Title :
Recognition of stability domain of nonlinear dynamical system using multilayer feed-forward artificial neural network approach
Author_Institution :
Dept. of Electr. Eng., Tennessee State Univ., Nashville, TN, USA
Abstract :
A method for obtaining the stability region of a nonlinear dynamical system using an artificial neural network is presented. This method takes advantage of many properties of adaptive networks. A design philosophy is presented. A Rumelhart feed-forward neural network with backward propagation of error algorithm is used to update the synaptic weights and thresholds until the desired stability region is recognized. An example of a nonlinear system is presented to illustrate the method
Keywords :
control system analysis; neural nets; nonlinear control systems; stability criteria; Rumelhart feed-forward neural network; back-propagation; backward propagation; multilayer feed-forward artificial neural network; nonlinear dynamical system; stability domain recognition; synaptic weight updating; threshold updating; Artificial neural networks; Computer networks; Feedforward systems; Lyapunov method; Multi-layer neural network; Nonhomogeneous media; Nonlinear dynamical systems; Nonlinear systems; Stability; Testing;
Conference_Titel :
System Theory, 1991. Proceedings., Twenty-Third Southeastern Symposium on
Conference_Location :
Columbia, SC
Print_ISBN :
0-8186-2190-7
DOI :
10.1109/SSST.1991.138550
