over-segmentation and the last result is gotten. This method avoid selecting original seeds and uses fewer samples, moreover computes rapidly. The experiment shows the better performance.
文摘拒绝服务(Denial of Service,DoS)是企图使其预期用户的一台主机或其他网络资源不可用,如临时或无限期地中断或暂停连接到因特网主机的服务.为了有效地阻止DoS攻击,首先需要提高DoS攻击检测的准确性,提出一种基于改进Kohonen网络的DoS攻击检测算法.该方法通过对DoS攻击原始数据的预处理,为后续数据处理的方便和保证程序运行时加快收敛奠定必要的基础,采用检测结果的正确率作为该算法的评价指标,采用SOM学习算法是把高维空间的输入数据映射到低维神经网络上,并且保持原来的拓扑次序,然后建立S-Kohonen(Supervised-Kohonen)神经网络检测模型.实验结果表明,与传统的Kohonen方法相比,S-Kohonen网络具有更好的检测性能.
