Advances in Neural Networks- ISNN 2013: 10th International Symposium on Neural Networks, ISNN 2013, Dalian, China, July 4-6, 2013, Proceedings, Part I [Paperback]

The two-volume set LNCS 7951 and 7952 constitutes the refereed proceedings of the 10th International Symposium on Neural Networks, ISNN 2013, held in Dalian, China, in July 2013.The 157 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in following topics: computational neuroscience, cognitive science, neural network models, learning algorithms, stability and convergence analysis, kernel methods, large margin methods and SVM, optimization algorithms, varational methods, control, robotics, bioinformatics and biomedical engineering, brain-like systems and brain-computer interfaces, data mining and knowledge discovery and other applications of neural networks.Control, Robotics and Hardware.- Optimal Tracking Control Scheme for Discrete-Time Nonlinear Systems with Approximation Errors.- Distributed Output Feedback Tracking Control of Uncertain Nonlinear Multi-Agent Systems with Unknown Input of Leader.- Quadrotor Flight Control Parameters Optimization Based on Chaotic Estimation of Distribution Algorithm.- Stability Analysis on Pattern-Based NN Control Systems.- Observer-Based H Fuzzy Control for T-S Fuzzy Neural Networks with Random Data Losses.- Robust Adaptive Neural Network Control for Wheeled Inverted Pendulum with Input Saturation.- An Improved Learning Scheme for Extracting T-S Fuzzy Rules from Data Samples.- On the Equivalence between Generalized Ellipsoidal Basis Function Neural Networks and T-S Fuzzy Systems.- Adaptive NN Control for a Class of Strict-Feedback Discrete-Time Nonlinear Systems with Input Saturation.- NN Based Adaptive Dynamic Surface Control for Fully Actuated AUV.- Bifurcation Control of a Fractional Order Hindmarsh-Rose Neuronal Model.- Adaptive Neural Control for a Class of Large-Scale Pure-Feedback Nonlinear Systems.- Adaptive Synchronization of Uncertain Chaotic Systems via Neural Network-Based Dynamic Surface Control Design.- Adaptive NN Dynamic Surface Control for Stochl#+