Neural Networks or artificial neural networks to be more precise, represent a technology that is rooted in many disciplines such as neuosciences, mathematics, statistics, physics, computer science and engineering. This book provides a comprehensive foundation of neural network recognizing the multi disciplinary nature of the subject. The material presented in this book is supported with examples computer oriented experiments and so on.
Author: Simon Haykin
Publisher: Prentice Hall
Chapters:
This book contains 15 chapters. The chapter headings are given below:
- Introduction
- Learning Processes
- Single Layer Perceptrons
- Multi Layer Perceptrons
- Radial-Basis Function Networks
- Support Vector Machines
- Committee Machines
- Principal Components Analysis
- Self-Organizing Maps
- Information-Theoretic Models
- Stochastic Machines and Their Approximates Rooted In Statistical Mechanics
- Neurodynamic Programming
- Temporal Processing Using Feedforward Networks
- Neurodynamics
- Dynamically Driven Recurrent Networks
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