Unified Computational Intelligence for Complex Systems

  • John Seiffertt
  • Donald C. Wunsch

Part of the Evolutionary Learning and Optimization book series (ALO, volume 6)

Table of contents

  1. Front Matter
  2. John Seiffertt, Donald C. Wunsch
    Pages 1-17
  3. John Seiffertt, Donald C. Wunsch
    Pages 19-32
  4. John Seiffertt, Donald C. Wunsch
    Pages 33-48
  5. John Seiffertt, Donald C. Wunsch
    Pages 49-60
  6. John Seiffertt, Donald C. Wunsch
    Pages 61-76
  7. John Seiffertt, Donald C. Wunsch
    Pages 77-89
  8. John Seiffertt, Donald C. Wunsch
    Pages 91-109
  9. Back Matter

About this book

Introduction

Computational intelligence encompasses a wide variety of techniques that allow computation to learn, to adapt, and to seek. That is, they may be designed to learn information without explicit programming regarding the nature of the content to be retained, they may be imbued with the functionality to adapt to maintain their course within a complex and unpredictably changing environment, and they may help us seek out truths about our own dynamics and lives through their inclusion in complex system modeling. These capabilities place our ability to compute in a category apart from our ability to erect suspension bridges, although both are products of technological advancement and reflect an increased understanding of our world. In this book, we show how to unify aspects of learning and adaptation within the computational intelligence framework. While a number of algorithms exist that fall under the umbrella of computational intelligence, with new ones added every year, all of them focus on the capabilities of learning, adapting, and helping us seek. So, the term unified computational intelligence relates not to the individual algorithms but to the underlying goals driving them. This book focuses on the computational intelligence areas of neural networks and dynamic programming, showing how to unify aspects of these areas to create new, more powerful, computational intelligence architectures to apply to new problem domains.

Keywords

Adaptive Resonance Theory Agent-Based Computational Social Science Approximate Dynamic Programming Backpropagation Backpropogation Computational Intelligence Dynamic Equations Game Theory Neural Network Reinforcement Learning Supervised Learning Time Scales Calculus Unsupervised Learning learning modeling

Authors and affiliations

  • John Seiffertt
    • 1
  • Donald C. Wunsch
    • 1
  1. 1.Department of Electrical and Computer EngineeringMissouri University of Science and TechnologyRollaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-03180-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-03179-3
  • Online ISBN 978-3-642-03180-9
  • Series Print ISSN 1867-4534
  • Series Online ISSN 1867-4542
  • About this book