Unified Computational Intelligence for Complex Systems [electronic resource] / by John Seiffertt, Donald C. Wunsch.

By: Seiffertt, John [author.]Contributor(s): Wunsch, Donald C [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Evolutionary Learning and Optimization: 6Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Description: 150p. 9 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642031809Subject(s): Engineering | Artificial intelligence | Physics | Economics, Mathematical | Engineering | Computational Intelligence | Complexity | Artificial Intelligence (incl. Robotics) | Game Theory/Mathematical Methods | Statistical Physics, Dynamical Systems and ComplexityAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access online
Contents:
The Unified Art Architecture -- An Application of Unified Computational Intelligence -- The Time Scales Calculus -- Approximate Dynamic Programming on Time Scales -- Backpropagation on Time Scales -- Unified Computational Intelligence in Social Science.
In: Springer eBooksSummary: 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.
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The Unified Art Architecture -- An Application of Unified Computational Intelligence -- The Time Scales Calculus -- Approximate Dynamic Programming on Time Scales -- Backpropagation on Time Scales -- Unified Computational Intelligence in Social Science.

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.

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