Artificial Adaptive Systems Using Auto Contractive Maps

Theory, Applications and Extensions

By: Paolo Massimo Buscema, Giulia Massini, Marco Breda

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Hardcover | 6 March 2018

At a Glance

Hardcover
188 Pages

Dimensions(cm)
23.5 x 15.49 x 1.27

Hardcover

$169.75

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This book offers an introduction to artificial adaptive systems and a general model of the relationships between the data and the algorithms for analyzing them. It then describes artificial neural networks as a subclass of artificial adaptive system and discusses the back-propagation algorithm, where an important connection between supervised and unsupervised artificial neural networks is made. 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. A unique feature of the approach of this book is its linkage to the minimal spanning tree and maximal regular graph, which give the user a tool to understand the results of a neural network analysis. A second unique feature is the transformation methods that are used to restructure the dataset. This allows powerful data understanding, association and transformations, as demonstrated in the numerous examples. A supervised version of auto contractive maps (Auto-CM) is also introduced as an outstanding digit and defect recognition method. Finally the monograph walks the readers through theory and examples of how Auto-CM, with the addition of a few equations (the so-called Spin Net algorithm), can be used as a dynamic auto-associative memory to predict and simulate dynamic scenarios and to work as content addressable memory.

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