fuzzy logic artificial neural de deka paresh (6 resultados)

Taschenbuch. Condición: Neu. Neuware -The combination of Artificial Neural Network and Fuzzy Logic are probably the most attractive techniques among the researchers in recent times which is capable of handling non-linear, imprecise, fuzzy, noisy and probabilistic information to solve complex problem in efficient manner. Hybrid systems are designed to take advantage of the strengths of each system and avoid the limitations of each system. It is natural for neural networks to learn but it is cumbersome for a fuzzy system to learn. Hence a combination of the two would result in a rule- based system that can learn and adapt. This book, therefore, provide a comprehensive and integrated approach using Fuzzy logic and Artificial neural network techniques in modeling selected hydrological problems related to International river Brahmaputra within India. Four different hydrological problems are modeled using the proposed fuzzy ¿ neural network approach for examining the usefulness of it. This comprehensive real time hydrological modelling study should be especially useful to the Hydrologist, civil engineers, agriculturists, students, field engineers and related governmental as well as non-governmental organisations.Books on Demand GmbH, Überseering 33, 22297 Hamburg 184 pp. Englisch.

Paperback. Condición: Brand New. 184 pages. 8.66x5.91x0.42 inches. In Stock.

paperback. Condición: New. New. book.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Deka Paresh ChandraDr.Deka took Phd from IIT, Guwahati in 2004.He served as Assistant professor in Arbaminch University, Ethiopia during 2005-2008.Currently, He is the faculty of NIT, Surathkal, India. He has published more than twen.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The combination of Artificial Neural Network and Fuzzy Logic are probably the most attractive techniques among the researchers in recent times which is capable of handling non-linear, imprecise, fuzzy, noisy and probabilistic information to solve complex problem in efficient manner. Hybrid systems are designed to take advantage of the strengths of each system and avoid the limitations of each system. It is natural for neural networks to learn but it is cumbersome for a fuzzy system to learn. Hence a combination of the two would result in a rule- based system that can learn and adapt. This book, therefore, provide a comprehensive and integrated approach using Fuzzy logic and Artificial neural network techniques in modeling selected hydrological problems related to International river Brahmaputra within India. Four different hydrological problems are modeled using the proposed fuzzy neural network approach for examining the usefulness of it. This comprehensive real time hydrological modelling study should be especially useful to the Hydrologist, civil engineers, agriculturists, students, field engineers and related governmental as well as non-governmental organisations. 184 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The combination of Artificial Neural Network and Fuzzy Logic are probably the most attractive techniques among the researchers in recent times which is capable of handling non-linear, imprecise, fuzzy, noisy and probabilistic information to solve complex problem in efficient manner. Hybrid systems are designed to take advantage of the strengths of each system and avoid the limitations of each system. It is natural for neural networks to learn but it is cumbersome for a fuzzy system to learn. Hence a combination of the two would result in a rule- based system that can learn and adapt. This book, therefore, provide a comprehensive and integrated approach using Fuzzy logic and Artificial neural network techniques in modeling selected hydrological problems related to International river Brahmaputra within India. Four different hydrological problems are modeled using the proposed fuzzy neural network approach for examining the usefulness of it. This comprehensive real time hydrological modelling study should be especially useful to the Hydrologist, civil engineers, agriculturists, students, field engineers and related governmental as well as non-governmental organisations.