CIPSO-ENN Coupling Algorithm for Nonlinear Dynamic System Parameter Identification

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CIPSO-ENN Coupling Algorithm for Nonlinear Dynamic System Parameter Identification

更新时间：2025-07-09

- CIPSO-ENN Coupling Algorithm for Nonlinear Dynamic System Parameter Identification
- Vol. 45, Issue 2, Pages: 49-53(2011)
- 作者机构：
  
  辽宁工程技术大学电气与控制工程学院,辽宁,葫芦岛,125105
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP183
- Online First：10 February 2011，
  
  Published：2011
- 稿件说明：
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CIPSO-ENN Coupling Algorithm for Nonlinear Dynamic System Parameter Identification[J]. 2011, 45(2): 49-53.
DOI：

CIPSO-ENN Coupling Algorithm for Nonlinear Dynamic System Parameter Identification[J]. 2011, 45(2): 49-53. DOI：

摘要

针对工程复杂性、时变性、非线性的特点

提出了基于混沌免疫粒子群算法(CIPSO)与Elman神经网络的耦合算法(CIPSD-ENN)

用于非线性动态模型参数辨识.CIPSO优化算法将人工免疫系统中的克隆选择和混沌优化机制引入粒子群算法

在粒子群种群进化过程中

该算法对粒子进行克隆选择

提高其收敛速度

对克隆后的粒子混沌变异以增强种群局部搜索能力.最后

CIPSO与动态反馈型Elman神经网络融合

对其权值、阈值寻优

建立了基于CIPSO和ENN的耦合算法系统辨识模型.实验结果表明

算法具有收敛速度快、收敛精度高、鲁棒性强的特点

与单纯Elman网络辨识相比

模型收敛速度提高了10倍

拟合精度提高了2个数量级.

Abstract

Aiming at the complexity

time varying and nonlinearity of the projects

a CIPSO-ENN coupling algorithm for identifying the parameters of nonlinear dynamic models is proposed

where the clonal selection of artificial immune system and chaotic mutation mechanism are embedded into standard particle swarm optimization. In the evolution of the particle swarm optimization population

this algorithm accelerates convergence of particle clonal selection and enhances the particle swarm local search capability after cloned particle chaotic mutation. Then CIPSO algorithm is merged with dynamic feedback Elman neural network to construct system identification model based on the CIPSO-ENN. The experiment results show that the identification model convergence rate is increased by 10 times and fitting accuracy is increased by 2 orders of magnitude compared with the pure Elman network identification method.

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references

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