Advertisement

Artificial Adaptive Systems Using Auto Contractive Maps

Theory, Applications and Extensions

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

Part of the Studies in Systems, Decision and Control book series (SSDC, volume 131)

Table of contents

  1. Front Matter
    Pages i-vii
  2. Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi
    Pages 1-9
  3. Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi
    Pages 11-35
  4. Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi
    Pages 37-60
  5. Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi
    Pages 61-76
  6. Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi
    Pages 77-104
  7. Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi
    Pages 105-119
  8. Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi
    Pages 121-146
  9. Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi
    Pages 147-179

About this book

Introduction

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.


Keywords

Associative Memory Data Driven Machine Learning Fixed Point Theory Fuzzy Data Sets Graph Theoretic Methods Deep Learning Auto Associative ANNs Adaptive Algorithms Spin Network Auto-CM Weights Matrix Dataset Transformation Hybrid Artificial Neural Networks Auto-CM Neural Network Content Addressable Memory

Authors and affiliations

  • Paolo Massimo Buscema
    • 1
  • Giulia Massini
    • 2
  • Marco Breda
    • 3
  • Weldon A. Lodwick
    • 4
  • Francis Newman
    • 5
  • Masoud Asadi-Zeydabadi
    • 6
  1. 1.Semeion Research Center of Sciences of CommunicationRomeItaly
  2. 2.Semeion Research Center of Sciences of CommunicationRomeItaly
  3. 3.Semeion Research Center of Sciences of CommunicationRomeItaly
  4. 4.Department of Mathematical and Statistical SciencesUniversity of Colorado DenverDenverUSA
  5. 5.Department of Radiation Oncology, School of MedicineUniversity of Colorado DenverDenverUSA
  6. 6.Physics DepartmentUniversity of Colorado DenverDenverUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-75049-1
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-75048-4
  • Online ISBN 978-3-319-75049-1
  • Series Print ISSN 2198-4182
  • Series Online ISSN 2198-4190
  • Buy this book on publisher's site