Perez C. - DESIGN OF EXPERIMENTS by EXAMPLES using MATLAB

DESIGN OF EXPERIMENTS BY EXAMPLES USING MATLAB

Prez C.

Introduction

The design of experiments ( DOE , DOX , or experimental design ) is the design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associated with true experiments in which the design introduces conditions that directly affect the variation, but may also refer to the design of quasi - experiments , in which natural conditions that influence the variation are selected for observation.

In its simplest form, an experiment aims at predicting the outcome by introducing a change of the preconditions, which is reflected in a variable called the predictor (independent) . The change in the predictor is generally hypothesized to result in a change in the second variable, hence called the outcome (dependent) variable. Experimental design involves not only the selection of suitable predictors and outcomes, but planning the delivery of the experiment under statistically optimal conditions given the constraints of available resources.

Main concerns in experimental design include the establishment of validity , reliability , and replicability . For example, these concerns can be partially addressed by carefully choosing the predictor, reducing the risk of measurement error, and ensuring that the documentation of the method is sufficiently detailed. Related concerns include achieving appropriate levels of statistical power and sensitivity .

Correctly designed experiments advance knowledge in the natural and social sciences and engineering. Other applications include marketing and policy making.

MATLAB Model-Based Calibration Toolbox provides apps and design tools for optimally calibrating complex experimental designs models. This Toolbox use un Apps that support the entire workflow: designing experiments, fitting statistical models to engine data, and producing optimal calibrations. Also support Design-of-Experiments methodology for reducing testing time through classical, space-filling, and optimal design techniques, This Toolbox accurate engine modeling with data fitting techniques including Gaussian process,radial basis function, and linear regression modeling. Other options are: Boundary modeling to keep optimization results within the engine operating envelope, Generation of lookup tables from optimizations over drive cycles, models, or test data. Also is posible export of performance-optimized models to Simulink for use in simulation and HILtesting