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Unified Computational Intelligence for Complex Systems

  • Book
  • © 2010

Overview

Authors:
  1. John Seiffertt

    1. Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, USA

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  2. Donald C. Wunsch

    1. Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, USA

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    You can also search for this author in PubMed   Google Scholar

  • First book presenting a computational intelligence architecture capable of learning in unsupervised, supervised, or reinforcement learning modes
  • The only book covering applications of time scales mathematics to engineering applications
  • The only book which ties these learning paradigms into all three levels of intelligence and provides applications to engineering, markets, and society

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

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Table of contents (7 chapters)

  1. Front Matter

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  2. Introduction

    • John Seiffertt, Donald C. Wunsch
    Pages 1-17

  3. The Unified Art Architecture

    • John Seiffertt, Donald C. Wunsch
    Pages 19-32

  4. An Application of Unified Computational Intelligence

    • John Seiffertt, Donald C. Wunsch
    Pages 33-48

  5. The Time Scales Calculus

    • John Seiffertt, Donald C. Wunsch
    Pages 49-60

  6. Approximate Dynamic Programming on Time Scales

    • John Seiffertt, Donald C. Wunsch
    Pages 61-76

  7. Backpropagation on Time Scales

    • John Seiffertt, Donald C. Wunsch
    Pages 77-89

  8. Unified Computational Intelligence in Social Science

    • John Seiffertt, Donald C. Wunsch
    Pages 91-109

  9. Back Matter

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Keywords

About this book

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.

Authors and Affiliations

  • Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, USA

    John Seiffertt, Donald C. Wunsch

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