Institute of Automation, Beijing University of Chemical Technology, Beijing 100029, PR China.
Institute of Automation, Beijing University of Chemical Technology, Beijing 100029, PR China.
ISA Trans. 2018 Jan;72:77-91. doi: 10.1016/j.isatra.2017.10.001. Epub 2017 Oct 13.
In this paper, we study the system identification of multi-input multi-output (MIMO) Hammerstein processes under the typical heavy-tailed noise. To the best of our knowledge, there is no general analytical method to solve this identification problem. Motivated by this, we propose a general identification method to solve this problem based on a Gaussian-Mixture Distribution intelligent optimization algorithm (GMDA). The nonlinear part of Hammerstein process is modeled by a Radial Basis Function (RBF) neural network, and the identification problem is converted to an optimization problem. To overcome the drawbacks of analytical identification method in the presence of heavy-tailed noise, a meta-heuristic optimization algorithm, Cuckoo search (CS) algorithm is used. To improve its performance for this identification problem, the Gaussian-mixture Distribution (GMD) and the GMD sequences are introduced to improve the performance of the standard CS algorithm. Numerical simulations for different MIMO Hammerstein models are carried out, and the simulation results verify the effectiveness of the proposed GMDA.
在本文中，我们研究了典型重尾噪声下多输入多输出（MIMO）Hammerstein 过程的系统辨识。据我们所知，目前尚无通用的解析方法来解决这个辨识问题。受此启发，我们提出了一种基于高斯混合分布智能优化算法（GMDA）的通用辨识方法来解决这个问题。Hammerstein 过程的非线性部分采用径向基函数（RBF）神经网络进行建模，辨识问题转化为优化问题。为了克服重尾噪声下解析辨识方法的缺点，采用了一种启发式优化算法——布谷鸟搜索（CS）算法。为了提高其在该辨识问题上的性能，引入了高斯混合分布（GMD）和 GMD 序列来改进标准 CS 算法的性能。对不同的 MIMO Hammerstein 模型进行了数值模拟，模拟结果验证了所提出的 GMDA 的有效性。
