User manual MATHWORKS SYSTEM IDENTIFICATION TOOLBOX RELEASE NOTES

[. . . ] System Identification ToolboxTM Release Notes How to Contact MathWorks Web Newsgroup www. mathworks. com/contact_TS. html Technical Support www. mathworks. com comp. soft-sys. matlab suggest@mathworks. com bugs@mathworks. com doc@mathworks. com service@mathworks. com info@mathworks. com Product enhancement suggestions Bug reports Documentation error reports Order status, license renewals, passcodes Sales, pricing, and general information 508-647-7000 (Phone) 508-647-7001 (Fax) The MathWorks, Inc. 3 Apple Hill Drive Natick, MA 01760-2098 For contact information about worldwide offices, see the MathWorks Web site. System Identification ToolboxTM Release Notes © COPYRIGHT 2003­2010 by The MathWorks, Inc. The software described in this document is furnished under a license agreement. The software may be used or copied only under the terms of the license agreement. [. . . ] For more information about these blocks, see the IDNLARX Model (nonlinear ARX model) and the IDNLHW Model (Hammerstein-Wiener model) block reference pages. Linearizing Nonlinear Black-Box Models at User-Specified Operating Points You can now use the linearize command to linearize nonlinear black-box models, including nonlinear ARX and Hammerstein-Wiener models, at specified operating points. Linearization produces a first-order Taylor series approximation of the system about an operating point. An operating point is defined by the set of constant input and state values for the model. If you do not know the operating point, you can use the findop command to compute it from specifications, such as steady-state requirements or values of these quantities at a given time instant from the simulation of the model. 14 Version 7. 2 (R2008a) System Identification ToolboxTM Software For nonlinear ARX models, if all of the steady-state input and output values are known, you can map these values to the model state values using the data2state command. linearize replaces lintan and removes the restriction for linearizing models containing custom regressors or specific nonlinearity estimators, such as neuralnet and treepartition. If you have installed Simulink® Control DesignTM software, you can linearize nonlinear ARX and Hammerstein-Wiener models in Simulink after importing them into Simulink. For more information, see: · "Linear Approximation of Nonlinear Black-Box Models" about computing operating points and linearizing models · "Simulating Model Output" about importing nonlinear black-box models into Simulink Estimating Multiple-Output Models Using Weighted Sum of Least Squares Minimization Criterion You can now specify a custom weighted trace criterion for minimization when estimating linear and nonlinear black-box models for multiple-output systems. This feature is useful for controlling the relative importance of output channels during the estimation process. The Algorithm property of linear and nonlinear models now provides the Criterion field for choosing the minimization criterion. This new field can have the following values: · det -- (Default) Specify this option to minimize the determinant of the prediction error covariance. This choice leads to maximum likelihood estimates of model parameters. It implicitly uses the inverse of estimated noise variance as the weighting function. · trace -- Specify this option to define your own weighing function that controls the relative weights of output signals during the estimation. This criterion minimizes the weighted sum of least square prediction errors. You 15 System Identification ToolboxTM Release Notes can specify the relative weighting of prediction errors for each output using the new Weighting field of the Algorithm property. By default, Weighting is an identity matrix, which means that all outputs are weighed equally. For more information about these new Algorithm fields for linear estimation, see the Algorithm Properties reference page. For more information about Algorithm fields for nonlinear estimation, see the idnlarx and idnlhw reference pages. Note If you are estimating a single-output model, det and trace values of the Criterion field produce the same estimation results. Improved Handling of Initial States for Linear and Nonlinear Models The following are new options to handle initial states for nonlinear models: · For nonlinear ARX models (idnlarx), you can now specify a numerical vector for initial states when using sim or predict by setting the Init argument. For example: predict(model, data, 'init', [1;2;3;4]) where the last argument is the state vector. · For Hammerstein-Wiener models (idnlhw), you can now choose to estimate the initial states when using predict or nlhw by setting INIT='e'. If you want to specify your own initial states, see the corresponding model reference pages for a definition of the states for each model type. If you do not know the states, you can use the findop or the findstates command to compute the states. For more information about using these commands, see the findop(idnlarx), findop(idnlhw), findstates(idnlarx), and findstates(idnlhw) reference pages. 16 Version 7. 2 (R2008a) System Identification ToolboxTM Software To help you interpret the states of a nonlinear ARX model, you can use the getDelayInfo command. [. . . ] Function or Property Name What Happens When You Use Function or Property?Still runs Use This Instead Compatibility Considerations idmodred balred See "balred Introduced for Model Reduction" on page 26. 27 System Identification ToolboxTM Release Notes Version 6. 1. 2 (R14SP3) System Identification Toolbox Software This table summarizes what's new in Version 6. 1. 2 (R14SP3): New Features and Changes No Version Compatibility Considerations No Fixed Bugs and Known Problems Bug Reports Related Documentation at Web Site No 28 Version 6. 1. 1 (R14SP2) System Identification ToolboxTM Software Version 6. 1. 1 (R14SP2) System Identification Toolbox Software This table summarizes what's new in Version 6. 1. 1 (R14SP2): New Features and Changes No Version Compatibility Considerations No Fixed Bugs and Known Problems Fixed bugs Related Documentation at Web Site No 29 System Identification ToolboxTM Release Notes Version 6. 0 (R13SP2) System Identification Toolbox Software This table summarizes what's new in Version 6. 0 (R13SP2): New Features and Changes Yes Details below Version Compatibility Considerations Yes Summary Fixed Bugs and Known Problems No bug fixes Related Documentation at Web Site V6. 0 product documentation New features and changes introduced in this version are: · "idproc Model Object Added" on page 30 · "Estimation and Validation in Frequency Domain Now Supported" on page 31 · "Continuous-Time Data Can Now Be Stored Using Frequency-Domain Objects" on page 31 · "Simulink Software Now Supports iddata and idmodel Objects" on page 32 · "advice About Data and Models Now Available" on page 32 · "Theta Models No Longer Supported" on page 32 idproc Model Object Added A new model object, idproc, is used to represent simple continuous-time process models. This object is characterized by static gain, possible dead time, and dominating time constant(s). A new GUI that supports this object is available in the System Identification Toolbox GUI. [. . . ]