Approximation and control of systems using a neural net

Author

Ermish, M. ; Nouri-Moghadam, M.

Author_Institution

Dept. of Math., Penn State Univ., PA, USA

fYear

1993

fDate

7-9 Mar 1993

Firstpage

594

Lastpage

598

Abstract

The use of multilayer neural networks for approximating linear and nonlinear systems is demonstrated. The gradient method is discussed in detail. Single-input/single-output and double-input/single-output systems are considered, and gradient (backpropagation) methods are used to adjust the parameters of a three-layer neural network in order to optimize a performance function. The results of simulations for the above systems are analyzed, and appropriate graphs that verify the theoretical results are included. The approach is also used for approximating the optimal control of vibrating beams

Keywords

approximation theory; backpropagation; discrete systems; multilayer perceptrons; network parameters; neurocontrollers; nonlinear control systems; optimal control; simulation; vibration control; backpropagation; gradient method; multilayer neural networks; optimal control; performance function; simulations; vibrating beams; Analytical models; Backpropagation; Control systems; Gradient methods; Linear approximation; Multi-layer neural network; Neural networks; Nonlinear systems; Optimal control; Optimization methods;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, 1993. Proceedings SSST '93., Twenty-Fifth Southeastern Symposium on

Conference_Location

Tuscaloosa, AL

ISSN

0094-2898

Print_ISBN

0-8186-3560-6

Type

conf

DOI

10.1109/SSST.1993.526520

Filename

526520