Download Statistical Models for Design of Experiments Using Matlab Book in PDF, ePub and Kindle
Matlab incorporates a wide variety of statistical models for the design of experiments. A one-stage model fits a model to all the data in one process. If your data inputs do not have a hierarchical structure, and all model inputs are global at the same level, then fit a one-stage model. If your data has local and global inputs, where some variables are fixed while varying others, then choose a two-stage or point-by-point model instead. A two-stage model fits a model to data with a hierarchical structure. If your data has local and global inputs, where some variables are fixed while varying others, then choose a two-stage model. For example, data collected in the form of spark sweeps is suited to a two-stage model. Each test sweeps a range of spark angles, with fixed engine speed, load, and air/fuel ratio within each test. If your data inputs do not have a hierarchical structure, and all model inputs are global, at the same level, then fit a one-stage model instead. For two-stage models, only specify a single local variable. If you want more local inputs, use a one-stage or point-by-point model instead. Point-by-point modeling allows you to build a model at each operating point of an engine with the necessary accuracy to produce an optimal calibration. You often need point-bypoint models for multiple injection diesel engines and gasoline direct-injection engines. With point-by-point models, no predictions are available between operating points. If you need predictions between operating points, use a one-stage model instead. Additionally, MATLAB allows you to work with the following topics: -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: - "Setting Up Models" - "One-Stage Model" - "Two-Stage Model" - "Point-by-Point Model?" - "Polynomials and Polynomial Splines" - "Linear Modls" - "Growth Models" - "User-Defined Models" - "Transient Models" - "Covariance Modeling" - "Correlation Models" - "Local and Bundary Models" - "Global Models" - "Polynomials and Hybrid Splines" - "Gaussian Process Model" - "Radial Basis Function" - "Hybrid and Interpolating RBF" - "Multiple Linear Models" - "Neural Network Models" - "Assess and Explore Models" - "Selecting Data and Models to Fit" - "Projects and Test Plans" - "Desing Editor and Design Constraints" - "Creating a Space-Filling Design" - "Creating an Optimal Design" - "Creating a Classical Design" - "Manipulate Designs" - "Saving, Exporting, and Importing Designs" - "Fit Models to Collected Design Data - "Data Loading Application Programming Interface"
