DocumentCode :
488632
Title :
A Neural Network Approach for Identification of Continuous-Time Nonlinear Dynamic Systems
Author :
Chu, S.Reynold ; Shoureshi, Rahmat
Author_Institution :
Graduate Research Assistant, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907
fYear :
1991
fDate :
26-28 June 1991
Firstpage :
1
Lastpage :
5
Abstract :
In this paper, a neural network approach for identifying continuous time nonlinear dynamic systems is presented. The nonlinear dynamic system may be described by a state space model or represented by an input-output relationship. The concept of state-variable filter is employed such that no derivatives of the output or input are required. The weight adjustments are based on a gradient algorithm and can be carried out by a bank of parallel analog filters.
Keywords :
Backpropagation algorithms; Feedforward neural networks; Filters; Intelligent networks; Jacobian matrices; Neural networks; Neurofeedback; Neurons; Nonlinear dynamical systems; Nonlinear equations;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference, 1991
Conference_Location :
Boston, MA, USA
Print_ISBN :
0-87942-565-2
Type :
conf
Filename :
4791308
Link To Document :
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