Design of Experiments by Examples Using Matlab

Design of Experiments by Examples Using Matlab
Author :
Publisher : Createspace Independent Publishing Platform
Total Pages : 196
Release :
ISBN-10 : 1974098109
ISBN-13 : 9781974098101
Rating : 4/5 (09 Downloads)

Book Synopsis Design of Experiments by Examples Using Matlab by : Perez C.

Download or read book Design of Experiments by Examples Using Matlab written by Perez C. and published by Createspace Independent Publishing Platform. This book was released on 2017-07-31 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB provides apps and design tools for optimally calibrating complex engines and powertrain subsystems. You can work with design of experiments, define optimal test plans, automatically fit statistical models, and generate calibrations and lookup tables for complex high-degree-of-freedom engines that would otherwise require exhaustive testing using traditional methods. Calibrations can be optimized at individual operating points or over drive cycles to identify the optimal balance of engine fuel economy, performance, and emissions. Using apps or MATLAB(R) functions, you can automate the calibration process for similar engine types. The Key Features in this book are the following: -Apps that support the entire workflow: designing experiments, fitting statistical models to engine data, and producing optimal calibrations -Design-of-Experiments methodology for reducing testing time through classical, space-filling, and optimal design techniques -Accurate engine modeling with data fitting techniques including Gaussian process, radial basis function, and linear regression modeling -Boundary modeling to keep optimization results within the engine operating envelope Generation of lookup tables from optimizations over drive cycles, models, or test data -Export of performance-optimized models to Simulink for use in simulation and HIL testing This book develops the following topics: - "Model-Based Calibration Toolbox" - "Design of Experiments" - "Empirical Engine Modeling" - "Selecting Data and Models to Fit" - "Selecting Global and Two-Stage Models" - "Using Validation Data" - "Exporting the Models" - "Optimized Calibration" - "Importing Additional Models into CAGE" - "Setting Up and running the Optimization" - "Composite Models and Modal Optimization" - "Use Optimization Results"


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