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A much stronger treatment of the topic than the Wiley books published in this area, making this text a must-have for students and lecturers alike
Provides a complete exposition of mainstream experimental design techniques and response surface methods
Contains a mix of technical and practical sections, appropriate for a first year graduate text in the subject or useful for self-study or reference
Presents a detailed treatment of Bayesian Optimization approaches based on experimental data and includes an introduction to Bayesian inference
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.
Content Level »Graduate
Keywords »ANOVA - Analysis - Analysis of variance - MATLAB - Maple - Regression - electronics - linear regression - mathematical programming - model - optimization - programming - simulation