Publisher's Synopsis
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