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linear model identification -凯发k8网页登录

identify impulse-response, frequency-response, and parametric models, such as linear state-space and transfer function models

use linear model identification when a linear model is sufficient to completely capture your system dynamics. you can identify linear models in the system identification app or at the command line. system identification toolbox™ enables you to create and estimate four general types of linear model.

  • parametric models — estimate parameters in structures such as transfer function models, linear state-space models, polynomial models, and process models.

  • frequency-response models — estimate spectral models using spectral analysis.

  • correlation models — perform nonparametric estimation of impulse-response models using correlation analysis.

  • linear grey-box models — estimate the coefficients of arbitrary ordinary differential or difference equations, incorporating system information that you obtain from prior knowledge or that you can deduce from physical principles.

linear model identification requires frequency-domain or uniformly sampled time-domain data. your data can have one or more input and output channels. for more information, see . you can also model time series data, which contains a single output channel and no input channel, using parametric model structures such as ar and arma.

you can use the identified models to simulate and predict model output at the command line, in the app, or in simulink®.

categories


  • essential information for identifying linear models, selecting suitable model structures, constructing and modifying model object structures, and using regularized estimation

  • low-order transfer function models with static gain, time constant, and input/output delay

  • input-output polynomial models, including arx, armax, output-error, and box-jenkins model structures

  • state-space models with free, canonical, and structured parameterizations; equivalent armax and output-error (oe) models

  • transfer function models

  • estimate coefficients of linear differential, difference, and state-space equations

  • frequency-response models obtained using spectral analysis

  • impulse-response models obtained using correlation analysis
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