文献类型：期刊文献

英文题名：Noise-Tolerant Zeroing Neural Network for Solving Non-Stationary Lyapunov Equation

作者：Yan, Jingkun[1,2];Xiao, Xiuchun[3];Li, Hongxin[1,2];Zhang, Jiliang[1,2];Yan, Jingwen[4];Liu, Mei[1,2]

机构：[1]Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China;[2]Minist Educ, Key Lab Ind Internet Things & Networked Control, Chongqing 400000, Peoples R China;[3]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[4]Shantou Univ, Coll Engn, Shantou 515063, Peoples R China

年份：2019

卷号：7

起止页码：41517

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:000464448800001)、、EI(收录号：20191606798051)、Scopus(收录号：2-s2.0-85064267455)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61703189, in part by by the Fund of Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, China under Grant 2018FF06, in part by the International Science and Technology Cooperation Program of China under Grant 2017YFE0118900, in part by the Natural Science Foundation of Gansu Province, China, 18JR3RA264 and 18JR3RA268, in part by the Sichuan Science and Technology Program 19YYJC1656, in part by the Fundamental Research Funds for the Central Universities lzujbky-2017-193, in part by the Natural Science Foundation of Hunan Province 2017JJ3257, in part by the Research Foundation of Education Bureau of Hunan Province, China, 17C1299, in part by the Project of Enhancing School With Innovation of Guangdong Ocean University GDOU2014050226, and in part by the Key Lab of Digital Signal and Image Processing of Guangdong Province 2016GDDSIPL-02.

语种：英文

外文关键词：Non-stationary Lyapunov equation; noise-tolerant zeroing neural network (NTZNN); conventional zeroing neural network (CZNN); global and exponential convergence

外文摘要：As a crucial means for stability analysis in control systems, the Lyapunov equation is applied in many fields of science and engineering. There are some methods proposed and studied for solving the non-stationary Lyapunov equation, such as the zeroing neural network (ZNN) model. However, a common drawback these methods have is that they rarely tolerate noises. Therefore, given that the existence of various types of noises during computation, a noise-tolerant ZNN (NTZNN) model with anti-noise ability is proposed for solving the non-stationary Lyapunov equation in this paper. For comparison, the conventional ZNN (CZNN) model is also applied to solve the same problem. Furthermore, theoretical analyses are provided to prove the global and exponential convergence performance of the proposed NTZNN model in the absence of noises. On this basis, the anti-noise performance of the proposed NTZNN model is proven. Finally, by adopting the proposed NTZNN model and the CZNN model to solve the non-stationary Lyapunov equation, computer simulations are conducted under the noise-free case and the noisy case, respectively. The simulation results indicate that the proposed NTZNN model is practicable for solving the non-stationary Lyapunov equation and superior to the CZNN model at the existence of noises.