Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (8): 2319-2326.DOI: 10.11772/j.issn.1001-9081.2019111996

• Advanced computing • Previous Articles     Next Articles

Nonlinear systems identification based on structural adaptive filtering method

FENG Zikai, CHEN Lijia, LIU Mingguo, YUAN Meng’en   

  1. School of Physics and Electronics, Henan University, Kaifeng Henan 475000, China
  • Received:2019-11-25 Revised:2020-04-09 Online:2020-08-10 Published:2020-05-19
  • Supported by:
    This work is partially supported by the Natural Science Foundation of Henan Province (15A510018), the Seed Fund of Henan University (ZZJJ20140037).


冯子凯, 陈立家, 刘名果, 袁蒙恩   

  1. 河南大学 物理与电子学院, 河南 开封 475000
  • 通讯作者: 冯子凯(1994-),男,河南安阳人,硕士研究生,主要研究方向:演化算法、信号处理、人工智能;
  • 作者简介:陈立家(1979-),男,河南开封人,副教授,博士,主要研究方向:数字信号处理、演化算法、人工智能;刘名果(1984-),男,河南巩义人,副教授,博士,主要研究方向:数字信号处理、人工智能;袁蒙恩(1992-),女,河南商丘人,硕士研究生,主要研究方向:演化算法、信号处理、人工智能。
  • 基金资助:

Abstract: In order to solve the problems of high identification limitation and low identification rate in nonlinear system identification with fixed structure and parameters, a Subsystem-based Structural Adaptive Filtering (SSAF) method for nonlinear system identification was proposed with introducing structural adaptation into the optimization of identification. Multiple subsystems with linear-nonlinear hybrid structure were cascaded to form the model for this method. The linear part is a 1-order or 2-order Infinite Impulse Response (IIR) digital filter with uncertain parameters, and the nonlinear part is a static nonlinear function. In the initial stage, the parameters of the subsystems were randomly generated, and the generated subsystems were connected randomly according to the set connection rules, and the effectiveness of the nonlinear system was guaranteed by the connection mechanism with no feedback branches. An Adaptive Multiple-Elites-guided Composite Differential Evolution with a shift mechanism(AMECoDEs) algorithm was used for loop optimization of the adaptive model until the optimal structure and parameters were found, that is, the global optimal. The simulation results show that AMECoDEs performs well on nonlinear test functions and real data sets with high identification rate and good convergence rate. Compared with the Focused Time Lagged Recurrent Neural Network (FTLRNN), the number of parameters used in SSAF is reduced to 1/10, and the accuracy of fitness is improved by 7%, which proves the effectiveness of the proposed method.

Key words: structure adaptive filtering, nonlinear systems identification, subsystem, linear-nonlinear

摘要: 针对非线性系统辨识中定结构参数辨识局限性高和辨识率低的问题,将结构自适应引入辨识的优化,提出一种基于子系统的结构自适应滤波(SSAF)方法。该方法的模型由若干子系统级联而成,每一个子系统均为线性-非线性混合结构。子系统的线性部分是一个一阶或二阶可选的无限脉冲响应滤波器(IIR),非线性部分则是一个静态的非线性函数。初始化中,子系统的参数随机产生,生成的若干子系统按照设定的连接规则进行随机连接,而不含反馈的连接机制确保了非线性系统的有效性。采用一种自适应多精英引导的复合差分进化(AMECoDEs)算法用于自适应模型循环优化,直至找到最优的结构和参数,即全局最优。实验结果表明,SSAF方法在非线性测试函数以及真实数据集上的表现优异,辨识率高且收敛性好,与聚焦时滞递归神经网络(FTLRNN)相比,它所用参数的个数仅为FTLRNN的1/10,且适应值精度提高了7%,验证了所提方法的有效性。

关键词: 结构自适应滤波, 非线性系统辨识, 子系统, 线性-非线性

CLC Number: