Progress in Neural Networks: Algorithms v. 7
Published on 1. August 2003
Book
Hardback
256 pages
978-1-84150-053-9 (ISBN)
Description
This long-standing series reviews current research in natural and synthetic networks, as well as reviewing state-of-the-art research in modeling, analysis, design, and development of neural networks in software and hardware areas. Contributions from leading researchers and practitioners shape academic and professional programs in this area. They serve as a platform for detailed and expanded discussion of topics of interest to the neural network and cognitive information processing communities. Topics covered in this volume include: - Networks for Regression Analysis - Faulted, and Fault Tolerant Neural Networks - Learning by Generalized Entropy - Adaptive Network Systems - Hebbian Learning - Control Theory - Implementation of a Neurocomputer - Uses of Hybrid Neural Networks and Multilayer Networks This series is directly aimed at those professionally involved in networks research,including lecturers and primary investigators in neural computing, learning, and memory.
More details
Series
Language
English
Place of publication
United Kingdom
Publishing group
Intellect
Target group
Professional and scholarly
Dimensions
Height: 230 mm
ISBN-13
978-1-84150-053-9 (9781841500539)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Content
Networks for Regression Analysis: Principles and Observations Applied to a Plasma Etch Controller - Edward A. Reitman (AT&T Bell Labs, New Jersey) and Suresh H. Patel (AT&T Micro Electronics, Orlando) - A Generating Function Method for Modeling the Dynamics of Faulted Artificial Neural Networks - Lee A. Belfore II (Dept. of Electrical and Computer Engineering, Marquette University, Wisconsin) - A Fault Tolerant Neural Network: Definition and Design - Oh-Jun Kwon and Sung-Yang Bang (Pohang University of Science and Technology, Korea) - Frequency Based Multilayer Neural Network with On-chip Learning and Enhanced Neuron Characteristics - Hroomi Hikawa (Dept of Computer Science and Intelligent Systems Oita University, Japan) - Learning by Generalized Entropy - Rytaro Kamimura and Shohachiro Nakanishi (Dept of Electrical Engineering, Tokai University, Japan) - Composite Adaptive Neural Controller for Nonlinear Dynamic Systems - Hyo-Gyu Kim and Se-Young Oh (Dept of Electrical Engineering, Pohang University of Science and Technology, Japan) - An Adaptive Algorithm for the Radial Basis Function Network - Chih-Chou Chiu (Dept of IE&M, Ming-Hsin Institute of Tech & Com, Taiwan), Deborah F.Cook (Dept of Management Science, Virginia Tech) and A.Dale Whittaker (Dept of Agricultural Engineering, Texas A&M University) - The Determination of Higher Order Nodes - Jason M. Kinser (Dept of Physics, Alabama A&M University) - A Neural Network Architecture for Partial Least-Squares Regression with Supervised Adaptive Modular Hebbian Learning - Fredric M.Ham and Ivica Kostanic (Florida Institute of Technology) - Classical Control Theory, Kolmogorov's Theorem and Automata Networks - Edward A. Reitman (AT&T Bell Labs, New Jersey) - Inner-Outer Point Evaluation in a Determnistic Generalization Domain - Pablo S. Mandolesi, Guillermo Sentoni, Osvaldo Agamennoni and Alfredo C. Desages (Dep. Ing. Electrica UNS and CONICET, Argentina) - Improving Generalization by Entropy Maximization - Ryotaro Kamimura (Information Science Laboratory, Tokai University, Japan) - Inversion of Multilayer Neural Network with Modeling Error Compensation - Jeong-Woo Lee and Jun-Ho Oh (KAIST, Teajeon, Republic of Korea) - Acousto-Optical Implementation of a General-Purpose Neurocomputer - H. John Caulfield (Alabama A&M University), Max H. Garzon, William S. Boyd and Stanley P.Franklin (Memphis State University) - Hybrid Neural Networks for Prediction of Wet-End Web Breaks in Paper Mills - Yu-To Chen, Piero P. Bonissone and Pratap S. Khedkar (IT Lab, GE Corporate Research and development)