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

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.


1133106557
Artificial Adaptive Systems Using Auto Contractive Maps: Theory, Applications and Extensions

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.


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

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

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

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

eBook1st ed. 2018 (1st ed. 2018)

$99.00 

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Overview

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.



Product Details

ISBN-13: 9783319750491
Publisher: Springer-Verlag New York, LLC
Publication date: 02/24/2018
Series: Studies in Systems, Decision and Control , #131
Sold by: Barnes & Noble
Format: eBook
File size: 7 MB

Table of Contents

An Introduction.- Artificial Neural Networks.- Auto-Contractive Maps.- Visualization of Auto-CM Output.- Dataset Transformations and Auto-CM.- Comparison of Auto-CM to Various Other Data Understanding Approaches.
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