An Improved Complex-Valued Recurrent Neural Network Model for Time-Varying Complex-Valued Sylvester Equation

IEEE Access

卷:7

Lin Xiao¹  Lei Ding²  Yongsheng Zhang²  Kaiqing Zhou²  Yonghong Lan³  Jichun Li⁴ 

[1] College of Information Science and Engineering, Hunan University, Changsha, China;

[2] College of Information Science and Engineering, Jishou University, Jishou, China;

[3] College of Information and Engineering, Xiangtan University, Xiangtan, China;

[4] School of Science, Engineering and Design, Teesside University, Middlesbrough, U.K.;

关键词: Zhang neural network;    complex-valued time-varying Sylvester equation;    convergence speed;    sign-multi-power function;    finite-time convergence;   

DOI  :  10.1109/ACCESS.2019.2896983

来源: DOAJ

【 摘 要 】

Complex-valued time-varying Sylvester equation (CVTVSE) has been successfully applied into mathematics and control domain. However, the computation load of solving CVTVSE will rise significantly with the increase of sampling rate, and it is a challenging job to tackle the CVTVSE online. In this paper, a new sign-multi-power activation function is designed. Based on this new activation function, an improved complex-valued Zhang neural network (ICZNN) model for tackling the CVTVSE is established. Furthermore, the strict proof for the maximum time of global convergence of the ICZNN is given in detail. A total of two numerical experiments are employed to verify the performance of the proposed ICZNN model, and the results show that, as compared with the previous Zhang neural network (ZNN) models with different nonlinear activation functions, this ICZNN model with the sign-multi-power activation function has a faster convergence speed to tackle the CVTVSE.

【 授权许可】

Unknown