System Identification with Matlab. Nonlinear Models and Forecasting Time Series

by A. Smith

This book develops the work with Nonlinear Models and Time Series Identification. To represent nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. MATLAB System Identification Toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for system response prediction and plant modeling in Simulink. The toolbox also supports time-series data modeling and time-series forecasting.. It is possible to analyze time series data by identifying linear and nonlinear models, including AR, ARMA, and state-space models; forecast values The most important content that this book provides are the following: - When to Fit Nonlinear Models - Nonlinear Model Estimation - Nonlinear Model Structures - Nonlinear ARX Models - Hammerstein-Wiener Models - Nonlinear Grey-Box Models - Preparing Data for Nonlinear Identification - Identifying Nonlinear ARX Models - Prepare Data for Identification - Configure Nonlinear ARX Model Structure - Specify Estimation Options for Nonlinear ARX Models - Initialize Nonlinear ARX Estimation Using Linear Model - Estimate Nonlinear ARX Models in the App - Estimate Nonlinear ARX Models at the Command Line - Estimate Nonlinear ARX Models Initialized Using Linear ARX Models - Validate Nonlinear ARX Models - Using Nonlinear ARX Models - Linear Approximation of Nonlinear Black-Box Models - Nonlinear Black-Box Model Identification - Identifying Hammerstein-Wiener Models - Available Nonlinearity Estimators for Hammerstein-Wiener Models - Estimate Hammerstein-Wiener Models in the App . - Estimate Hammerstein-Wiener Models at the Command Line - Validating Hammerstein-Wiener Models - How the Software Computes Hammerstein-Wiener Model Output - Evaluating Nonlinearities (SISO) - Evaluating Nonlinearities (MIMO) - Simulation of Hammerstein-Wiener Model - Estimate Hammerstein-Wiener Models Initialized Using Linear OE Models - Estimate Linear Grey-Box Models - Estimate Continuous-Time Grey-Box Model for Heat Diffusion - Estimate Discrete-Time Grey-Box Model with Parameterized Disturbance - Estimate Coefficients of ODEs to Fit Given Solution - Estimate Model Using Zero/Pole/Gain Parameters - Estimate Nonlinear Grey-Box Models - Identifying State-Space Models with Separate Process and Measurement Noise Descriptions - Time Series Identification - Preparing Time-Series Data - Estimate Time-Series Power Spectra - Estimate AR and ARMA Models - Definition of AR and ARMA Models - Estimating Polynomial Time-Series Models in the App - Estimating AR and ARMA Models at the Command Line - Estimate State-Space Time Series Models - Identify Time-Series Models at the Command Line - Estimate ARIMA Models - Analyze Time-Series Models - Introduction to Forecasting of Dynamic System Response - Forecasting Time Series Using Linear Models - Forecasting Response of Linear Models with Exogenous Inputs - Forecasting Response of Nonlinear Models - Forecast the Output of a Dynamic System - Forecast Time Series Data Using an ARMA Model - Recursive Model Identification

266 pages, Paperback

Published November 19, 2017