Name
Title | ||
|---|---|---|
An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking. |
Abstract | ||
|---|---|---|
To obtain the online solution of complex-valued systems of linear equation in complex domain with 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 gradient neural network (GNN) model and the ZNN model. Finally, the application for controlling the robot using the proposed method for the comple-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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3389/fnbot.2017.00045 | FRONTIERS IN NEUROROBOTICS |
Keywords | Field | DocType |
complex-valued systems of linear equation,recurrent neural network,finite-time convergence,robot,gradient neural network,motion tracking | Linear equation,Feedforward neural network,Computer science,Stochastic neural network,Recurrent neural network,Probabilistic neural network,Artificial intelligence,Rate of convergence,Artificial neural network,Match moving,Machine learning | Journal |
Volume | ISSN | Citations |
11 | 1662-5218 | 2 |
PageRank | References | Authors |
0.36 | 22 | 5 |