Hybrid Neural Networks for Weather Forecasting : An Integrated Approach to Forecasting

Language: English

Published by LAP LAMBERT Academic Publishing, 2015

3659786608 / 9783659786600

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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.

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Title
Hybrid Neural Networks for Weather Forecasting : An Integrated Approach to Forecasting
Author
Shaminder Singh
Publisher
LAP LAMBERT Academic Publishing
Publication year
2015
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3659786608
ISBN 13
9783659786600
Item weight
137 grams
Dimensions
220x150x6 mm

AHA-BUCH GmbH

Einbeck, Germany

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