基于粒子群算法的时变时滞系统辨识

Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788) Pub Date : 2004-06-15 DOI:10.1109/WCICA.2004.1340586

Jing Ke, Y. Qiao, Jixin Qian

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引用次数: 2

摘要

粒子群优化算法是一种新的进化计算方法，适用于非线性、不可微、多模态的复杂优化问题。提出了一种基于粒子群算法的时变时滞系统辨识方法。该方法的基本思想是将辨识问题转化为混合整数非线性规划问题，然后利用粒子群算法求解时变参数的最优估计。仿真结果表明，所提出的辨识方案对时变时滞系统具有良好的跟踪能力。

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Identification of time-varying delay systems using particle swarm optimization

Particle swarm optimization algorithm is a new evolutionary computation method, which is applicable to complex optimization problems that are nonlinear, nondifferentiable and multimodal. A method for identification of time-varying delay systems using particle swarm optimization is proposed. The basic idea of the method is that the identification problems are cast as mixed-integer nonlinear programming problems, and then particle swarm optimization algorithm is used to find the optimal estimation of the time-varying parameters. Simulation results reveal that the suggested identification scheme possesses a good tracking ability to the time-varying delay systems.

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Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788)

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