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伝達関数モデル

伝達関数モデル

アプリ

System IdentificationIdentify models of dynamic systems from measured data

関数

tfestTransfer function estimation
idtfTransfer function model with identifiable parameters
pemPrediction error estimate for linear and nonlinear model
delayestEstimate time delay (dead time) from data
getpvecModel parameters and associated uncertainty data
setpvecModify value 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
tfdata伝達関数データにアクセスする
initSet or randomize initial parameter values
tfestOptionsOption set for tfest

例および操作のヒント

Estimate Transfer Function Models in the System Identification App

This topic shows how to estimate transfer function models in the System Identification app.

Estimate Transfer Function Models at the Command Line

This topic shows how to estimate transfer function models at the command line.

Estimate Transfer Function Models by Specifying Number of Poles

This example shows how to identify a transfer function containing a specified number of poles for given data.

Estimate Transfer Function Models with Transport Delay to Fit Given Frequency-Response Data

This example shows how to identify a transfer function to fit a given frequency response data (FRD) containing additional phase roll off induced by input delay.

Estimate Transfer Function Models With Prior Knowledge of Model Structure and Constraints

This example shows how to estimate a transfer function model when the structure of the expected model is known and apply constraints to the numerator and denominator coefficients.

Estimate Transfer Functions with Delays

This example shows how to estimate transfer function models with I/O delays.

Estimate Transfer Function Models with Unknown Transport Delays

This example shows how to estimate a transfer function model with unknown transport delays and apply an upper bound on the unknown transport delays.

Troubleshoot Frequency-Domain Identification of Transfer Function Models

Improve frequency-domain model estimation by preprocessing data and applying frequency-dependent weighting filters.

概念

What are Transfer Function Models?

Transfer function models describe the relationship between the inputs and outputs of a system using a ratio of polynomials. The model order is equal to the order of the denominator polynomial. The roots of the denominator polynomial are referred to as the model poles. The roots of the numerator polynomial are referred to as the model zeros.

Data Supported by Transfer Function Models

Characteristics of estimation data for transfer function identification.

Transfer Function Structure Specification

Specify the values and constraints for the numerator, denominator and transport delays.

Specifying Initial Conditions for Iterative Estimation of Transfer Functions

Specify how initial conditions are handled during model estimation in the app and at the command line.

Identifying State-Space Models with Separate Process and Measurement Noise Descriptions

An identified linear model is used to simulate and predict system outputs for given input and noise signals. The input signals are measured while the noise signals are only known via their statistical mean and variance. The general form of the state-space model, often associated with Kalman filtering, is an example of such a model, and is defined as: