東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Applications of soft computing in ti...

Singh, Pritpal.

Linked to FindBook

Google Book

Amazon

博客來

Applications of soft computing in time series forecasting = simulation and modeling techniques /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applications of soft computing in time series forecasting/ by Pritpal Singh.
Reminder of title:	simulation and modeling techniques /
Author:	Singh, Pritpal.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xxi, 158 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Engineering. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-26293-2
ISBN:	9783319262932$q(electronic bk.)

Applications of soft computing in time series forecasting = 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$q(electronic bk.)

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

586835
Engineering.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Applications of soft computing in time series forecasting = simulation and modeling techniques /
LDR:02065nmm a2200313 a 4500 001 2029183
003 DE-He213
005 20160722113946.0
006 m d
007 cr nn 008maaau
008 160908s2016 gw s 0 eng d
020 $a 9783319262932$q(electronic bk.)
020 $a 9783319262925$q(paper)
024 7 $a 10.1007/978-3-319-26293-2 $2 doi
035 $a 978-3-319-26293-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.3 $2 23
090 $a Q342 $b .S617 2016
100 1 $a Singh, Pritpal. $3 2180027
245 1 0 $a Applications of soft computing in time series forecasting $h [electronic resource] : $b simulation and modeling techniques / $c by Pritpal Singh.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xxi, 158 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in fuzziness and soft computing, $x 1434-9922 ; $v v.330
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.
650 0 $a Engineering. $3 586835
650 0 $a Artificial intelligence. $3 516317
650 0 $a Computer simulation. $3 525602
650 0 $a Statistical physics. $3 536281
650 0 $a Computational intelligence. $3 595739
650 2 4 $a Nonlinear Dynamics. $3 608190
650 2 4 $a Simulation and Modeling. $3 890873
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in fuzziness and soft computing ; $v v.273. $3 1566482
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-26293-2
950 $a Engineering (Springer-11647)