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Añadir al carritoCondición: New. pp. 53 Index.
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Publicado por Nova Science Publishers, Incorporated, 2011
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Añadir al carritoCondición: New. Num Pages: 58 pages, b/w illus. BIC Classification: UT. Category: (G) General (US: Trade). Dimension: 205 x 127 x 5. Weight in Grams: 100. . 2011. Paperback. . . . .
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Añadir al carritoCondición: New. Num Pages: 58 pages, b/w illus. BIC Classification: UT. Category: (G) General (US: Trade). Dimension: 205 x 127 x 5. Weight in Grams: 100. . 2011. Paperback. . . . . Books ship from the US and Ireland.
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Añadir al carritoKartoniert / Broschiert. Condición: New. Artificial Neural Network Training & Software Implementation TechniquesInhaltsverzeichnisIntroduction Feed-Forward Neural Networks FFANN Software Simulation FFANN Training Concept Trajectory-Driven Training Paradigm Evolutionary.
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Publicado por Nova Science Publishers, Inc (US), 2011
ISBN 10: 1611229901 ISBN 13: 9781611229905
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - Artificial neural networks (ANN) are widely used in diverse fields of science and industry. Though there have been numerous techniques used for their implementations, the choice of a specific implementation is subjected to different factors including cost, accuracy, processing speed and overall performance. Featured with synaptic plasticity, the process of training is concerned with adjusting the individual weights between each of the individual ANN neurons until we can achieve close to the desired output. This book introduces the common trajectory-driven and evolutionary-based ANN training algorithms.
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ISBN 10: 6139472555 ISBN 13: 9786139472550
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Añadir al carritoTaschenbuch. Condición: 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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Añadir al carritoPaperback. Condición: Brand New. 260 pages. 8.66x5.91x0.59 inches. In Stock.
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Añadir al carritoCondición: Used: Like New. LIVRE A L?ETAT DE NEUF. EXPEDIE SOUS 3 JOURS OUVRES. NUMERO DE SUIVI COMMUNIQUE AVANT ENVOI, EMBALLAGE RENFORCE. EAN:9781611229905.
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Publicado por LAP LAMBERT Academic Publishing Apr 2019, 2019
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Añadir al carritoTaschenbuch. Condición: 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 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. 260 pp. Englisch.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139472555 ISBN 13: 9786139472550
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Añadir al carritoCondición: 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 programming. He is currently a s.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing Apr 2019, 2019
ISBN 10: 6139472555 ISBN 13: 9786139472550
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Añadir al carritoTaschenbuch. Condición: 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.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139472555 ISBN 13: 9786139472550
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: 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 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.