Principles of Artificial Neural Networks:Basic Designs to Deep Learning

by Daniel Graupe

The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.

This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks — demonstrating how such case studies are designed, executed and how their results are obtained.

The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.

Contents: Introduction and Role of Artificial Neural Networks Fundamentals of Biological Neural Networks Basic Principles of ANNs and Their Structures The Perceptron The Madaline Back Propagation Hopfield Networks Counter Propagation Adaptive Resonance Theory The Cognitron and Neocognitron Statistical Training Recurrent (Time Cycling) Back Propagation Networks Deep Learning Neural Networks: Principles and Scope Deep Learning Convolutional Neural Networks LAMSTAR Neural Networks Performance of DLNN — Comparative Case Studies

Readership: Researchers, academics, professionals and senior undergraduate and graduate students in artificial intelligence, machine learning, neural networks and computer engineering.

440 pages, Kindle Edition

Published March 15, 2019