Advances in Neural Networks - ISNN 2005神经网络进展-ISNN 2005 第一部分

出版时间：2005-8  出版社：北京燕山出版社  作者：Jun Wang 等著  页数：1041  

内容概要

The three volume set LNCS 3496/3497/3498 constitutes the refereed proceedings of the Second International Symposium on Neural Networks, ISNN 2005, held in Chongqing, China in May/June 2005.    The 483 revised papers presented were carefully reviewed and selected from 1.425 submissions. The papers are organized in topical sections on theoretical analysis, model design, learning methods, optimization methods, kernel methods, component analysis, pattern analysis, systems modeling, signal processing, image processing, financial analysis, control systems, robotic systems, telecommunication networks, incidence detection, fault diagnosis, power systems, biomedical applications, industrial applications, and other applications.

书籍目录

1  Theoretical Analysis  Population Coding, Bayesian Inference and Information Geometry  One-Bit-Matching ICA Theorem, Convex-Concave Programming, and Combinatorial Optimization  Dynamic Models for Intention (Goal-Directedness) Are Required by Truly Intelligent Robots  Differences and Commonalities Between Connectionism and Symbolicism  Pointwise Approximation for Neural Networks  On the Universal Approximation Theorem of Fuzzy Neural Networks with Random Membership Function Parameters  A Review: Relationship Between Response Properties of Visual Neurons and Advances in Nonlinear Approximation Theory  Image Representation in Visual Cortex and High Nonlinear Approximation  Generalization and Property Analysis of GENET  On Stochastic Neutral Neural Networks  Eigenanalysis of CMAC Neural Network  A New Definition of Sensitivity for RBFNN and Its Applications to Feature Reduction  Complexity of Error Hypersurfaces in Multilayer Perceptrons with General Multi-input and Multi-output Architecture  Nonlinear Dynamical Analysis on Coupled Modified Fitzhugh-Nagumo Neuron Model  Stability of Nonautonomous Recurrent Neural Networks with Time-Varying Delays  Global Exponential Stability of Non-autonomous Neural Networks with Variable Delay  A Generalized LMI-Based Approach to the Global Exponential Stability of Recurrent Neural Networks with Delay  A Further Result for Exponential Stability of Neural Networks with Time-Varying Delays  Improved Results for Exponential Stability of Neural Networks with Time-Varying Delays  Global Exponential Stability of Recurrent Neural Networks with Infinite Time-Varying Delays and Reaction-Diffusion Terms  Exponential Stability Analysis of Neural Networks with Multiple Time Delays  Exponential Stability of Cohen-Grossberg Neural Networks with Delays  Global Exponential Stability of Cohen-Grossberg Neural Networks with Time-Varying Delays and   Continuously Distributed Delays   Exponential Stability of Stochastic Cohen-Grossberg Neural Networks with Time-Varying Delays  Exponential Stability of Fuzzy Cellular Neural Networks with Unbounded Delay ……2 Model Design3 Learning Methods4 Optimization Methods5 Kernel Methods6 Component AnalysisAuthor Index