Isbn: 9783659401237 - temporal weather prediction using genetic algorithm: utilizing the techniques of back propagation algorithms (8 risultati)

Condizione: New. pp. 64.

Taschenbuch. Condizione: Neu. Temporal Weather Prediction using Genetic Algorithm | Utilizing the techniques of Back Propagation Algorithms | Pankaj Bhambri (u. a.) | Taschenbuch | 64 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659401237 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 64 pp. Englisch.…

Condizione: New. Print on Demand pp. 64 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condizione: New. PRINT ON DEMAND pp. 64.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…