Optimal State Variable Feedback With Bounded Gains

1969 ◽  
Vol 91 (2) ◽  
pp. 251-256
Author(s):  
H. F. Millers ◽  
Y. Takahashi
Keyword(s):  
Nonlinear Programming ◽  
Projection Method ◽  
Performance Criterion ◽  
Gradient Projection ◽  
Order Linear ◽  
Optimal State ◽  
State Variable ◽  
Linear And Nonlinear ◽  
Bounded Feedback ◽  
Fifth Order

The optimization of bounded feedback gains with respect to an arbitrary integral performance criterion is performed using nonlinear programming. The gradient projection method has been applied to several examples of fifth order, linear, and nonlinear, with good results.

IMPROVED NEURAL NETWORKS FOR LINEAR AND NONLINEAR PROGRAMMING

1991 ◽  
Vol 02 (04) ◽  
pp. 331-339 ◽  
Author(s):  
Jiahan Chen ◽  
Michael A. Shanblatt ◽  
Chia-Yiu Maa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nonlinear Programming ◽  
Penalty Function ◽  
New Combination ◽  
Optimal Point ◽  
Linear And Nonlinear Programming ◽  
Linear And Nonlinear ◽  
Simulation Results

A method for improving the performance of artificial neural networks for linear and nonlinear programming is presented. By analyzing the behavior of the conventional penalty function, the reason for the inherent degenerating accuracy is discovered. Based on this, a new combination penalty function is proposed which can ensure that the equilibrium point is acceptably close to the optimal point. A known neural network model has been modified by using the new penalty function and the corresponding circuit scheme is given. Simulation results show that the relative error for linear and nonlinear programming is substantially reduced by the new method.

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