Unified Computational Intelligence for Complex Systems

1. Front Matter

   PDF

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

   PDF

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.

• 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
• complexity

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

  John Seiffertt, Donald C. Wunsch

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