西南交通大学学报2024,Vol.59Issue(3) :493-500.DOI:10.3969/j.issn.0258-2724.20210579

状态依赖型切换系统的数据驱动方法建模

Modeling of State-Dependent Switching System Based on Data-Driven

王涛 谭吉 刘东 杨叶江
西南交通大学学报2024,Vol.59Issue(3) :493-500.DOI:10.3969/j.issn.0258-2724.20210579

状态依赖型切换系统的数据驱动方法建模

Modeling of State-Dependent Switching System Based on Data-Driven

王涛 1谭吉 1刘东 1杨叶江1
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作者信息

  • 1. 西南交通大学电气工程学院,四川成都 610031
  • 折叠

摘要

切换系统是由一系列连续或离散的子系统和切换机制组合而成的一类复杂系统,状态依赖型切换系统因其复杂性而尚未被深入研究.因此,通过系统的输入输出轨迹来对状态依赖的切换系统进行数据驱动建模,利用数据挖掘技术寻找数据之间的有用信息,建立输入与输出之间更形象的表达形式;在此基础上提出一种结构框架,根据辨识轨迹的切换时刻将数据分段,借助神经网络建立子系统的模型以及切换规则,深度挖掘状态依赖切换系统的信息,得到切换系统中子系统及子系统间的信息.实验结果表明:相比传统的机理建模,本文提出的数据驱动方法将建模的复杂度降低了 17.3%.

Abstract

A switched system is a class of complex systems that integrate a series of continuous or discrete subsystems and switching mechanisms.State-dependent switching systems have not been studied in depth due to complexity.Therefore,the modeling of state-dependent switching systems is explored through the input-output trajectories of the systems.The data mining technique is used to find useful information between data and establish a more specific and explicit representation between inputs and outputs.On this basis,a framework is proposed to segment the data according to the switching time of the identified trajectory,build the subsystem model by neural network to fit its switching rules,deeply mine the information of the state-dependent switching system,and obtain the information between the subsystems and subsystems in the switching system.The experimental results show that compared with the modeling of traditional mechanism,the proposed data-driven method reduces the modeling complexity by 17.3%.

关键词

状态依赖切换系统/数据驱动/数据模型/神经网络

Key words

state-dependent switching systems/data-driven/data model/neural network

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基金项目

国家自然科学基金(U21A20169)

四川省自然科学基金面上项目(2022NSFSC045)

西安市科技计划项目(23ZDCYYYCJ0007)

出版年

2024
西南交通大学学报
西南交通大学

西南交通大学学报

CSTPCD北大核心
影响因子:0.973
ISSN:0258-2724
参考文献量2
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