基于随机配置网络的非线性系统智能建模方法
作者:
作者单位:

辽宁石油化工大学

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中图分类号:

510.8010

基金项目:

国家自然科学基金项目(61773107, 61991402,61866021, 61890923, 61833004, 61973202)


Intelligent modeling method of nonlinear system based on stochastic configuration network
Author:
Affiliation:

Liaoning Shihua University

Fund Project:

The National Natural Science Foundation of China (61773107, 61991402,61866021, 61890923, 61833004, 61973202)

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    摘要:

    针对一类非线性离散动态系统,利用系统运行在工作点附近的特性将其表示为低阶模型与高阶非线性项和的形式,在此基础上采用交替辨识策略研究整个非线性系统的智能建模方法.采用带遗忘因子的递推最小二乘法对低阶模型的未知参数进行辨识,对高阶非线性部分则采用随机配置网络进行估计,从而提出了一种改进的非线性系统交替辨识智能建模方法.该算法充分利用了随机配置网络可以根据监控机制随机分配隐含节点的输入权值和偏差的特点,对输出权值进行自动校正,并逐步增加隐层节点,直到达到预设定的估计精度.将随机配置网络与递推最小二乘算法相结合,有效提高了非线性系统的辨识精度.最后通过数值仿真实验进行对比分析,实验结果说明了所提算法的有效性.

    Abstract:

    For a class of nonlinear discrete dynamic system, it is expressed as the sum of low-order model and high-order nonlinear term by the characteristics of the system running near the working point. On this basis, the intelligent modeling method of the whole nonlinear system is studied by using alternating identification strategy. Recursive least square method with forgetting factor is used to identify unknown parameters of low order model, and stochastic configuration networks is used to estimate the high order nonlinear part. Therefore, an improved intelligent modeling method for alternating identification of nonlinear system is proposed. The algorithm makes full use of the feature that the stochastic configuration networks can randomly assign the input weights and deviations of the hidden nodes according to the monitoring mechanism, automatically corrects the output weights, and gradually increases the hidden nodes until the pre-set estimation accuracy is reached. The combination of stochastic configuration networks and recursive least square algorithm can effectively improve the identification accuracy of nonlinear systems. Finally, the numerical simulation experiments are carried out to illustrate the effectiveness of the proposed algorithm.

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历史
  • 收稿日期:2021-03-16
  • 最后修改日期:2021-06-21
  • 录用日期:2021-07-05
  • 在线发布日期: 2021-08-01
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