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非線形モデル同定の基礎

概念

Dynamic models in System Identification Toolbox™ software are mathematical relationships between the inputs u(t) and outputs y(t) of a system. The model is dynamic because the output value at the current time depends on the input-output values at previous time instants. Therefore, dynamic models have memory of the past. You can use the input-output relationships to compute the current output from previous inputs and outputs. Dynamic models have states, where a state vector contains the information of the past.

• Nonlinear Model Structures

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

• Available Nonlinear Models

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

• ブラックボックス モデリング

ブラックボックス モデリングは、モデルの特定の数学的構造に関係なくデータを当てはめることに主な関心がある場合に有益です。

• 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. Your data can have one or more input and output channels.

• 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. This report is stored in the Report property of the estimated model. The exact contents of the report depend on the estimator function you use to obtain the model.

• Next Steps After Getting an Accurate Model

How you can work with identified models.

• モデル オブジェクトのタイプ

モデル オブジェクト タイプには、固定係数をもつシステムを表すための数値モデル、および調整可能な係数または不確かな係数をもつシステムのための一般化モデルがあります。