Advances in Neural Networks - ISNN 2004 (häftad)
Häftad (Paperback / softback)
Antal sidor
2004 ed.
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Yin, Fuliang (ed.), Wang, Jun (ed.), Guo, Chengan (ed.)
LXX, 1024 p.
Pt. II
234 x 156 x 53 mm
1457 g
Antal komponenter
1 Paperback / softback
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 II

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 duringAugust 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, Hungary, Poland, Germany, France, Belgium, Spain, UK, USA, Canada, Mexico, - nezuela, Chile, andAustralia). Based on reviews, the Program Committee selected 329 high-quality papers for presentation at ISNN 2004 and publication in the proceedings. The papers are organized into many topical sections under 11 major categories (theo- tical analysis; learning and optimization; support vector machines; blind source sepa- tion,independentcomponentanalysis,andprincipalcomponentanalysis;clusteringand 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,andpractitionerstothebeautifulcoastalcityDalianinnortheastern China. It provided an international forum for the participants to present new results, to discuss the state of the art, and to exchange information on emerging areas and future trends of neural network research. It also created a nice opportunity for the participants to meet colleagues and make friends who share similar research interests.
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VI Robotics and Control.- Application of RBFNN for Humanoid Robot Real Time Optimal Trajectory Generation in Running.- Full-DOF Calibration-Free Robotic Hand-Eye Coordination Based on Fuzzy Neural Network.- Neuro-Fuzzy Hybrid Position/Force Control for a Space Robot with Flexible Dual-Arms.- Fuzzy Neural Networks Observer for Robotic Manipulators Based on H ??? Approach.- Mobile Robot Path-Tracking Using an Adaptive Critic Learning PD Controller.- Reinforcement Learning and ART2 Neural Network Based Collision Avoidance System of Mobile Robot.- FEL-Based Adaptive Dynamic Inverse Control for Flexible Spacecraft Attitude Maneuver.- Multivariable Generalized Minimum Variance Control Based on Artificial Neural Networks and Gaussian Process Models.- A Neural Network Based Method for Solving Discrete-Time Nonlinear Output Regulation Problem in Sampled-Data Systems.- The Design of Fuzzy Controller by Means of CI Technologies-Based Estimation Technique.- A Neural Network Adaptive Controller for Explicit Congestion Control with Time Delay.- Robust Adaptive Control Using Neural Networks and Projection.- Design of PID Controllers Using Genetic Algorithms Approach for Low Damping, Slow Response Plants.- Neural Network Based Fault Tolerant Control of a Class of Nonlinear Systems with Input Time Delay.- Run-to-Run Iterative Optimization Control of Batch Processes Based on Recurrent Neural Networks.- Time-Delay Recurrent Neural Networks for Dynamic Systems Control.- Feedforward-Feedback Combined Control System Based on Neural Network.- Online Learning CMAC Neural Network Control Scheme for Nonlinear Systems.- Pole Placement Control for Nonlinear Systems via Neural Networks.- RBF NN-Based Backstepping Control for Strict Feedback Block Nonlinear System and Its Application.- Model Reference Control Based on SVM.- PID Controller Based on the Artificial Neural Network.- Fuzzy Predictive Control Based on PEMFC Stack.- Adaptive Control for Induction Servo Motor Based on Wavelet Neural Networks.- The Application of Single Neuron Adaptive PID Controller in Control System of Triaxial and Torsional Shear Apparatus.- Ram Velocity Control in Plastic Injection Molding Machines with Neural Network Learning Control.- Multiple Models Neural Network Decoupling Controller for a Nonlinear System.- Feedback-Assisted Iterative Learning Control for Batch Polymerization Reactor.- Recent Developments on Applications of Neural Networks to Power Systems Operation and Control: An Overview.- A Novel Fermentation Control Method Based on Neural Networks.- Modeling Dynamic System by Recurrent Neural Network with State Variables.- Robust Friction Compensation for Servo System Based on LuGre Model with Uncertain Static Parameters.- System Identification Using Adjustable RBF Neural Network with Stable Learning Algorithms.- A System Identification Method Based on Multi-layer Perception and Model Extraction.- Complex Model Identification Based on RBF Neural Network.- VII Telecommunications.- A Noisy Chaotic Neural Network Approach to Topological Optimization of a Communication Network with Reliability Constraints.- Space-Time Multiuser Detection Combined with Adaptive Wavelet Networks over Multipath Channels.- Optimizing Sensor Node Distribution with Genetic Algorithm in Wireless Sensor Network.- Fast De-hopping and Frequency Hopping Pattern (FHP) Estimation for DS/FHSS Using Neural Networks.- Autoregressive and Neural Network Model Based Predictions for Downlink Beamforming.- Forecast and Control of Anode Shape in Electrochemical Machining Using Neural Network.- A Hybrid Neural Network and Genetic Algorithm Approach for Multicast QoS Routing.- Performance Analysis of Recurrent Neural Networks Based Blind Adaptive Multiuser Detection in Asynchronous DS-CDMA Systems.- Neural Direct Sequence Spread Spectrum Acquisition.- Multi-stage Neural Networks for Channel Assignment in Cellular Radio Networks.- Experimental Spread Spectrum Communication System Based on CNN.- Neural Congestion