大葉大學圖書館 |

Advanced multiresponse process optimisation[electronic resource] :an intelligent and integrated approach /

纪录类型:	书目-语言数据,印刷品 : Monograph/item
[NT 15000414] null:	670
[NT 47271] Title/Author:	Advanced multiresponse process optimisation : an intelligent and integrated approach // by Tatjana V. Sibalija, Vidosav D. Majstorovic.
作者:	Sibalija, Tatjana V.
[NT 51406] other author:	Majstorovic, Vidosav D.
出版者:	Cham : : Springer International Publishing :, 2016.
面页册数:	xvii, 284 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
标题:	Manufacturing processes.
标题:	Sustainable engineering.
标题:	Engineering.
标题:	Manufacturing, Machines, Tools.
标题:	Artificial Intelligence (incl. Robotics)
标题:	Robotics and Automation.
标题:	Computational Intelligence.
标题:	Operation Research/Decision Theory.
ISBN:	9783319192550
ISBN:	9783319192543
[NT 15000228] null:	Introduction -- Review of multiresponse optimisation approaches -- An intelligent, integrated, problem-independent method for multiresponse process optimisation -- Implementation of an intelligent, integrated, problem-independent method to multiresponse process optimisation -- Case studies -- Conclusion.
[NT 15000229] null:	This book presents an intelligent, integrated, problem-independent method for multiresponse process optimization. In contrast to traditional approaches, the idea of this method is to provide a unique model for the optimization of various processes, without imposition of assumptions relating to the type of process, the type and number of process parameters and responses, or interdependences among them. The presented method for experimental design of processes with multiple correlated responses is composed of three modules: an expert system that selects the experimental plan based on the orthogonal arrays; the factor effects approach, which performs processing of experimental data based on Taguchi's quality loss function and multivariate statistical methods; and process modeling and optimization based on artificial neural networks and metaheuristic optimization algorithms. The implementation is demonstrated using four case studies relating to high-tech industries and advanced, non-conventional processes.
电子资源:	http://dx.doi.org/10.1007/978-3-319-19255-0

Advanced multiresponse process optimisation[electronic resource] :an intelligent and integrated approach /
Sibalija, Tatjana V.

Advanced multiresponse process optimisationan intelligent and integrated approach /[electronic resource] :by Tatjana V. Sibalija, Vidosav D. Majstorovic. - Cham :Springer International Publishing :2016. - xvii, 284 p. :ill. (some col.), digital ;24 cm.

Introduction -- Review of multiresponse optimisation approaches -- An intelligent, integrated, problem-independent method for multiresponse process optimisation -- Implementation of an intelligent, integrated, problem-independent method to multiresponse process optimisation -- Case studies -- Conclusion.

This book presents an intelligent, integrated, problem-independent method for multiresponse process optimization. In contrast to traditional approaches, the idea of this method is to provide a unique model for the optimization of various processes, without imposition of assumptions relating to the type of process, the type and number of process parameters and responses, or interdependences among them. The presented method for experimental design of processes with multiple correlated responses is composed of three modules: an expert system that selects the experimental plan based on the orthogonal arrays; the factor effects approach, which performs processing of experimental data based on Taguchi's quality loss function and multivariate statistical methods; and process modeling and optimization based on artificial neural networks and metaheuristic optimization algorithms. The implementation is demonstrated using four case studies relating to high-tech industries and advanced, non-conventional processes.

ISBN: 9783319192550

Standard No.: 10.1007/978-3-319-19255-0doiSubjects--Topical Terms:

340969
Manufacturing processes.

LC Class. No.: TS183

Dewey Class. No.: 670

Advanced multiresponse process optimisation[electronic resource] :an intelligent and integrated approach /
LDR:02311nam a2200313 a 4500 001 454286
003 DE-He213
005 20160712170843.0
006 m d
007 cr nn 008maaau
008 161227s2016 gw s 0 eng d
020 $a 9783319192550 $q (electronic bk.)
020 $a 9783319192543 $q (paper)
024 7 $a 10.1007/978-3-319-19255-0 $2 doi
035 $a 978-3-319-19255-0
040 $a GP $c GP
041 0 $a eng
050 4 $a TS183
072 7 $a TGXT $2 bicssc
072 7 $a TEC020000 $2 bisacsh
082 0 4 $a 670 $2 23
090 $a TS183 $b .S563 2016
100 1 $a Sibalija, Tatjana V. $3 651631
245 1 0 $a Advanced multiresponse process optimisation $h [electronic resource] : $b an intelligent and integrated approach / $c by Tatjana V. Sibalija, Vidosav D. Majstorovic.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xvii, 284 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Introduction -- Review of multiresponse optimisation approaches -- An intelligent, integrated, problem-independent method for multiresponse process optimisation -- Implementation of an intelligent, integrated, problem-independent method to multiresponse process optimisation -- Case studies -- Conclusion.
520 $a This book presents an intelligent, integrated, problem-independent method for multiresponse process optimization. In contrast to traditional approaches, the idea of this method is to provide a unique model for the optimization of various processes, without imposition of assumptions relating to the type of process, the type and number of process parameters and responses, or interdependences among them. The presented method for experimental design of processes with multiple correlated responses is composed of three modules: an expert system that selects the experimental plan based on the orthogonal arrays; the factor effects approach, which performs processing of experimental data based on Taguchi's quality loss function and multivariate statistical methods; and process modeling and optimization based on artificial neural networks and metaheuristic optimization algorithms. The implementation is demonstrated using four case studies relating to high-tech industries and advanced, non-conventional processes.
650 0 $a Manufacturing processes. $3 340969
650 0 $a Sustainable engineering. $3 418803
650 1 4 $a Engineering. $3 372756
650 2 4 $a Manufacturing, Machines, Tools. $3 463924
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 463642
650 2 4 $a Robotics and Automation. $3 463885
650 2 4 $a Computational Intelligence. $3 463962
650 2 4 $a Operation Research/Decision Theory. $3 511310
700 1 $a Majstorovic, Vidosav D. $3 651632
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-19255-0
950 $a Engineering (Springer-11647)