TY  - BOOK
AU  - Seiffertt,John
AU  - Wunsch,Donald C.
ED  - SpringerLink (Online service)
TI  - Unified Computational Intelligence for Complex Systems
T2  - Evolutionary Learning and Optimization,
SN  - 9783642031809
AV  - Q342 
U1  - 006.3 23
PY  - 2010///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Engineering
KW  - Artificial intelligence
KW  - Physics
KW  - Economics, Mathematical
KW  - Computational Intelligence
KW  - Complexity
KW  - Artificial Intelligence (incl. Robotics)
KW  - Game Theory/Mathematical Methods
KW  - Statistical Physics, Dynamical Systems and Complexity
N1  - 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
N2  - 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
UR  - http://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-03180-9
ER  -