Modeling of State-Dependent Switching System Based on Data-Driven
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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，建立了输入与输出之间更形象的表达形式；并在此基础上提出一种框架，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拟合其切换规则，深度挖掘状态依赖切换系统的信息，得到了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降低了建模的复杂度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%.
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Key words:
- state-dependent switching systems /
- data-driven /
- data model /
- neural network
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图 9 状态空间输出与所提出框架建模的输出对比
Figure 9. Comparison of state space output and proposed framework modeling output
图 10 替代电路输出电流预测和测试对比
Figure 10. Comparison of predicted and test output currents of alternative circuit
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