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self organizing maps
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     Based on statistical learning theory and support vector machine, a new technique, Kernel Self organizing Maps (KSOM), is presented in this paper.
     结合统计学习理论 SL T 和支持向量机 SVM 的原理 ,在传统的采用欧氏距离作为竞争评价函数的自组织映射 SOM 基础上 ,提出了一种基于核函数的自组织映射 KSOM 方法 ,并把它应用于齿轮故障的聚类识别 .
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  相似匹配句对
     Outline for Self organizing Methodology
     自组织方法论论纲
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     Self-organizing Information of Life
     生命自组织信息
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     Self Organizing Feature Maps for Categorical Data
     面向分类数据的自组织神经网络
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     Improved Self-Organizing Feature Maps Algorithm
     一种改进的自组织特征映射算法
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     Research and Development of Self-organizing Maps Algorithm
     SOM神经网络算法的研究与进展
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  self organizing maps
Kohonen self organizing maps (SOM) analysis of these data allowed us to combine multivariate (distribution of samples on Kohonen SOMs) and univariate information (component plane representation of metabolites) in a single analysis.
      
The rainfall and discharge data available for modelling is explored using Self Organizing Maps (SOM) and the subset of data having definite relationship between the selected hydrologic variables identified.
      
The proposed method uses Self Organizing Maps (SOMs), a class of unsupervised learning neural networks, to perform direct clustering of machines into cells, without first resorting to grouping parts into families as done by previous approaches.
      
Design of cellular manufacturing systems using Latent Semantic Indexing and Self Organizing Maps
      
A Sensitivity Analysis of the Self Organizing Maps as an Adaptive One-pass Non-stationary Clustering Algorithm: the Case of Colo
      
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In this paper, the neural network processing principle and structure are given which are requisite for damage assessment and processing of fiberoptic array sensing signals in fiberoptic smart materials and structures Requiring of this application,the models of backpropagation neural network, self-organizing map neural network and its variants(such as LVQ1,LVQ2,LVQ3,LVQ4 and LVQ5 et al)are discribed in detail.At the same time,the simulation results are also given.

本文以光纤机敏材料与结构中的损伤估计为目的,根据光纤阵列传感信号处理的需要,在给出人工神经网络处理原理与结构基础上,结合应用详细地阐述了适用的反向传播神经网络(BP)模型、自组织特征映射神经网络(Kohonen)模型及其变化形式(LVQ_1,LVQ_2,LVQ_3,LVQ_4及LVQ_5等),同时给出了仿真实验的结果

A novel approach is introduced for composite damage assessment.The system consists of an embedded fiberoptic sensor array,Shape Memory Alloy(SMA)and Kohonen Self-Organizing Maps(SOM) neural network processor. The fiberoptic sensor array embedded in the com-posite structure can be used to detect the damages in the composite。The neural network is simu-lated by high speed Parallel Distributed Proeessing(PDP) which consists of TMS320C25 high speed processor and IBM PC/386 computer, deals with...

A novel approach is introduced for composite damage assessment.The system consists of an embedded fiberoptic sensor array,Shape Memory Alloy(SMA)and Kohonen Self-Organizing Maps(SOM) neural network processor. The fiberoptic sensor array embedded in the com-posite structure can be used to detect the damages in the composite。The neural network is simu-lated by high speed Parallel Distributed Proeessing(PDP) which consists of TMS320C25 high speed processor and IBM PC/386 computer, deals with the output signals of sensors on time,and controls and actuates the shape memory alloy wires to change the strain state of the compo-site,So that,the damage of composite will be delayed。

介绍了一种复合材料损伤评估的新系统。该系统由埋入光纤传感器阵列、形状记忆合金丝和K ohonen 自组织神经网络处理器组成。由埋入光纤传感器阵列实现对材料损伤的检测,神经网络由TMS320C25 高速并行处理器和IBMPC/386组成的高速并行分布处理器进行模拟,实现传感器输出信号的实时处理,并产生相应的控制信号激励形状记忆合金丝(SMA),以改变材料的应力状态,延缓材料的破坏。

Analyses the neural networks of the feature principal comonent extraction(PCE),the self organizing feature map(SOFM),the classes augment self organizing semantic map(SOSM)and improved feature fine quantization self organizing map.By means of the feature compression of vehicles and vision analysis,the result indicates that PCE and SOFM can show similarity between objects and relative structures,have function of semantic map.The SOFM of feature fine...

Analyses the neural networks of the feature principal comonent extraction(PCE),the self organizing feature map(SOFM),the classes augment self organizing semantic map(SOSM)and improved feature fine quantization self organizing map.By means of the feature compression of vehicles and vision analysis,the result indicates that PCE and SOFM can show similarity between objects and relative structures,have function of semantic map.The SOFM of feature fine quantization can achieve detail classfication as classes augment SOSM, it overcomes the drawbacks of increasing dimensions of SOSM augments input feature,unnecessary calculation and inconsistency of input feature and map result.

剖析了用神经网络实现特征主元提取(PCE)、自组织特征影射(SOFM)、类扩展自组织语义影射(SOSM)和改进的特征细化自组织影射.通过对运载工具的特征压缩,进行可视性分析,结果表明PCE和SOFM都能显示事物间的类似程度和关系结构,具有语义影射的功能.特征细化的SOFM同样能达到类扩展SOSM细化分类的功能,它克服了类扩展的SOSM增加输入特征的维数、增加不必要的计算量、输入特征与影射结果不相一致的缺点.

 
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