General Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices.

Applied Predictive Modeling

Fermentation of lignocellulosic hydrolysates. II: inhibitors and mechanisms of inhibition.

Surrogate-based Analysis and Optimization

https://eprints.soton.ac.uk/65935/01/Forr_09.pdf

Recent advances in surrogate-based optimization

The use of statistical techniques to build approximations of expensive computer analysis codes pervades much of today’s engineering design. These statistical approximations, or metamodels, are used to replace the actual expensive computer analyses, facilitating multidisciplinary, multiobjective optimization and concept exploration. In this paper, we review several of these techniques, including design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning and kriging. We survey their existing application in engineering design, and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of statistical approximation techniques in given situations, and how common pitfalls can be avoided.

/pdf/metamodels-for-computer-based-engineering-design-survey-and-382x1fki1s.pdf

Metamodels for Computer-Based Engineering Design: Survey and Recommendations

From the Publisher:
Using a practical approach, it discusses two-level factorial and fractional factorial designs, several aspects of empirical modeling with regression techniques, focusing on response surface methodology, mixture experiments and robust design techniques. Features numerous authentic application examples and problems. Illustrates how computers can be a useful aid in problem solving. Includes a disk containing computer programs for a response surface methodology simulation exercise and concerning mixtures.

http://www.gbv.de/dms/ilmenau/toc/187413967.PDF

Raymond H. Myers

Papers

Response Surface Methodology: Process and Product Optimization Using Designed Experiments

Wiley Series in Probability and Statistics