
Advances in Neural Networks - ISNN 2007
4th International Symposium on Neutral Networks, ISNN 2007 Nanjing, China, June 3-7, 2007. Proceedings, Part I
Springer (Publisher)
Published on 24. May 2007
Book
Paperback/Softback
LIV, 1359 pages
978-3-540-72382-0 (ISBN)
Description
The three volume set LNCS 4491/4492/4493 constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. The 262 revised long papers and 192 revised short papers presented were carefully reviewed and selected from a total of 1.975 submissions. The papers are organized in topical sections on neural fuzzy control, neural networks for control applications, adaptive dynamic programming and reinforcement learning, neural networks for nonlinear systems modeling, robotics, stability analysis of neural networks, learning and approximation, data mining and feature extraction, chaos and synchronization, neural fuzzy systems, training and learning algorithms for neural networks, neural network structures, neural networks for pattern recognition, SOMs, ICA/PCA, biomedical applications, feedforward neural networks, recurrent neural networks, neural networks for optimization, support vector machines, fault diagnosis/detection, communications and signal processing, image/video processing, and applications of neural networks.
More details
Series
Edition
2007 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
LIV, 1359 p. In 2 volumes, not available separately.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 76 mm
Weight
2114 gr
ISBN-13
978-3-540-72382-0 (9783540723820)
DOI
10.1007/978-3-540-72383-7
Schweitzer Classification
Content
Neural Fuzzy Control.- Neural Networks for Control Applications.- Adaptive Dynamic Programming and Reinforcement Learning.- Neural Networks for Nonlinear Systems Modeling.- Robotics.- Stability Analysis of Neural Networks.- Learning and Approximation.- Learning and Approximation.