Publicado por LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3846580805 ISBN 13: 9783846580806
Idioma: Inglés
Librería: dsmbooks, Liverpool, Reino Unido
EUR 119,18
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Añadir al carritoPaperback. Condición: Like New. Like New. book.
Publicado por ISTE Ltd and John Wiley & Sons Inc, 2021
ISBN 10: 1848219539 ISBN 13: 9781848219533
Idioma: Inglés
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Original o primera edición
EUR 197,50
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Añadir al carritoCondición: New. 2021. 1st Edition. Hardcover. . . . . .
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 194,48
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Añadir al carritoBuch. Condición: Neu. Neuware - This book is a general presentation of complex systems, examined from the point of view of management. There is no standard formula to govern such systems, nor to effectively understand and respond to them.
Publicado por ISTE Ltd and John Wiley & Sons Inc, 2020
ISBN 10: 1848219539 ISBN 13: 9781848219533
Idioma: Inglés
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
EUR 245,99
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Añadir al carritoCondición: New. 2021. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.
Publicado por LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3846580805 ISBN 13: 9783846580806
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 59,00
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - First we describe, analyze and present the theoretical derivations and the source codes for several (modified and well-known) non-linear Neural Network algorithms based on the unconstrained optimization theory and applied to supervised training networks. In addition to the indication of the relative efficiency of these algorithms in an application, we analyze their main characteristics and present the MATLAB source codes. Algorithms of this part depend on some modified variable metric updates and for the purpose of comparison, we illustrate the default values specification for each algorithm, presenting a simple non-linear test problem. Further more in this thesis we also emphasized on the conjugate gradient (CG) algorithms, which are usually used for solving nonlinear test functions and are combined with the modified back propagation (BP) algorithm yielding few new fast training multilayer Neural Network algorithms. This study deals with the determination of new search directions by exploiting the information calculated by gradient descent as well as the previous search directions.