McGraw Hill Education
|Number of Pages
This new text has been designed to present the concepts of artificial neural networks in a concise and logical manner for your computer engineering students.
Numerous exercises are presented and can be expanded into projects and thesis work.
Each new topic is preceded by a reduced complexity example of the concept.
Key concepts and related mathematical analysis are developed in easy-to-understand terms.
The distinctive coverage of genetic algorithms provides a background in genetic solutions for search problems, along with two representational techniques.
Table of contents :-
1 Overview: Artificial Neural Networks and Neural Computing
2 Mathematical Fundamentals for ANN Study
3 Elementary ANN Building Blocks
4 Single Unit Mappings and the Perception
5 Introduction to Neural Mappings and Pattern Associator Applications
6 Feedforward Networks and Training
7 Feedforward Networks
II Extensions and Advanced Topics
8 Recurrent Networks
9 Competitive and Self-Organizing Networks
10 Radial Basis Function Networks and Time Delay Neural Networks
11 Fuzzy Neural Networks, Including Fuzzy Sets and Logic and ANN Implementations
12 ANN Hardware and Implementation Concerns