Weather plays a significant role in terms of life, property, agriculture and industry. Neural networks are capable of predicting the non-linear behavior of weather without the physics being explicitly explored. The most common method to train neural networks is through gradient decent based back propagation algorithm. But back propagation algorithm suffers from several disadvantages like local minima problem, slow training, and scaling problem. So the ways to solve these problems by hybridizing it with genetic algorithms. The hybrid technique can learn efficiently by combining the strengths of genetic algorithm with back propagation algorithm . The hybrid neural networks are more qualified if only the requirement of a global searching is considered. It is good at global search i.e. not in one direction and it works with a population of points instead of a single point. Also it blends the merits of both deterministic algorithm BP and stochastic optimizing algorithm GA.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Weather plays a significant role in terms of life, property, agriculture and industry. Neural networks are capable of predicting the non-linear behavior of weather without the physics being explicitly explored. The most common method to train neural networks is through gradient decent based back propagation algorithm. But back propagation algorithm suffers from several disadvantages like local minima problem, slow training, and scaling problem. So the ways to solve these problems by hybridizing it with genetic algorithms. The hybrid technique can learn efficiently by combining the strengths of genetic algorithm with back propagation algorithm . The hybrid neural networks are more qualified if only the requirement of a global searching is considered. It is good at global search i.e. not in one direction and it works with a population of points instead of a single point. Also it blends the merits of both deterministic algorithm BP and stochastic optimizing algorithm GA.
Jasmeen Gill is working as Assistant Professor in CSE department , RIMT-IET,Punjab,India.She has more than 10 years of teaching experience and more than 20 research articles to her credit. Shaminder Singh is working as Assistant Professorat GGI Khanna ,Punjab ,India . He has 11 years of teaching experience and 18 research articles tohis credit.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Weather plays a significant role in terms of life, property, agriculture and industry. Neural networks are capable of predicting the non-linear behavior of weather without the physics being explicitly explored. The most common method to train neural networks is through gradient decent based back propagation algorithm. But back propagation algorithm suffers from several disadvantages like local minima problem, slow training, and scaling problem. So the ways to solve these problems by hybridizing it with genetic algorithms. The hybrid technique can learn efficiently by combining the strengths of genetic algorithm with back propagation algorithm . The hybrid neural networks are more qualified if only the requirement of a global searching is considered. It is good at global search i.e. not in one direction and it works with a population of points instead of a single point. Also it blends the merits of both deterministic algorithm BP and stochastic optimizing algorithm GA. 80 pp. Englisch. N° de réf. du vendeur 9783659786600
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gill JasmeenJasmeen Gill is working as Assistant Professor in CSE department , RIMT-IET,Punjab,India.She has more than 10 years of teaching experience and more than 20 research articles to her credit. Shaminder Singh is working as As. N° de réf. du vendeur 158876622
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Weather plays a significant role in terms of life, property, agriculture and industry. Neural networks are capable of predicting the non-linear behavior of weather without the physics being explicitly explored. The most common method to train neural networks is through gradient decent based back propagation algorithm. But back propagation algorithm suffers from several disadvantages like local minima problem, slow training, and scaling problem. So the ways to solve these problems by hybridizing it with genetic algorithms. The hybrid technique can learn efficiently by combining the strengths of genetic algorithm with back propagation algorithm . The hybrid neural networks are more qualified if only the requirement of a global searching is considered. It is good at global search i.e. not in one direction and it works with a population of points instead of a single point. Also it blends the merits of both deterministic algorithm BP and stochastic optimizing algorithm GA.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch. N° de réf. du vendeur 9783659786600
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Weather plays a significant role in terms of life, property, agriculture and industry. Neural networks are capable of predicting the non-linear behavior of weather without the physics being explicitly explored. The most common method to train neural networks is through gradient decent based back propagation algorithm. But back propagation algorithm suffers from several disadvantages like local minima problem, slow training, and scaling problem. So the ways to solve these problems by hybridizing it with genetic algorithms. The hybrid technique can learn efficiently by combining the strengths of genetic algorithm with back propagation algorithm . The hybrid neural networks are more qualified if only the requirement of a global searching is considered. It is good at global search i.e. not in one direction and it works with a population of points instead of a single point. Also it blends the merits of both deterministic algorithm BP and stochastic optimizing algorithm GA. N° de réf. du vendeur 9783659786600
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Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. Hybrid Neural Networks for Weather Forecasting | An Integrated Approach to Forecasting | Jasmeen Gill (u. a.) | Taschenbuch | 80 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659786600 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 104147473
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