Documentation Center |
Estimate polynomial model using time- or frequency-domain data
sys = polyest(data,[na nb nc nd nf nk])
sys = polyest(data,[na nb nc nd nf nk],Name,Value)
sys = polyest(data,init_sys)
sys = polyest(___, opt)
sys = polyest(data,[na nb nc nd nf nk]) estimates a polynomial model, sys, using the time- or frequency-domain data, data.
sys is of the form
A(q), B(q), F(q), C(q) and D(q) are polynomial matrices. u(t) is the input, and nk is the input delay. y(t) is the output and e(t) is the disturbance signal. na ,nb, nc, nd and nf are the orders of the A(q), B(q), C(q), D(q) and F(q) polynomials, respectively.
sys = polyest(data,[na nb nc nd nf nk],Name,Value) estimates a polynomial model with additional attributes of the estimated model structure specified by one or more Name,Value pair arguments.
sys = polyest(data,init_sys) estimates a polynomial model using the dynamic system init_sys to configure the initial parameterization.
sys = polyest(___, opt) estimates a polynomial model using the option set, opt, to specify estimation behavior.
sys |
Estimated polynomial model. sys is an idpoly model. If data.Ts is zero, sys is a continuous-time model representing:
Y(s), U(s) and E(s) are the Laplace transforms of the time-domain signals y(t), u(t) and e(t), respectively. |
To estimate a polynomial model using time-series data, use ar.
Use polyest to estimate a polynomial of arbitrary structure. If the structure of the estimated polynomial model is known, that is, you know which polynomials will be active, then use the appropriate dedicated estimating function. For examples, for an ARX model, use arx. Other polynomial model estimating functions include, oe, armax, and bj.
To estimate a continuous-time transfer function, use tfest. You can also use oe, but only with continuous-time frequency-domain data.
ar | armax | arx | bj | forecast | iddata | idpoly | oe | pem | polyestOptions | procest | ssest | tfest