Unified Computational Intelligence for Complex Systems

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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Seiffertt, John (Συγγραφέας), Wunsch, Donald C. (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010.
Σειρά:Evolutionary Learning and Optimization, 6
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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024 7 |a 10.1007/978-3-642-03180-9  |2 doi 
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100 1 |a Seiffertt, John.  |e author. 
245 1 0 |a Unified Computational Intelligence for Complex Systems  |h [electronic resource] /  |c by John Seiffertt, Donald C. Wunsch. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg,  |c 2010. 
300 |a 150 p. 9 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Evolutionary Learning and Optimization,  |x 1867-4534 ;  |v 6 
505 0 |a 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. 
520 |a 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. 
650 0 |a Computer science. 
650 0 |a Artificial intelligence. 
650 0 |a Statistical physics. 
650 0 |a Dynamical systems. 
650 0 |a Computational intelligence. 
650 0 |a Complexity, Computational. 
650 0 |a Economic theory. 
650 1 4 |a Computer Science. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Complexity. 
650 2 4 |a Economic Theory/Quantitative Economics/Mathematical Methods. 
650 2 4 |a Statistical Physics, Dynamical Systems and Complexity. 
700 1 |a Wunsch, Donald C.  |e author. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783642031793 
830 0 |a Evolutionary Learning and Optimization,  |x 1867-4534 ;  |v 6 
856 4 0 |u http://dx.doi.org/10.1007/978-3-642-03180-9  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)