000			03919nam a2200265 i 4500
001			41260
008			160216s2015||||||| |||||||||||eng|d
020			_a9783319192550 _q(hardback).
035			_a(OCoLC)914706234
040			_aAU-PeEL _beng _cAU-PeEL _dAU-PeEL _dBAUN _erda
049			_aBAUN_MERKEZ
050	1	4	_aTS183 _b.S53 2016
100	1		_aSibalija, Tatjana V.
245	1	0	_aAdvanced multiresponse process optimisation: _ban intelligent and integrated approach / _cTatjana V Sibalija.
264		1	_aCham : _bSpringer International Publishing, _c2015.
300			_a309 pages : _billustrations (some color) ; _c26 cm.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnr
505	0	0	_tForeword _t-- Preface _t-- Acknowledgments _t-- Contents _t-- Abbreviations and Symbols _t-- 1 Introduction _t-- Abstract _t-- 1.1 Process Optimisation Based on Experimental Design _t-- 1.1.1 Foundations of Taguchi's Method _t-- 1.1.1.1 Orthogonal Arrays _t-- 1.1.1.2 Robustness _t-- 1.1.1.3 Quality Loss Function _t-- 1.2 The Need for Advanced Multiresponse Process Optimisation in a Modern Industry _t-- References _t-- 2 Review of Multiresponse Process Optimisation Methods _t-- Abstract _t-- 2.1 Conventional Multiresponse Process Optimisation Approaches Based on Statistical Methods _t-- 2.1.1 Response Surface Methodology. _t-- 2.1.2 Taguchi's Robust Parameter Design _t-- 2.1.2.1 Multiresponse Optimisation Based on Engineering Experience and Knowledge About the Process in Taguchi Method _t-- 2.1.2.2 Multiresponse Optimisation Based on the Assignment of Weigh Factors to Process Responses in Taguchi Method _t-- 2.1.2.3 Multiresponse Optimisation Based on Regression Analysis in Taguchi Method _t-- 2.1.2.4 Multiresponse Optimisation Based on Desirability Function Analysis in Taguchi Method _t-- 2.1.2.5 Multiresponse Optimisation Based on Data Envelopment Analysis in Taguchi Method. _t--2.1.2.6 Multiresponse Optimisation Based on Principal Component Analysis in Taguchi Method _t-- 2.1.2.7 Multiresponse Optimisation Based on Grey Relational Analysis in Taguchi Method _t-- 2.1.2.8 Other Conventional Multiresponse Optimisation Approaches _t-- 2.1.3 Multiresponse Optimisation Based on Goal-Programming _t-- 2.2 Non-conventional Multiresponse Process Optimisation Approaches Based on Artificial Intelligence Techniques _t-- 2.2.1 Multiresponse Optimisation Based on Fuzzy Multi-attribute Decision Making and Fuzzy Logic _t-- 2.2.2 Multiresponse Optimisation Based on Artificial Neural Networks. _t-- 2.2.3 Multiresponse Optimisation Based on Metaheuristic Search Techniques _t-- 2.2.3.1 Multiresponse Optimisation Based on Genetic Algorithm _t-- 2.2.3.2 Multiresponse Optimisation Based on Simulated Annealing _t-- 2.2.3.3 Multiresponse Optimisation Based on Particle Swarm Optimisation _t-- 2.2.3.4 Multiresponse Optimisation Based on Ant Colony Optimisation _t-- 2.2.3.5 Multiresponse Optimisation Based on Tabu Search _t-- 2.2.3.6 Multiresponse Optimisation Based on Recently Developed Evolutionary Algorithms _t-- Multiresponse Optimisation Based on Artificial Bee Colony Algorithm. _t-- Multiresponse Optimisation Based on Biogeography-Based Optimisation _t-- Multiresponse Optimisation Based on Teaching--Learning-Based Optimisation _t-- 2.2.4 Multiresponse Optimisation Using Expert System _t-- References _t-- 3 An Intelligent, Integrated, Problem-Independent Method for Multiresponse Process Optimisation _t-- Abstract _t-- 3.1 Method Overview: Intelligent System for Multiresponse Robust Process Design (IS-MR-RPD) Model _t-- 3.2 Design of Experimental Plan _t-- 3.2.1 Taguchi's Experimental Design: Orthogonal Arrays _t-- 3.2.2 Expert System for the Design of Experiment (ES_DoE) in IS-MR-RPD Model. _t--3.2.2.1 Expert System Shell Java DON.
650		0	_aExpert systems (Computer science) _953363
700	1		_aMajstorović, Vidosav D. _9101774 _eaut
942			_2lcc _cKT
999			_c37864 _d37864