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Input-Output Polynomial Models

Input-output polynomial models, including ARX, ARMAX, output-error, and Box-Jenkins model structures

A polynomial model uses a generalized notion of transfer functions to express the relationship between the input, u(t), the output y(t), and the noise e(t) using an equation of the form:

A(q)y(t)=B(q)F(q)u(t−nk)+C(q)D(q)e(t)

A(q), B(q), F(q), C(q) and D(q) are polynomial matrices in terms of the time-shift operator q-1. u(t) is the input, and nk is the input delay. y(t) is the output and e(t) is the disturbance signal.

Each polynomial has an independent order, or number of estimable coefficients. For example, if A(q) has an order of 2, then theA polynomial has the form A(q) = 1 + a1q-1 + a2q-2.

In practice, not all the polynomials are simultaneously active. Simpler polynomial forms, such as ARX, ARMAX, Output-Error, and Box-Jenkins provide model structures suitable for specific objectives such as handling nonstationary disturbances or providing completely independent parameterization for dynamics and noise. For more information about these model types, see What Are Polynomial Models?

Apps

System IdentificationIdentify models of dynamic systems from measured data

Functions

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idpolyPolynomial model with identifiable parameters
arxEstimate parameters of ARX, ARIX, AR, or ARI model
armaxEstimate parameters of ARMAX, ARIMAX, ARMA, or ARIMA model using time-domain data
bjEstimate Box-Jenkins polynomial model using time domain data
iv4ARX model estimation using four-stage instrumental variable method
ivxARX model estimation using instrumental variable method with arbitrary instruments
oeEstimate output-error polynomial model using time-domain or frequency-domain data
polyestEstimate polynomial model using time- or frequency-domain data
pemPrediction error minimization for refining linear and nonlinear models
arxstrucCompute loss functions for single-output ARX models
ivstrucCompute loss functions for sets of ARX model structures using instrumental variable method
selstrucSelect model order for single-output ARX models
strucGenerate model-order combinations for single-output ARX model estimation
arxRegulDetermine regularization constants for ARX model estimation
delayestEstimate time delay (dead time) from data
initSet or randomize initial parameter values
polydataAccess polynomial coefficients and uncertainties of identified model
getpvecObtain model parameters and associated uncertainty data
setpvecModify values of model parameters
getparObtain attributes such as values and bounds of linear model parameters
setparSet attributes such as values and bounds of linear model parameters
setPolyFormatSpecify format for B and F polynomials of multi-input polynomial model
armaxOptionsOption set for armax
arxOptionsOption set for arx
arxRegulOptionsOption set for arxRegul
bjOptionsOption set for bj
iv4OptionsOption set for iv4
oeOptionsOption set for oe
polyestOptionsOption set for polyest

Topics

Polynomial Model Basics

What Are Polynomial Models?

Polynomial model structures including ARX, ARMAX, output-error, and Box-Jenkins.

Data Supported by Polynomial Models

Use time-domain and frequency-domain data to estimate discrete-time and continuous-time models.

Estimate Polynomial Models

Preliminary Step – Estimating Model Orders and Input Delays

To estimate polynomial models, you must provide input delays and model orders.

Estimate Polynomial Models in the App

Import data into the app, specify model orders, delays and estimation options.

Estimate Polynomial Models at the Command Line

Specify model orders, delays, and estimation options.

Polynomial Sizes and Orders of Multi-Output Polynomial Models

Size of A, B, C, D, and F polynomials for multi-output models.

Estimate Models Using armax

This example shows how to estimate a linear, polynomial model with an ARMAX structure for a three-input and single-output (MISO) system using the iterative estimation method armax.

Set Polynomial Model Options

Specifying Initial States for Iterative Estimation Algorithms

When you use the pem or polyest functions to estimate ARMAX, Box-Jenkins (BJ), Output-Error (OE), you must specify how the algorithm treats initial conditions.

Polynomial Model Estimation Algorithms

Choose between the ARX and IV algorithms for ARX and AR model estimation.

Featured Examples

Spectrum Estimation Using Complex Data - Marple's Test Case

Spectrum Estimation Using Complex Data - Marple's Test Case

Perform spectral estimation on time series data. We use Marple's test case (The complex data in L. Marple: S.L. Marple, Jr, Digital Spectral Analysis with Applications, Prentice-Hall, Englewood Cliffs, NJ 1987.)

Open Live Script

System Identification Toolbox Documentation

Support

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