Language: English
Published by LAP Lambert Academic Publishing, 2018
ISBN 10: 6137340163 ISBN 13: 9786137340165
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New.
Language: English
Published by LAP Lambert Academic Publishing, 2018
ISBN 10: 6137340163 ISBN 13: 9786137340165
Seller: Mispah books, Redhill, SURRE, United Kingdom
paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Language: English
Published by LAP LAMBERT Academic Publishing Mai 2018, 2018
ISBN 10: 6137340163 ISBN 13: 9786137340165
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book proposes two novel algorithms for function classification and prediction that extends a three layered Feed Forward Neural Network (FFNN) and the standard Generalized Regression Neural Network (GRNN).In this book genetic algorithm(GA) is used inside the particle swarm optimization(PSO) algorithm to bring the worst particle in PSO search space nearer to the food. This helps in faster convergence of PSO algorithm and the possibility of the algorithm to get stuck at the local minima is eliminated.The detailed analysis of results and comparisons show that the proposed algorithms have effectively improved the performance of a neural network as a classifier and predictor and obtained the better accuracy with minimum mean square error than the non optimized neural networks. 160 pp. Englisch.
Language: English
Published by LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6137340163 ISBN 13: 9786137340165
Seller: moluna, Greven, Germany
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: R. Kshirsagar PravinDr. Pravin R. Kshirsagar, Associate Professor, ETC Department,at GHRECM, Pune (M.S),India.Dr. Sudhir G. Akojwar, Associate Professor, Head, Department of Electronics and Telecommunication Engineering, at Governmen.
Language: English
Published by LAP Lambert Academic Publishing, 2018
ISBN 10: 6137340163 ISBN 13: 9786137340165
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND.
Language: English
Published by LAP LAMBERT Academic Publishing Mai 2018, 2018
ISBN 10: 6137340163 ISBN 13: 9786137340165
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book proposes two novel algorithms for function classification and prediction that extends a three layered Feed Forward Neural Network (FFNN) and the standard Generalized Regression Neural Network (GRNN).In this book genetic algorithm(GA) is used inside the particle swarm optimization(PSO) algorithm to bring the worst particle in PSO search space nearer to the food. This helps in faster convergence of PSO algorithm and the possibility of the algorithm to get stuck at the local minima is eliminated.The detailed analysis of results and comparisons show that the proposed algorithms have effectively improved the performance of a neural network as a classifier and predictor and obtained the better accuracy with minimum mean square error than the non optimized neural networks.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 160 pp. Englisch.
Language: English
Published by LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6137340163 ISBN 13: 9786137340165
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book proposes two novel algorithms for function classification and prediction that extends a three layered Feed Forward Neural Network (FFNN) and the standard Generalized Regression Neural Network (GRNN).In this book genetic algorithm(GA) is used inside the particle swarm optimization(PSO) algorithm to bring the worst particle in PSO search space nearer to the food. This helps in faster convergence of PSO algorithm and the possibility of the algorithm to get stuck at the local minima is eliminated.The detailed analysis of results and comparisons show that the proposed algorithms have effectively improved the performance of a neural network as a classifier and predictor and obtained the better accuracy with minimum mean square error than the non optimized neural networks.