Trends in Neural Computation

✍ Scribed by Kar-Ann Toh, Quoc-Long Tran, Dipti Srinivasan (auth.), Dr. Ke Chen, Dr. Lipo Wang (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2007
• Tongue: English
• Leaves: 509
• Series: Studies in Computational Intelligence 35
• Edition: 1
• Category: Library

✦ Synopsis

Nowadays neural computation has become an interdisciplinary field in its own right; researches have been conducted ranging from diverse disciplines, e.g. computational neuroscience and cognitive science, mathematics, physics, computer science, and other engineering disciplines. From different perspectives, neural computation provides an alternative methodology to understand brain functions and cognitive process and to solve challenging real-world problems effectively.

Trend in Neural Computation includes twenty chapters either contributed from leading experts or formed by extending well selected papers presented in the 2005 International Conference on Natural Computation. The edited book aims to reflect the latest progresses made in different areas of neural computation, including theoretical neural computation, biologically plausible neural modeling, computational cognitive science, artificial neural networks – architectures and learning algorithms and their applications in real-world problems. Researchers, graduate students and industrial practitioners in the broad areas of neural computation would benefit from the state-of-the-art work collected in this book.

✦ Table of Contents

Front Matter....Pages I-X
Hyperbolic Function Networks for Pattern Classification....Pages 1-33
Variable Selection for the Linear Support Vector Machine....Pages 35-59
Selecting Data for Fast Support Vector Machines Training....Pages 61-84
Universal Approach to Study Delayed Dynamical Systems....Pages 85-110
A Hippocampus-Neocortex Model for Chaotic Association....Pages 111-133
Latent Attractors: A General Paradigm for Context-Dependent Neural Computation....Pages 135-169
Learning Mechanisms in Networks of Spiking Neurons....Pages 171-197
GTSOM: Game Theoretic Self-organizing Maps....Pages 199-223
How to Generate Different Neural Networks....Pages 225-240
A Gradient-Based Forward Greedy Algorithm for Space Gaussian Process Regression....Pages 241-263
An Evolved Recurrent Neural Network and Its Application....Pages 265-283
A Min-Max Modular Network with Gaussian-Zero-Crossing Function....Pages 285-313
Combining Competitive Learning Networks of Various Representations for Sequential Data Clustering....Pages 315-336
Modular Neural Networks and Their Applications in Biometrics....Pages 337-365
Performance Analysis of Dynamic Cell Structures....Pages 367-389
Short Term Electric Load Forecasting: A Tutorial....Pages 391-418
Performance Improvement for Formation-Keeping Control Using a Neural Network HJI Approach....Pages 419-442
A Robust Blind Neural Equalizer Based on Higher-Order Cumulants....Pages 443-460
The Artificial Neural Network Applied to Servo Control System....Pages 461-481
Robot Localization Using Vision....Pages 483-512

✦ Subjects

Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)