Parameter estimation plays a critical role in accurately describing system behavior through mathematical models such as statistical probability distribution functions, parametric dynamic models, and data-based Simulink® models.
Improving the accuracy of statistical models can involve estimating:
For more information, see Statistics and Machine Learning Toolbox™, which supports these and similar parameter estimation tasks with more than 40 different probability distributions, including Normal, Weibull, Gamma, Generalized Pareto, and Poisson. The toolbox also supports linear and nonlinear regression.
Creating accurate parametric dynamic models can involve estimating:
For more information, see System Identification Toolbox™, which supports these tasks with parameter estimation for linear and nonlinear parametric dynamic models.
Common tasks for parameter estimation of Simulink models include:
For more information, see Simulink Design Optimization™, which supports these parameter estimation tasks with an interactive tool that helps you configure, manipulate, and run your Simulink optimization problem.
See also: Statistics and Machine Learning Toolbox, Simulink Design Optimization, System Identification Toolbox, control systems, mathematical modeling, linearization, PID control, PID Tuning, battery simulation and controls consulting, parameter estimation videos