Advances in Neural Networks - Isnn 2006: Third International Symposium on Neural Networks, Isnn 2006, Chengdu, China, May 28 - June 1, 2006, Proceedin

ISBN-13: 9783540344391 / Angielski / Miękka / 2006 / 1444 str.

J. Wang; Jun Wang

Opis

This book and its sister volumes constitute the Proceedings of the Third International Symposium on Neural Networks (ISNN 2006) held in Chengdu in southwestern China during May 28 31, 2006. After a successful ISNN 2004 in Dalian and ISNN 2005 in Chongqing, ISNN became a well-established series of conferences on neural computation in the region with growing popularity and improving quality. ISNN 2006 received 2472 submissions from authors in 43 countries and regions (mainland China, Hong Kong, Macao, Taiwan, South Korea, Japan, Singapore, Thailand, Malaysia, India, Pakistan, Iran, Qatar, Turkey, Greece, Romania, Lithuania, Slovakia, Poland, Finland, Norway, Sweden, Demark, Germany, France, Spain, Portugal, Belgium, Netherlands, UK, Ireland, Canada, USA, Mexico, Cuba, Venezuela, Brazil, Chile, Australia, New Zealand, South Africa, Nigeria, and Tunisia) across six continents (Asia, Europe, North America, South America, Africa, and Oceania). Based on rigorous reviews, 616 high-quality papers were selected for publication in the proceedings with the acceptance rate being less than 25%. The papers are organized in 27 cohesive sections covering all major topics of neural network research and development. In addition to the numerous contributed papers, ten distinguished scholars gave plenary speeches (Robert J. Marks II, Erkki Oja, Marios M. Polycarpou, Donald C. Wunsch II, Zongben Xu, and Bo Zhang) and tutorials (Walter J. Freeman, Derong Liu, Paul J. Werbos, and Jacek M. Zurada)."

Recenzje

From the reviews:

"It addresses the domain of artificial Intelligence (AI) and presents state-of-the-art research, development, and education in the field. ... a useful resource for scientists and educators interested in the field of NN systems ... . gives the reader a sense of the applications and challenges of the filed, and provides an extensive bibliography for further investigation. ... The collection offers a broad perspective on a topic that is widely discussed and underlines many of its applications. This makes it highly recommendable to the AI community." (M. Caramihai, Computing Reviews, December, 2006)

Spis treści

Neurobiological Analysis.- The Ideal Noisy Environment for Fast Neural Computation.- How Does a Neuron Perform Subtraction? – Arithmetic Rules of Synaptic Integration of Excitation and Inhibition.- Stochastic Resonance Enhancing Detectability of Weak Signal by Neuronal Networks Model for Receiver.- A Gaussian Dynamic Convolution Models of the FMRI BOLD Response.- Cooperative Motor Learning Model for Cerebellar Control of Balance and Locomotion.- A Model of Category Learning with Attention Augmented Simplistic Prototype Representation.- On the Learning Algorithms of Descriptive Models of High-Order Human Cognition.- A Neural Model on Cognitive Process.- Theoretical Analysis.- Approximation Bound of Mixture Networks in Spaces.- Integral Transform and Its Application to Neural Network Approximation.- The Essential Approximation Order for Neural Networks with Trigonometric Hidden Layer Units.- Wavelets Based Neural Network for Function Approximation.- Passivity Analysis of Dynamic Neural Networks with Different Time-Scales.- Exponential Dissipativity of Non-autonomous Neural Networks with Distributed Delays and Reaction-Diffusion Terms.- Convergence Analysis of Continuous-Time Neural Networks.- Global Convergence of Continuous-Time Recurrent Neural Networks with Delays.- Global Exponential Stability in Lagrange Sense of Continuous-Time Recurrent Neural Networks.- Global Exponential Stability of Recurrent Neural Networks with Time-Varying Delay.- New Criteria of Global Exponential Stability for a Class of Generalized Neural Networks with Time-Varying Delays.- Dynamics of General Neural Networks with Distributed Delays.- On Equilibrium and Stability of a Class of Neural Networks with Mixed Delays.- Stability Analysis of Neutral Neural Networks with Time Delay.- Global Asymptotical Stability in Neutral-Type Delayed Neural Networks with Reaction-Diffusion Terms.- Almost Sure Exponential Stability on Interval Stochastic Neural Networks with Time-Varying Delays.- Stochastic Robust Stability of Markovian Jump Nonlinear Uncertain Neural Networks with Wiener Process.- Stochastic Robust Stability Analysis for Markovian Jump Discrete-Time Delayed Neural Networks with Multiplicative Nonlinear Perturbations.- Global Robust Stability of General Recurrent Neural Networks with Time-Varying Delays.- Robust Periodicity in Recurrent Neural Network with Time Delays and Impulses.- Global Asymptotical Stability of Cohen-Grossberg Neural Networks with Time-Varying and Distributed Delays.- LMI Approach to Robust Stability Analysis of Cohen-Grossberg Neural Networks with Multiple Delays.- Existence and Global Stability Analysis of Almost Periodic Solutions for Cohen-Grossberg Neural Networks.- A New Sufficient Condition on the Complete Stability of a Class Cellular Neural Networks.- Stability Analysis of Reaction-Diffusion Recurrent Cellular Neural Networks with Variable Time Delays.- Exponential Stability of Delayed Stochastic Cellular Neural Networks.- Global Exponential Stability of Cellular Neural Networks with Time-Varying Delays and Impulses.- Global Exponential Stability of Fuzzy Cellular Neural Networks with Variable Delays.- Stability of Fuzzy Cellular Neural Networks with Impulses.- Absolute Stability of Hopfield Neural Network.- Robust Stability Analysis of Uncertain Hopfield Neural Networks with Markov Switching.- Asymptotic Stability of Second-Order Discrete-Time Hopfield Neural Networks with Variable Delays.- Convergence Analysis of Discrete Delayed Hopfield Neural Networks.- An LMI-Based Approach to the Global Stability of Bidirectional Associative Memory Neural Networks with Variable Delay.- Existence of Periodic Solution of BAM Neural Network with Delay and Impulse.- On Control of Hopf Bifurcation in BAM Neural Network with Delayed Self-feedback.- Convergence and Periodicity of Solutions for a Class of Discrete-Time Recurrent Neural Network with Two Neurons.- Existence and Global Attractability of Almost Periodic Solution for Competitive Neural Networks with Time-Varying Delays and Different Time Scales.- Global Synchronization of Impulsive Coupled Delayed Neural Networks.- Synchronization of a Class of Coupled Discrete Recurrent Neural Networks with Time Delay.- Chaos and Bifurcation in a New Class of Simple Hopfield Neural Network.- Synchronization of Chaotic System with the Perturbation Via Orthogonal Function Neural Network.- Numerical Analysis of a Chaotic Delay Recurrent Neural Network with Four Neurons.- Autapse Modulated Bursting.- Neurodynamic Optimization.- A Neural Network Model for Non-smooth Optimization over a Compact Convex Subset.- Differential Inclusions-Based Neural Networks for Nonsmooth Convex Optimization on a Closed Convex Subset.- A Recurrent Neural Network for Linear Fractional Programming with Bound Constraints.- A Delayed Lagrangian Network for Solving Quadratic Programming Problems with Equality Constraints.- Wavelet Chaotic Neural Networks and Their Application to Optimization Problems.- A New Optimization Algorithm Based on Ant Colony System with Density Control Strategy.- A New Neural Network Approach to the Traveling Salesman Problem.- Dynamical System for Computing Largest Generalized Eigenvalue.- A Concise Functional Neural Network for Computing the Extremum Eigenpairs of Real Symmetric Matrices.- Learning Algorithms.- A Novel Stochastic Learning Rule for Neural Networks.- Learning with Single Quadratic Integrate-and-Fire Neuron.- Manifold Learning of Vector Fields.- Similarity Measure for Vector Field Learning.- The Mahalanobis Distance Based Rival Penalized Competitive Learning Algorithm.- Dynamic Competitive Learning.- Hyperbolic Quotient Feature Map for Competitive Learning Neural Networks.- A Gradient Entropy Regularized Likelihood Learning Algorithm on Gaussian Mixture with Automatic Model Selection.- Self-organizing Neural Architecture for Reinforcement Learning.- On the Efficient Implementation Biologic Reinforcement Learning Using Eligibility Traces.- Combining Label Information and Neighborhood Graph for Semi-supervised Learning.- A Cerebellar Feedback Error Learning Scheme Based on Kalman Estimator for Tracing in Dynamic System.- An Optimal Iterative Learning Scheme for Dynamic Neural Network Modelling.- Delayed Learning on Internal Memory Network and Organizing Internal States.- A Novel Learning Algorithm for Feedforward Neural Networks.- On H??? Filtering in Feedforward Neural Networks Training and Pruning.- A Node Pruning Algorithm Based on Optimal Brain Surgeon for Feedforward Neural Networks.- A Fast Learning Algorithm Based on Layered Hessian Approximations and the Pseudoinverse.- A Modular Reduction Method for k-NN Algorithm with Self-recombination Learning.- Selective Neural Network Ensemble Based on Clustering.- An Individual Adaptive Gain Parameter Backpropagation Algorithm for Complex-Valued Neural Networks.- Training Cellular Neural Networks with Stable Learning Algorithm.- A New Stochastic PSO Technique for Neural Network Training.- A Multi-population Cooperative Particle Swarm Optimizer for Neural Network Training.- Training RBF Neural Network with Hybrid Particle Swarm Optimization.- Robust Learning by Self-organization of Nonlinear Lines of Attractions.- Improved Learning Algorithm Based on Generalized SOM for Dynamic Non-linear System.- Q-Learning with FCMAC in Multi-agent Cooperation.- Q Learning Based on Self-organizing Fuzzy Radial Basis Function Network.- A Fuzzy Neural Networks with Structure Learning.- Reinforcement Learning-Based Tuning Algorithm Applied to Fuzzy Identification.- A New Learning Algorithm for Function Approximation Incorporating A Priori Information into Extreme Learning Machine.- Robust Recursive Complex Extreme Learning Machine Algorithm for Finite Numerical Precision.- Evolutionary Extreme Learning Machine – Based on Particle Swarm Optimization.- A Gradient-Based ELM Algorithm in Regressing Multi-variable Functions.- A New Genetic Approach to Structure Learning of Bayesian Networks.- Model Design.- Research on Multi-Degree-of-Freedom Neurons with Weighted Graphs.- Output PDF Shaping of Singular Weights System: Monotonical Performance Design.- Stochastic Time-Varying Competitive Neural Network Systems.- Heterogeneous Centroid Neural Networks.- Building Multi-layer Small World Neural Network.- Growing Hierarchical Principal Components Analysis Self-Organizing Map.- Hybrid Neural Network Model Based on Multi-layer Perceptron and Adaptive Resonance Theory.- Evolving Neural Networks Using the Hybrid of Ant Colony Optimization and BP Algorithms.- A Genetic Algorithm with Modified Tournament Selection and Efficient Deterministic Mutation for Evolving Neural Network.- A Neural Network Structure Evolution Algorithm Based on e, m Projections and Model Selection Criterion.- A Parallel Coevolutionary Immune Neural Network and Its Application to Signal Simulation.- A Novel Elliptical Basis Function Neural Networks Optimized by Particle Swarm Optimization.- Fuzzy Neural Network Optimization by a Particle Swarm Optimization Algorithm.- Fuzzy Rule Extraction Using Robust Particle Swarm Optimization.- A New Design Methodology of Fuzzy Set-Based Polynomial Neural Networks with Symbolic Gene Type Genetic Algorithms.- Design of Fuzzy Polynomial Neural Networks with the Aid of Genetic Fuzzy Granulation and Its Application to Multi-variable Process System.- A Novel Self-Organizing Fuzzy Polynomial Neural Networks with Evolutionary FPNs: Design and Analysis.- Design of Fuzzy Neural Networks Based on Genetic Fuzzy Granulation and Regression Polynomial Fuzzy Inference.- A New Fuzzy ART Neural Network Based on Dual Competition and Resonance Technique.- Simulated Annealing Based Learning Approach for the Design of Cascade Architectures of Fuzzy Neural Networks.- A New Fuzzy Identification Method Based on Adaptive Critic Designs.- Impacts of Perturbations of Training Patterns on Two Fuzzy Associative Memories Based on T-Norms.- Alpha-Beta Associative Memories for Gray Level Patterns.- Associative Memories Based on Discrete-Time Cellular Neural Networks with One-Dimensional Space-Invariant Templates.- Autonomous and Deterministic Probabilistic Neural Network Using Global k-Means.- Selecting Variables for Neural Network Committees.- An Adaptive Network Topology for Classification.- A Quantitative Comparison of Different MLP Activation Functions in Classification.- Estimating the Number of Hidden Neurons in a Feedforward Network Using the Singular Value Decomposition.- Neuron Selection for RBF Neural Network Classifier Based on Multiple Granularities Immune Network.- Hierarchical Radial Basis Function Neural Networks for Classification Problems.- Biased Wavelet Neural Network and Its Application to Streamflow Forecast.- A Goal Programming Based Approach for Hidden Targets in Layer-by-Layer Algorithm of Multilayer Perceptron Classifiers.- SLIT: Designing Complexity Penalty for Classification and Regression Trees Using the SRM Principle.- Flexible Neural Tree for Pattern Recognition.- A Novel Model of Artificial Immune Network and Simulations on Its Dynamics.- Kernel Methods.- A Kernel Optimization Method Based on the Localized Kernel Fisher Criterion.- Genetic Granular Kernel Methods for Cyclooxygenase-2 Inhibitor Activity Comparison.- Support Vector Machines with Beta-Mixing Input Sequences.- Least Squares Support Vector Machine on Gaussian Wavelet Kernel Function Set.- A Smoothing Multiple Support Vector Machine Model.- Fuzzy Support Vector Machines Based on Spherical Regions.- Building Support Vector Machine Alternative Using Algorithms of Computational Geometry.- Cooperative Clustering for Training SVMs.- SVMV – A Novel Algorithm for the Visualization of SVM Classification Results.- Support Vector Machines Ensemble Based on Fuzzy Integral for Classification.- An Adaptive Support Vector Machine Learning Algorithm for Large Classification Problem.- SVDD-Based Method for Fast Training of Multi-class Support Vector Classifier.- Binary Tree Support Vector Machine Based on Kernel Fisher Discriminant for Multi-classification.- A Fast and Sparse Implementation of Multiclass Kernel Perceptron Algorithm.- Mutual Conversion of Regression and Classification Based on Least Squares Support Vector Machines.- Sparse Least Squares Support Vector Machine for Function Estimation.- A Multiresolution Wavelet Kernel for Support Vector Regression.- Multi-scale Support Vector Machine for Regression Estimation.- Gradient Based Fuzzy C-Means Algorithm with a Mercer Kernel.- An Efficient Similarity-Based Validity Index for Kernel Clustering Algorithm.- Fuzzy Support Vector Clustering.- An SVM Classification Algorithm with Error Correction Ability Applied to Face Recognition.- A Boosting SVM Chain Learning for Visual Information Retrieval.- Nonlinear Estimation of Hyperspectral Mixture Pixel Proportion Based on Kernel Orthogonal Subspace Projection.- A New Proximal Support Vector Machine for Semi-supervised Classification.- Sparse Gaussian Processes Using Backward Elimination.- Comparative Study of Extreme Learning Machine and Support Vector Machine.- ICA and BSS.- Multi-level Independent Component Analysis.- An ICA Learning Algorithm Utilizing Geodesic Approach.- An Extended Online Fast-ICA Algorithm.- Gradient Algorithm for Nonnegative Independent Component Analysis.- Unified Parametric and Non-parametric ICA Algorithm for Arbitrary Sources.- A Novel Kurtosis-Dependent Parameterized Independent Component Analysis Algorithm.- Local Stability Analysis of Maximum Nongaussianity Estimation in Independent Component Analysis.- Convergence Analysis of a Discrete-Time Single-Unit Gradient ICA Algorithm.- An Novel Algorithm for Blind Source Separation with Unknown Sources Number.- Blind Source Separation Based on Generalized Variance.- Blind Source Separation with Pattern Expression NMF.- Nonlinear Blind Source Separation Using Hybrid Neural Networks.- Identification of Mixing Matrix in Blind Source Separation.- Identification of Independent Components Based on Borel Measure for Under-Determined Mixtures.- Estimation of Delays and Attenuations for Underdetermined BSS in Frequency Domain.- Application of Blind Source Separation to Five-Element Cross Array Passive Location.- Convolutive Blind Separation of Non-white Broadband Signals Based on a Double-Iteration Method.- Multichannel Blind Deconvolution Using a Novel Filter Decomposition Method.- Two-Stage Blind Deconvolution for V-BLAST OFDM System.- Data Preprocessing.- A Comparative Study on Selection of Cluster Number and Local Subspace Dimension in the Mixture PCA Models.- Adaptive Support Vector Clustering for Multi-relational Data Mining.- Robust Data Clustering in Mercer Kernel-Induced Feature Space.- Pseudo-density Estimation for Clustering with Gaussian Processes.- Clustering Analysis of Competitive Learning Network for Molecular Data.- Self-Organizing Map Clustering Analysis for Molecular Data.- A Conscientious Rival Penalized Competitive Learning Text Clustering Algorithm.- Self-Organizing-Map-Based Metamodeling for Massive Text Data Exploration.- Ensemble Learning for Keyphrases Extraction from Scientific Document.- Grid-Based Fuzzy Support Vector Data Description.- Development of the Hopfield Neural Scheme for Data Association in Multi-target Tracking.- Determine Discounting Coefficient in Data Fusion Based on Fuzzy ART Neural Network.- Scientific Data Lossless Compression Using Fast Neural Network.- HyperSurface Classifiers Ensemble for High Dimensional Data Sets.- Designing a Decompositional Rule Extraction Algorithm for Neural Networks.- Estimating Fractal Intrinsic Dimension from the Neighborhood.- Dimensionality Reduction for Evolving RBF Networks with Particle Swarms.- Improved Locally Linear Embedding Through New Distance Computing.- An Incremental Linear Discriminant Analysis Using Fixed Point Method.- A Prewhitening RLS Projection Alternated Subspace Tracking (PAST) Algorithm.- Classification with the Hybrid of Manifold Learning and Gabor Wavelet.- A Novel Input Stochastic Sensitivity Definition of Radial Basis Function Neural Networks and Its Application to Feature Selection.- Using Ensemble Feature Selection Approach in Selecting Subset with Relevant Features.- A New Method for Feature Selection.- Improved Feature Selection Algorithm Based on SVM and Correlation.- Feature Selection in Text Classification Via SVM and LSI.- Parsimonious Feature Extraction Based on Genetic Algorithms and Support Vector Machines.- Feature Extraction for Time Series Classification Using Discriminating Wavelet Coefficients.- Feature Extraction of Underground Nuclear Explosions Based on NMF and KNMF.- Hidden Markov Model Networks for Multiaspect Discriminative Features Extraction from Radar Targets.- Application of Self-organizing Feature Neural Network for Target Feature Extraction.- Divergence-Based Supervised Information Feature Compression Algorithm.