Output Error Model System Identification
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Search All Support Resources Support Documentation MathWorks Search MathWorks.com MathWorks Documentation Support Documentation Toggle navigation Trial oe model Software Product Updates Documentation Home System Identification Toolbox Examples Functions arx model and Other Reference Release Notes PDF Documentation Linear Model Identification Input-Output Polynomial Models System Identification
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Toolbox Functions oe On this page Syntax Description Input Arguments Name-Value Pair Arguments Output Arguments Examples Estimate Continuous-Time Model Using Frequency Response Estimate Output-Error Model
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Using Regularization Estimate Model Using Band-Limited Discrete-Time Frequency-Domain Data Alternatives More About Output-Error (OE) Model Continuous-Time, Output-Error Model Tips Algorithms See Also This is machine translation Translated by Mouse over text to see original. Click the button below to return to the English verison of the page. Back to English output error cisco × Translate This Page Select Language Bulgarian Catalan Chinese Simplified Chinese Traditional Czech Danish Dutch English Estonian Finnish French German Greek Haitian Creole Hindi Hmong Daw Hungarian Indonesian Italian Japanese Korean Latvian Lithuanian Malay Maltese Norwegian Polish Portuguese Romanian Russian Slovak Slovenian Spanish Swedish Thai Turkish Ukrainian Vietnamese Welsh MathWorks Machine Translation The automated translation of this page is provided by a general purpose third party translator tool. MathWorks does not warrant, and disclaims all liability for, the accuracy, suitability, or fitness for purpose of the translation. Translate oeEstimate Output-Error polynomial model using time or frequency domain datacollapse all in page Syntaxsys = oe(data,[nb nf nk])
sys = oe(data,[nb nf nk],Name,Value)
sys = oe(data,init_sys)
sys = oe(data,___,opt)
Descriptionsys
= oe(data,[nb nf nk]) estimates an Output-Error model, sys, represented by:y(t)=B(q)F(q)u(t−nk)+e(t)y(t) is the output, u(t) is the input, and e(t) is the error.sys is estimated for the time- or frequency-domain, measu
models. These are described later on in the manual. At a basic level it is sufficient to think of them as variants of the ARX model
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allowing also a characterization of the properties of the disturbances e. Linear state-space models are also easy to work with. The essential structure variable is just a scalar: the model order. This gives just one knob to turn when searching for a suitable model description. See below. General linear models can be described symbolically by y=Gu+He which says that the measured output https://www.mathworks.com/help/ident/ref/oe.html y(t) is a sum of one contribution that comes from the measured input u(t) and one contribution that comes from the noise He. The symbol G then denotes the dynamic properties of the system, that is, how the output is formed from the input. For linear systems it is called the transfer function from input to output. The symbol H refers to the http://www-rohan.sdsu.edu/doc/matlab/toolbox/ident/ch1sip6.html noise properties, and is called the disturbance model. It describes how the disturbances at the output are formed from some standardized noise source e(t). State-space models are common representations of dynamical models. They describe the same type of linear difference relationship between the inputs and the outputs as in the ARX model, but they are rearranged so that only one delay is used in the expressions. To achieve this, some extra variables, the state variables, are introduced. They are not measured, but can be reconstructed from the measured input-output data. This is especially useful when there are several output signals, i.e., when y(t) is a vector. Tutorial, gives more details about this. For basic use of the toolbox it is sufficient to know that the order of the state-space model relates to the number of delayed inputs and outputs used in the corresponding linear difference equation. The state-space representation looks like x(t+1)=Ax(t)+Bu(t)+Ke(t) y(t)=Cx(t)+Du(t)+e(t) Here x(t) is the vector of state variables. The model order is the dimension of this vector. The matrix K determines the disturbance properties. Notice that if K = 0, then the noise
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