Advances in Neural Networks - ISNN 2004 (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
1044
Utgivningsdatum
2004-08-01
Upplaga
2004 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Yin, Fuliang (ed.), Wang, Jun (ed.), Guo, Chengan (ed.)
Illustratör/Fotograf
Bibliographie
Illustrationer
LXX, 1044 p.
Volymtitel
Part I
Dimensioner
234 x 156 x 54 mm
Vikt
1485 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783540228417
Advances in Neural Networks - ISNN 2004 (häftad)

Advances in Neural Networks - ISNN 2004

International Symposium on Neural Networks, Dalian, China, August 19-21, 2004, Proceedings, Part I

Häftad Engelska, 2004-08-01
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This book constitutes the proceedings of the International Symposium on Neural N- works (ISNN 2004) held in Dalian, Liaoning, China during August 19-21, 2004. ISNN 2004 received over 800 submissions from authors in ?ve continents (Asia, Europe, North America, South America, and Oceania), and 23 countries and regions (mainland China, Hong Kong, Taiwan, South Korea, Japan, Singapore, India, Iran, Israel, Turkey, H- gary, Poland, Germany, France, Belgium, Spain, UK, USA, Canada, Mexico, Venezuela, Chile, and Australia). Based on reviews, the Program Committee selected 329 hi- quality papers for presentation at ISNN 2004 and publication in the proceedings. The papers are organized into many topical sections under 11 major categories (theore- cal analysis; learning and optimization; support vector machines; blind source sepa- tion, independent component analysis, and principal component analysis; clustering and classi?cation; robotics and control; telecommunications; signal, image and time series processing; detection, diagnostics, and computer security; biomedical applications; and other applications) covering the whole spectrum of the recent neural network research and development. In addition to the numerous contributed papers, ?ve distinguished scholars were invited to give plenary speeches at ISNN 2004. ISNN 2004 was an inaugural event. It brought together a few hundred researchers, educators, scientists, and practitioners to the beautiful coastal city Dalian in northeastern China.
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Innehållsförteckning

Theoretical Analysis.- Approximation Bounds by Neural Networks in L ? p [-4pt].- Geometric Interpretation of Nonlinear Approximation Capability for Feedforward Neural Networks.- Mutual Information and Topology 1: Asymmetric Neural Network.- Mutual Information and Topology 2: Symmetric Network.- Simplified PCNN and Its Periodic Solutions.- On Robust Periodicity of Delayed Dynamical Systems with Time-Varying Parameters.- Delay-Dependent Criteria for Global Stability of Delayed Neural Network System.- Stability Analysis of Uncertain Neural Networks with Delay.- A New Method for Robust Stability Analysis of a Class of Recurrent Neural Networks with Time Delays.- Criteria for Stability in Neural Network Models with Iterative Maps.- Exponential Stability Analysis for Neural Network with Parameter Fluctuations.- Local Stability and Bifurcation in a Model of Delayed Neural Network.- On the Asymptotic Stability of Non-autonomous Delayed Neural Networks.- Global Exponential Stability of Cohen-Grossberg Neural Networks with Multiple Time-Varying Delays.- Stability of Stochastic Cohen-Grossberg Neural Networks.- A Novel Approach to Exponential Stability Analysis of Cohen-Grossberg Neural Networks.- Analysis for Global Robust Stability of Cohen-Grossberg Neural Networks with Multiple Delays.- On Robust Stability of BAM Neural Networks with Constant Delays.- Absolutely Exponential Stability of BAM Neural Networks with Distributed Delays.- Stability Analysis of Discrete-Time Cellular Neural Networks.- Novel Exponential Stability Criteria for Fuzzy Cellular Neural Networks with Time-Varying Delay.- Stability of Discrete Hopfield Networks with Delay in Serial Mode.- Further Results for an Estimation of Upperbound of Delays for Delayed Neural Networks.- Synchronization in Two Uncoupled Chaotic Neurons.- Robust Synchronization of Coupled Delayed Recurrent Neural Networks.- Learning and Optimization.- Self-Optimizing Neural Networks.- Genetically Optimized Self-Organizing Neural Networks Based on PNs and FPNs.- A New Approach to Self-Organizing Hybrid Fuzzy Polynomial Neural Networks: Synthesis of Computational Intelligence Technologies.- On Soft Learning Vector Quantization Based on Reformulation.- A New Approach to Self-Organizing Polynomial Neural Networks by Means of Genetic Algorithms.- Fuzzy-Kernel Learning Vector Quantization.- Genetic Generation of High-Degree-of-Freedom Feed-Forward Neural Networks.- Self-Organizing Feature Map Based Data Mining.- Diffusion and Growing Self-Organizing Map: A Nitric Oxide Based Neural Model.- A New Adaptive Self-Organizing Map.- Evolving Flexible Neural Networks Using Ant Programming and PSO Algorithm.- Surrogating Neurons in an Associative Chaotic Neural Network.- Ensembles of RBFs Trained by Gradient Descent.- Gradient Descent Training of Radial Basis Functions.- Recent Developments on Convergence of Online Gradient Methods for Neural Network Training.- A Regularized Line Search Tunneling for Efficient Neural Network Learning.- Transductive Learning Machine Based on the Affinity-Rule for Semi-supervised Problems and Its Algorithm.- A Learning Algorithm with Gaussian Regularizer for Kernel Neuron.- An Effective Learning Algorithm of Synergetic Neural Network.- Sparse Bayesian Learning Based on an Efficient Subset Selection.- A Novel Fuzzy Neural Network with Fast Training and Accurate Generalization.- Tuning Neuro-Fuzzy Function Approximator by Tabu Search.- Finite Convergence of MRI Neural Network for Linearly Separable Training Patterns.- A Rapid Two-Step Learning Algorithm for Spline Activation Function Neural Networks with the Application on Biped Gait Recognition.- An Online Feature Learning Algorithm Using HCI-Based Reinforcement Learning.- Optimizing the Weights of Neural Networks Based on Antibody Clonal Simulated Annealing Algorithm.- Backpropagation Analysis of the Limited Precision on High-Order Function Neural Networks.- LMS Adaptive Notch Filter Design Based on Immune Algorithm.- Trainin