Front Neurorobot. 2017 Sep 01;11:45. doi: 10.3389/fnbot.2017.00045. eCollection 2017.

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

Frontiers in neurorobotics

Lei Ding, Lin Xiao, Bolin Liao, Rongbo Lu, Hua Peng

PMID: 28919855 PMCID: PMC5585159 DOI: 10.3389/fnbot.2017.00045

Abstract

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.

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

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Publication Types

• Journal Article