Name
Abstract | ||
|---|---|---|
This paper proposes a method to design an optimal control system by use of a neural network. Because the normal backpropagation (BP) method cannot be applied to this case, we choose Powell's conjugate direction algorithm for training the neural network. In a regulator problem, the neural network functions as a state feedback controller, and in a servo problem it functions as both feedfoward and feedback controller. The proposed method can be applied to various problem where conventional methods cannot be applied. Simulation results show the effectivity of the proposed method |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/ICNN.1997.616139 | Neural Networks,1997., International Conference |
Keywords | Field | DocType |
closed loop systems,control system synthesis,discrete time systems,learning (artificial intelligence),multilayer perceptrons,neurocontrollers,optimal control,servomechanisms,state feedback,powell's conjugate direction algorithm,design method,feedfoward controller,neural network,optimal control system,servo problem,state feedback controller,stationary state,algorithm design and analysis,neural networks,learning artificial intelligence,backpropagation,design methodology | Regulator,Optimal control,Servo,Feedback controller,Full state feedback,Computer science,Control theory,Time delay neural network,Artificial neural network,Backpropagation | Conference |
Volume | ISBN | Citations |
2 | 0-7803-4122-8 | 3 |
PageRank | References | Authors |
0.77 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nakanishi, H. | 1 | 3 | 4.15 |
| Takehisa Kohda | 2 | 6 | 1.85 |
| Koichi Inoue | 3 | 17 | 5.27 |