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Artificial Adaptive Systems Using Auto Contractive Maps

Part of the Studies in Systems, Decision and Control Book Series (volume 131)

DOI: 10.1007/978-3-319-75049-1

ISBN: 978-3-319-75048-4

Publisher: Springer International Publishing - Springer Journals

Feb 25, 2018

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Chapter 1An Introduction

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

Pages: 1 –9

[Auto-Contractive Maps (Auto-CM) is a newer approach to artificial adaptive systems (AASs). In turn, AASs encompass the subject of artificial neural networks (ANNs). This chapter is an introduction to AASs.]
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Chapter 2Artificial Neural Networks

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

Pages: 11 –35

[Artificial Adaptive Systems include Artificial Neural Networks (ANNs or simply neural networks as they are commonly known). The philosophy of neural networks is to extract from data the underlying model that relates this data as an input/output (domain/range) pair. This is quite different from...
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Chapter 3Auto-contractive Maps

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

Pages: 37 –60

[This chapter focuses on Auto-Contractive Maps, which is a particularly useful ANN. Moreover, the relationship between Auto-Contractive Map (Auto-CM), which is the main topic of this monograph, its relationship to other ANNs and some illustrative example applications are presented.]
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Chapter 4Visualization of Auto-CM Output

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

Pages: 61 –76

[One of the most powerful aspects of our approach to neural networks is not only the development of the Auto-CM neural network but the visualization of its results. In this chapter we look at two visualization approaches—the Minimal Spanning Tree (MST) and the Maximal Regular Graph (MRG). The...
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Chapter 5Dataset Transformations and Auto-CM

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

Pages: 77 –104

[We have looked at how to visualize the relationships among the elements of a dataset in Chap. 4. This chapter is devoted to the use of Auto-CM in the transformation of datasets for the purpose of extracting further relationships among data elements. The first transformation we call the...
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Chapter 6Advances, the K-Contractive Map: A Supervised Version of Auto-CM

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

Pages: 105 –119

[This section is devoted to a more advanced type of Auto-CM that is supervised.]
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Chapter 7Comparison of Auto-CM to Various Other Data Understanding Approaches

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

Pages: 121 –146

[We compare Auto-CM with various other methods that extract patterns from data. The way that we measure the results of comparisons uses MST.]
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Chapter 8Auto-CM as a Dynamic Associative Memory

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

Pages: 147 –179

[We look at how to use Auto-CM in the context of datasets that are changing in time. We modify our approach while keeping the original philosophy of Auto-CM.]

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