A Self-Organizing Quasi-Linear ARX RBFN Model for Nonlinear Dynamical Systems Identification
A Self-Organizing Quasi-Linear ARX RBFN Model for Nonlinear Dynamical Systems Identification
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DOI：
10.9746/jcmsi.9.70
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
I. Sutrisno;M. A. Jami’in;Jinglu Hu;M. Marhaban
中科院分区：
文献类型：
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作者：
I. Sutrisno;M. A. Jami’in;Jinglu Hu;M. Marhaban
准线性ARX径向基函数网络（RBFN）模型在非线性系统辨识和控制中表现出良好的逼近能力和实用性。该算法结构简单，泛化能力强，对输入噪声有较强的容忍能力。在准线性ARX RBFN模型的基础上引入自组织机制，提出了一种自组织准线性ARX RBFN模型（QARX-RBFN）。基于主动发射率和RBF节点的互信息，可以在准线性ARX RBFN模型中增加或删除RBF节点，从而自动优化给定系统的准线性ARX RBFN模型结构。这大大提高了模型的性能。非线性动力系统的辨识和控制的数值仿真验证了所提出的自组织QARX-RBFN模型的有效性。
The quasi-linear ARX radial basis function network (RBFN) model has shown good approximation ability and usefulness in nonlinear system identification and control. It has an easy-to-use structure, good generalization and strong tolerance to input noise. In this paper, we propose a self-organizing quasi-linear ARX RBFN (QARX-RBFN) model by introducing a self-organizing scheme to the quasi-linear ARX RBFN model. Based on the active firing rate and the mutual information of RBF nodes, the RBF nodes in the quasi-linear ARX RBFN model can be added or removed, so as to automatically optimize the structure of the quasi-linear ARX RBFN model for a given system. This significantly improves the performance of the model. Numerical simulations on both identification and control of nonlinear dynamical system confirm the effectiveness of the proposed self-organizing QARX-RBFN model.