Systematic Design for Emergence in Cellular Nonlinear Networks With Applications in Natural Computing and Signal Processing /

Cellular nonlinear networks are naturally inspired computing architectures where complex dynamic behaviors may emerge as a result of the local or prescribed connectivity among simple cells. Functionally, much like in biology, each cell is defined by a few bits of information called a gene. Such syst...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Dogaru, Radu (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
Σειρά:Studies in Computational Intelligence, 95
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Dogaru, Radu.  |e author. 
245 1 0 |a Systematic Design for Emergence in Cellular Nonlinear Networks  |h [electronic resource] :  |b With Applications in Natural Computing and Signal Processing /  |c by Radu Dogaru. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg,  |c 2008. 
300 |a XII, 166 p. 80 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 95 
505 0 |a Natural Computing Paradigms and Emergent Computation -- Cellular Nonlinear Networks: State of the Art and Applications -- Cellular and Natural Computing Models and Software Simulation -- Emergence, Locating and Measuring It -- Exponents of Growth -- Sieves for Selection of Genes According to Desired Behaviors -- Predicting Emergence from Cell’s Structure -- Applications of Emergent Phenomena. 
520 |a Cellular nonlinear networks are naturally inspired computing architectures where complex dynamic behaviors may emerge as a result of the local or prescribed connectivity among simple cells. Functionally, much like in biology, each cell is defined by a few bits of information called a gene. Such systems may be used in signal processing applications (intelligent sensors) or may be used to model and understand natural systems. While many publications focus on the dynamics in cellular automata and various applications, less deal with an important problem, that of designing for emergence. Put in simple words: How to choose a cell such that a desired behavior will occur in the cellular system. This book proposes a systematic framework for measuring emergence and a systematic design method to locate computationally meaningful genes in a reasonable computing time. Programs and application examples are provided so that the reader may easily understand the new concepts and develop her own specific experiments. An accessible language recommends it to a large audience including specialists from various interdisciplinary fields who may benefit from a better understanding of emergence and its applications to their specific field. 
650 0 |a Computer science. 
650 0 |a Artificial intelligence. 
650 0 |a Applied mathematics. 
650 0 |a Engineering mathematics. 
650 1 4 |a Computer Science. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Appl.Mathematics/Computational Methods of Engineering. 
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773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783540768005 
830 0 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 95 
856 4 0 |u http://dx.doi.org/10.1007/978-3-540-76801-2  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)