Identification and control of continuous-time nonlinear systems via dynamic neural networks

2003 ◽  
Vol 50 (3) ◽  
pp. 478-486 ◽  
Author(s):  
X.M. Ren ◽  
A.B. Rad ◽  
P.T. Chan ◽  
W.L. Lo
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Continuous Time ◽  
Dynamic Neural Networks ◽  
And Control

Dynamic Neural Networks for Modeling and Control of Nonlinear Systems

2003 ◽  
Vol 9 (2) ◽  
pp. 61-70 ◽  
Author(s):  
Farzad Pourboghrat ◽  
Harin Pongpairoj ◽  
Ziqian Liu ◽  
Farshad Farid ◽  
Farhang Pourboghrat ◽  
...  
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Dynamic Neural Networks ◽  
Modeling And Control ◽  
And Control

Finite-Dimensional Filtering and Control for Continuous-Time Nonlinear Systems

2004 ◽  
Vol 22 (2) ◽  
pp. 499-505
Author(s):  
Robert J. Elliott ◽  
Lakhdar Aggoun ◽  
Ali Benmerzouga
Keyword(s):  
Nonlinear Systems ◽  
Continuous Time ◽  
Finite Dimensional ◽  
And Control

Robust adaptive tracking for nonlinear systems using dynamic neural networks

1999 ◽  
Vol 32 (2) ◽  
pp. 4476-4481
Author(s):  
Dai Qionghai ◽  
Wu Hongwei ◽  
Sun Fuxin ◽  
Li Yanda ◽  
Wang Wei ◽  
...  
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Dynamic Neural Networks ◽  
Adaptive Tracking ◽  
Robust Adaptive

scholarly journals A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yiming Jiang ◽  
Chenguang Yang ◽  
Jing Na ◽  
Guang Li ◽  
Yanan Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Human Robot Interaction ◽  
Learning Tools ◽  
Robot Interaction ◽  
Practical Applications ◽  
Powerful Learning ◽  
Wide Range ◽  
Complex Nonlinear Systems ◽  
And Control

As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

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