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System Modeling Based on System Identification Toolbox in Matlab

Dong Hai-rui

Open publisher page 3 citations

Abstract

The function in system identification toolbox of Matlab mainly includes parametric or nonparametric model identification, model validation, regressive parameter estimation, model class definition and conversion, and graphic interface. So, based on the toolbox, the system was established models. The main steps include data collection, data preprocess, model structure selection, parameters estimation, model validation and dynamic simulation, etc.

About this research paper

What this paper is about

The function in system identification toolbox of Matlab mainly includes parametric or nonparametric model identification, model validation, regressive parameter estimation, model class definition and conversion, and graphic interface. So, based on the toolbox, the system was established models. The main steps include data collection, data preprocess, model structure selection, parameters estimation, model validation and dynamic simulation, etc.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Method / approach

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Available abstract

The function in system identification toolbox of Matlab mainly includes parametric or nonparametric model identification, model validation, regressive parameter estimation, model class definition and conversion, and graphic interface. So, based on the toolbox, the system was established models. The main steps include data collection, data preprocess, model structure selection, parameters estimation, model validation and dynamic simulation, etc.

Key concepts: Toolbox, MATLAB, Identification (biology), System identification, Computer science, Nonparametric statistics, Parametric model, Parametric statistics

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