An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking

Frontiers in Neurorobotics

Bolin Liao¹  Lei Ding¹  Rongbo Lu¹  Lin Xiao¹  Hua Peng¹ 

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

关键词: complex-valued systems of linear equation;    recurrent neural network;    finite-time convergence;    robot;    gradient neural network;    motion tracking;   

DOI  :  10.3389/fnbot.2017.00045

来源: DOAJ

【 摘 要 】

To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new neural network for complex-valued systems of linear equation in complex domain is proposed and theoretically proved to be convergent within finite time. Then, the illustrative results show that the new neural network model has the higher precision and the higher convergence rate, as compared with the gradient neural network (GNN) model and the ZNN model. Finally, the application for controlling the robot using the proposed method for the complex-valued systems of linear equation is realized, and the simulation results verify the effectiveness and superiorness of the new neural network for the complex-valued systems of linear equation.

【 授权许可】

Unknown