| 000 | 04966nam a22005175i 4500 | ||
|---|---|---|---|
| 001 | 978-0-387-71435-6 | ||
| 003 | DE-He213 | ||
| 005 | 20250710084012.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100301s2007 xxu| s |||| 0|eng d | ||
| 020 |
_a9780387714356 _a99780387714356 |
||
| 024 | 7 |
_a10.1007/978-0-387-71435-6 _2doi |
|
| 082 | 0 | 4 |
_a519.5 _223 |
| 100 | 1 |
_aCastillo, Enrique Del. _eauthor. |
|
| 245 | 1 | 0 |
_aProcess Optimization _h[recurso electrónico] : _bA Statistical Approach / _cby Enrique Del Castillo. |
| 264 | 1 |
_aBoston, MA : _bSpringer US, _c2007. |
|
| 300 | _bonline resource. | ||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_arecurso en línea _bcr _2rdacarrier |
||
| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aInternational Series in Operations Research & Management Science, _x0884-8289 ; _v105 |
|
| 505 | 0 | _aPreliminaries -- 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 | _aPROCESS 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. | ||
| 650 | 0 | _aENGINEERING. | |
| 650 | 0 | _aMATHEMATICAL OPTIMIZATION. | |
| 650 | 0 | _aSTATISTICS. | |
| 650 | 0 | _aMATHEMATICAL STATISTICS. | |
| 650 | 0 | _aENGINEERING DESIGN. | |
| 650 | 0 | _aSYSTEM SAFETY. | |
| 650 | 1 | 4 | _aENGINEERING. |
| 650 | 2 | 4 | _aSTATISTICAL THEORY AND METHODS. |
| 650 | 2 | 4 | _aQUALITY CONTROL, RELIABILITY, SAFETY AND RISK. |
| 650 | 2 | 4 | _aSTATISTICS, GENERAL. |
| 650 | 2 | 4 | _aMATHEMATICAL MODELING AND INDUSTRIAL MATHEMATICS. |
| 650 | 2 | 4 | _aENGINEERING DESIGN. |
| 650 | 2 | 4 | _aOPTIMIZATION. |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9780387714349 |
| 830 | 0 |
_aInternational Series in Operations Research & Management Science, _x0884-8289 ; _v105 |
|
| 856 | 4 | 0 |
_uhttp://dx.doi.org/10.1007/978-0-387-71435-6 _zVer el texto completo en las instalaciones del CICY |
| 912 | _aZDB-2-SBE | ||
| 942 |
_2ddc _cER |
||
| 999 |
_c58247 _d58247 |
||