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Applications of soft computing in time series forecasting[electronic resource] :simulation and modeling techniques /

纪录类型:	书目-语言数据,印刷品 : Monograph/item
[NT 15000414] null:	006.3
[NT 47271] Title/Author:	Applications of soft computing in time series forecasting : simulation and modeling techniques // by Pritpal Singh.
作者:	Singh, Pritpal.
出版者:	Cham : : Springer International Publishing :, 2016.
面页册数:	xxi, 158 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
标题:	Engineering.
标题:	Artificial intelligence.
标题:	Computer simulation.
标题:	Statistical physics.
标题:	Computational intelligence.
标题:	Nonlinear Dynamics.
标题:	Simulation and Modeling.
标题:	Artificial Intelligence (incl. Robotics)
ISBN:	9783319262932
ISBN:	9783319262925
[NT 15000229] null:	This book reports on an in-depth study of fuzzy time series (FTS) modeling. It reviews and summarizes previous research work in FTS modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and evolutionary computing (EC), focusing on how these techniques can be integrated into different phases of the FTS modeling approach. In particular, the book describes novel methods resulting from the hybridization of FTS modeling approaches with neural networks and particle swarm optimization. It also demonstrates how a new ANN-based model can be successfully applied in the context of predicting Indian summer monsoon rainfall. Thanks to its easy-to-read style and the clear explanations of the models, the book can be used as a concise yet comprehensive reference guide to fuzzy time series modeling, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and government organizations.
电子资源:	http://dx.doi.org/10.1007/978-3-319-26293-2

Applications of soft computing in time series forecasting[electronic resource] :simulation and modeling techniques /
Singh, Pritpal.

Applications of soft computing in time series forecastingsimulation and modeling techniques /[electronic resource] :by Pritpal Singh. - Cham :Springer International Publishing :2016. - xxi, 158 p. :ill. (some col.), digital ;24 cm. - Studies in fuzziness and soft computing,v.3301434-9922 ;. - Studies in fuzziness and soft computing ;v.273..

This book reports on an in-depth study of fuzzy time series (FTS) modeling. It reviews and summarizes previous research work in FTS modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and evolutionary computing (EC), focusing on how these techniques can be integrated into different phases of the FTS modeling approach. In particular, the book describes novel methods resulting from the hybridization of FTS modeling approaches with neural networks and particle swarm optimization. It also demonstrates how a new ANN-based model can be successfully applied in the context of predicting Indian summer monsoon rainfall. Thanks to its easy-to-read style and the clear explanations of the models, the book can be used as a concise yet comprehensive reference guide to fuzzy time series modeling, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and government organizations.

ISBN: 9783319262932

Standard No.: 10.1007/978-3-319-26293-2doiSubjects--Topical Terms:

372756
Engineering.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Applications of soft computing in time series forecasting[electronic resource] :simulation and modeling techniques /
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520 $a This book reports on an in-depth study of fuzzy time series (FTS) modeling. It reviews and summarizes previous research work in FTS modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and evolutionary computing (EC), focusing on how these techniques can be integrated into different phases of the FTS modeling approach. In particular, the book describes novel methods resulting from the hybridization of FTS modeling approaches with neural networks and particle swarm optimization. It also demonstrates how a new ANN-based model can be successfully applied in the context of predicting Indian summer monsoon rainfall. Thanks to its easy-to-read style and the clear explanations of the models, the book can be used as a concise yet comprehensive reference guide to fuzzy time series modeling, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and government organizations.
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