Temporal Weather Prediction using Genetic Algorithm: Utilizing the techniques of Back Propagation Algorithms - Couverture souple

Bhambri, Pankaj; Singh, Shaminder

 
9783659401237: Temporal Weather Prediction using Genetic Algorithm: Utilizing the techniques of Back Propagation Algorithms
Couverture souple
ISBN 10 : 3659401234 ISBN 13 : 9783659401237
Editeur : LAP LAMBERT Academic Publishing, 2013

Synopsis

Prediction is a phenomenon of knowing what may happen to a system in the next coming time periods. Weather is a time series based, continuous, data-intensive, dynamic, and chaotic process.Due to dependence of weather on time series based data and non-linearity in climatic physics neural networks are suitable to predict meteorological processes. In the present research, firstly weather related data have been collected, weather parameters have been selected, N-Sliding window technique is applied, relations between dependent parameters are found and data has been normalized to feed to the network as input. After the per-processing of data, suitable neural network architecture has been determined and then the network has been trained by feeding the input as well as output data set under supervised training. Afterwards, testing of the networks has been done for different input sets to check how accurately the network has been trained. Finally, a comparison between the existing and proposed time series based technique has been done. The proposed hybrid technique can learn efficiently by combining the strengths of genetic algorithm with back propagation algorithm.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Prediction is a phenomenon of knowing what may happen to a system in the next coming time periods. Weather is a time series based, continuous, data-intensive, dynamic, and chaotic process.Due to dependence of weather on time series based data and non-linearity in climatic physics neural networks are suitable to predict meteorological processes. In the present research, firstly weather related data have been collected, weather parameters have been selected, N-Sliding window technique is applied, relations between dependent parameters are found and data has been normalized to feed to the network as input. After the per-processing of data, suitable neural network architecture has been determined and then the network has been trained by feeding the input as well as output data set under supervised training. Afterwards, testing of the networks has been done for different input sets to check how accurately the network has been trained. Finally, a comparison between the existing and proposed time series based technique has been done. The proposed hybrid technique can learn efficiently by combining the strengths of genetic algorithm with back propagation algorithm.

Biographie de l'auteur

Er. Pankaj Bhambri completed his M.Tech. (CSE) from Guru Nanak Dev Engineering College, Ludhiana after completing B.E. (IT) with HONORS from Dr. B.R.Ambedkar University, Agra. He has about 10 years of teaching experience. He has contributed in the areas of bioinformatics, image processing and parallel computing through books & research papers.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2013
Langue
anglais
ISBN 10 
3659401234
ISBN 13 
9783659401237
Reliure
Broché
Nombre de pages
64
Coordonnées du fabricant
non disponible
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