Harmony Search Algorithm Supervised by Kattan Ali (6 results)
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Taschenbuch. Condition: Neu. The Harmony Search Algorithm for Supervised Training of Neural Network | Design & Implementation | Ali Kattan | Taschenbuch | 260 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139472550 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt,…info[at]bod[dot]de | Anbieter: preigu.
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Paperback. Condition: Brand New. 260 pages. 8.66x5.91x0.59 inches. In Stock.
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Language: English
Published by LAP LAMBERT Academic Publishing Apr 2019, 2019
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Within the field of Artificial Intelligence, there are basically two paradigms for the supervised training of Feed-forward Artificial Neural Network (FFANN): the trajectory-driven paradigm, such as Backpropagation, and the evolution…ary Stochastic Global Optimization paradigm (SGO), such as Genetic Algorithm. One of the relatively young SGO methods is the Harmony Search (HS) algorithm, which draws its inspiration not from biological or physical processes but from the improvisation process of Jazz musicians. HS was reported to be competitive alternative to other SGO methods. It has been used successfully in many applications mostly in engineering and industry. In this work the HS algorithm is adapted for the supervised training of FFANN and the performance is evaluated using different benchmarking problems. Two enhancements are introduced to achieve better convergence condition and better performance. A parallel implementation is also included along with performance analysis. 260 pp. Englisch.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kattan AliAli Kattan, a member of IEEE since 2009, is a PhD holder and an Assistant Professor of Computer Sciences. His research interests include machine learning, optimization, robotics, IoT and web p…rogramming. He is currently a s.
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Published by LAP LAMBERT Academic Publishing Apr 2019, 2019
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Within the field of Artificial Intelligence, there are basically two paradigms for the supervised training of Feed-forward Artificial Neural Network (FFANN): the trajectory-driven paradigm, such as Backpropagation, and the evolutionary…Stochastic Global Optimization paradigm (SGO), such as Genetic Algorithm. One of the relatively young SGO methods is the Harmony Search (HS) algorithm, which draws its inspiration not from biological or physical processes but from the improvisation process of Jazz musicians. HS was reported to be competitive alternative to other SGO methods. It has been used successfully in many applications mostly in engineering and industry. In this work the HS algorithm is adapted for the supervised training of FFANN and the performance is evaluated using different benchmarking problems. Two enhancements are introduced to achieve better convergence condition and better performance. A parallel implementation is also included along with performance analysis.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 260 pp. Englisch.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Within the field of Artificial Intelligence, there are basically two paradigms for the supervised training of Feed-forward Artificial Neural Network (FFANN): the trajectory-driven paradigm, such as Backpropagation, and the evolutionary S…tochastic Global Optimization paradigm (SGO), such as Genetic Algorithm. One of the relatively young SGO methods is the Harmony Search (HS) algorithm, which draws its inspiration not from biological or physical processes but from the improvisation process of Jazz musicians. HS was reported to be competitive alternative to other SGO methods. It has been used successfully in many applications mostly in engineering and industry. In this work the HS algorithm is adapted for the supervised training of FFANN and the performance is evaluated using different benchmarking problems. Two enhancements are introduced to achieve better convergence condition and better performance. A parallel implementation is also included along with performance analysis.
