Artificial Adaptive Systems Using Auto Contractive Maps: Theory, Applications and Extensions

Buscema, Paolo Massimo; Massini, Giulia; Breda, Marco; Lodwick, Weldon A.; Newman, Francis; Asadi-Zeydabadi, Masoud

ISBN 10: 303009135X / ISBN 13: 9783030091354

Published by Springer, 2018

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Bibliographic Details

• Title: Artificial Adaptive Systems Using Auto Contractive Maps: Theory, Applications and Extensions
• Publisher: Springer
• Publication Date: 2018
• Language: English
• ISBN 10: 303009135X
• ISBN 13: 9783030091354
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Describes a newer approach to artificial adaptive systems, the auto contractive map

Offers a comprehensive guide on to the  use  auto contractive map and  its supervised version to extract extensive information from data

Describes how to couple auto contractive maps and graph theoretic methods to organize and understand data in a powerful new way

Includes numerous examples on real and fictitious data

 

From the Back Cover

This book offers an introduction to artificial adaptive systems and a general model of the relationships between the data and algorithms used to analyze them. It subsequently describes artificial neural networks as a subclass of artificial adaptive systems, and reports on the backpropagation algorithm, while also identifying an important connection between supervised and unsupervised artificial neural networks. 

The book’s primary focus is on the auto contractive map, an unsupervised artificial neural network employing a fixed point method versus traditional energy minimization. This is a powerful tool for understanding, associating and transforming data, as demonstrated in the numerous examples presented here. A supervised version of the auto contracting map is also introduced as an outstanding method for recognizing digits and defects. In closing, the book walks the readers through the theory and examples of how the auto contracting map can be used in conjunction with another artificial neural network, the “spin-net,” as a dynamic form of auto-associative memory.