About the book on Artificial Neural Networks book :

What are artificial neural networks?  What are their components and associated learning algorithms?  What can they be used for? What are their properties and limitations?  Elements of Artificial Neural Networks book addresses these questions, providing a clear introduction to neural networks to newcomers to the field who want to use them as well as understand the underlying principles and algorithms.

The authors of this book on artificial neural networks, who have team-taught the material in a one-semester course for more than 10 years, describe most of the basic neural network models (with several detailed solved examples) and discuss their rationale and relative advantages.  Their approach requires little mathematical or technical background.  Written from an algorithmic perspective, this text on artificial neural networks stresses links to contiguous fields and can serve as a first course for students in computer science as well as disciplines such as engineering, medicine, economics and management, where the goal is to learn how to develop practical applications using neural network tools.

The first chapter of Introduction presents the basic concepts and tackles important- yet rarely addressed - questions related to the use of neural networks in practical situations.  The material is structured around classes of problems to which networks can be applied.  Topics include supervised learning (single layer and multilayer networks), unsupervised learning, associative models, and optimization methods.

The most frequently used algorithms are introduced early on, right after perceptrons, so that these can form the basis for initiating course projects.  In this updated second edition, algorithms developed in the late 1990s are also included. Algorithms are presented using block-structured pseudo-code, and exercises are provided throughout.

"Elements of Artificial Neural Networks Book is appropriate as a text for a senior level class for engineering and/or computer science students.  It is also likely to be used by students in economics and management.  The authors have done a very good job in describing many of the popular network structures, with several detailed solved examples.  The lucid writing style makes the book accessible to a wide range of students and fills the need for a sound engineering oriented senior-level text in this exciting area."-Joydeep Ghosh, Professor and Endowed Engineering Foundation Fellow,

About the authors of Elements of Artificial Neural Networks book :
Kishan Mehrotra and Chilukuri K. Mohan are Professors in the Department of Electrical Engineering and Computer and Information Science, Syracuse University.  Sanjay Ranka is Professor at the University of Florida, Gainesville.  They bring together perspectives and research backgrounds from multiple research areas crucial to the field of neural networks, viz., Artificial Intelligence, Statistics, Parallel Algorithms, and Data mining.

Table of Contents of Elements of Artificial Neural Networks Book:

• INTRODUCTION
   Exercises
• SUPERVISED LEARNING: SINGLE LAYER NETWORKS
  Exercises
• SUPERVISED LEARNING: MULTILAYER NETWORKS I
  Exercises
• SUPERVISED LEARNING: MULTILAYER NETWORKS II
   Exercises
• UNSUPERVISED LEARNING
  Exercises
• ASSOCIATIVE LEARNING
Exercises
• EVOLUTIONARY OPTIMIZATION
  Exercises