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비선형 모델 식별 기본 사항

식별된 비선형 모델, 블랙박스 모델링 및 정규화

예제 및 방법

Identify Nonlinear Black-Box Models Using System Identification App

Identify nonlinear black-box models from single-input/single-output (SISO) data using the System Identification app.


모델 객체의 유형

모델 객체의 유형에는 계수가 고정된 시스템을 나타내는 수치 모델과, 조정 가능하거나 불확실한 계수가 있는 시스템에 대한 일반화된 모델이 포함됩니다.

About Identified Nonlinear Models

Dynamic models in System Identification Toolbox™ software are mathematical relationships between the inputs u(t) and outputs y(t) of a system.

Nonlinear Model Structures

Construct model objects for nonlinear model structures, access model properties.

Available Nonlinear Models

The System Identification Toolbox software provides three types of nonlinear model structures:

Black-Box Modeling

Black-box modeling is useful when your primary interest is in fitting the data regardless of a particular mathematical structure of the model.

Modeling Multiple-Output Systems

Use a multiple-output modeling technique that suits the complexity and internal input-output coupling of your system.

Preparing Data for Nonlinear Identification

Estimating nonlinear ARX and Hammerstein-Wiener models requires uniformly sampled time-domain data.

Loss Function and Model Quality Metrics

Configure the loss function that is minimized during parameter estimation. After estimation, use model quality metrics to assess the quality of identified models.

Regularized Estimates of Model Parameters

Regularization is the technique for specifying constraints on the flexibility of a model, thereby reducing uncertainty in the estimated parameter values.

Estimation Report

The estimation report contains information about the results and options used for a model estimation.

Next Steps After Getting an Accurate Model

How you can work with identified models.