Process Optimization A Statistical Approach /

PROCESS OPTIMIZATION: A Statistical Approach is a textbook for a course in experimental optimization techniques for industrial production processes and other "noisy" systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Qualit...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: del Castillo, Enrique (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer US : Imprint: Springer, 2007.
Edición:1st ed. 2007.
Colección:International Series in Operations Research & Management Science, 105
Temas:
Probabilities.
Security systems.
Engineering design.
Statistics .
Mathematical models.
Probability Theory.
Security Science and Technology.
Engineering Design.
Statistical Theory and Methods.
Statistics.
Mathematical Modeling and Industrial Mathematics.
Acceso en línea:Texto Completo

MARC

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490 1 |a International Series in Operations Research & Management Science,  |x 2214-7934 ;  |v 105 
505 0 |a Preliminaries -- An Overview of Empirical Process Optimization -- Elements of Response Surface Methods -- Optimization Of First Order Models -- Experimental Designs For First Order Models -- Analysis and Optimization of Second Order Models -- Experimental Designs for Second Order Models -- Statistical Inference in Process Optimization -- Statistical Inference in First Order RSM Optimization -- Statistical Inference in Second Order RSM Optimization -- Bias Vs. Variance -- Robust Parameter Design and Robust Optimization -- Robust Parameter Design -- Robust Optimization -- Bayesian Approaches in Process Optimization -- to Bayesian Inference -- Bayesian Methods for Process Optimization -- to Optimization of Simulation and Computer Models -- Simulation Optimization -- Kriging and Computer Experiments -- Appendices -- Basics of Linear Regression -- Analysis of Variance -- Matrix Algebra and Optimization Results -- Some Probability Results Used in Bayesian Inference. 
520 |a PROCESS OPTIMIZATION: A Statistical Approach is a textbook for a course in experimental optimization techniques for industrial production processes and other "noisy" systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Quality and Process Engineers and Applied Statisticians working in industry, in particular, in semiconductor/electronics manufacturing and in biotech manufacturing industries. The major features of PROCESS OPTIMIZATION: A Statistical Approach are: It provides a complete exposition of mainstream experimental design techniques, including designs for first and second order models, response surface and optimal designs; Discusses mainstream response surface method in detail, including unconstrained and constrained (i.e., ridge analysis and dual and multiple response) approaches; Includes an extensive discussion of Robust Parameter Design (RPD) problems, including experimental design issues such as Split Plot designs and recent optimization approaches used for RPD; Presents a detailed treatment of Bayesian Optimization approaches based on experimental data (including an introduction to Bayesian inference), including single and multiple response optimization and model robust optimization; Provides an in-depth presentation of the statistical issues that arise in optimization problems, including confidence regions on the optimal settings of a process, stopping rules in experimental optimization and more; Contains a discussion on robust optimization methods as used in mathematical programming and their application in response surface optimization; Offers software programs written in MATLAB and MAPLE to implement Bayesian and frequentist process optimization methods; Provides an introduction to the optimization of computer and simulation experiments including and introduction to stochastic approximation and stochastic perturbation stochastic approximation (SPSA) methods; Includes an introduction to Kriging methods and experimental design for computer experiments; Provides extensive appendices on Linear Regression, ANOVA, and Optimization Results. . 
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650 2 4 |a Engineering Design. 
650 2 4 |a Statistical Theory and Methods. 
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