Kazemi Mahdi, Arefi Mohammad Mehdi
Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
ISA Trans. 2017 Mar;67:382-388. doi: 10.1016/j.isatra.2016.12.002. Epub 2016 Dec 15.
In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used.
本文针对存在输出有色噪声的非线性系统提出了一种在线辨识算法。所提方法基于扩展递归最小二乘（ERLS）算法，其中辨识出的系统为多项式维纳形式。为此，通过一种内部迭代算法估计一个未知中间信号。当系统参数变化时，该迭代递归算法自适应地修改所提维纳模型的参数向量。此外，为提高所提方法对变化的鲁棒性，将一种鲁棒RLS算法应用于该模型。给出了仿真结果以表明所提方法的有效性。结果证实所提方法具有快速收敛速率和鲁棒特性，提高了所提模型和辨识方法的效率。例如，在使用约400个数据的连续搅拌釜式反应器（CSTR）过程中，拟合度（FIT）准则将达到92%。
