Principles of Artificial Neural Networks

Basic Designs to Deep Learning

Nonfiction, Computers, Advanced Computing, Engineering, Neural Networks, Artificial Intelligence, General Computing

• Author: Daniel Graupe
• ISBN: 9789811201240
• Publisher: World Scientific Publishing Company
• Publication: March 15, 2019
• Imprint: WSPC
• Language: English

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.
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