A chemical reactor benchmark for parallel adaptive control using feedforward neural networks

Author

Cajueiro, Daniel Oliveira ; Hemerly, Elder Moreira

Author_Institution

Inst. Tecnologico de Aeronautica, Sao Jose dos Campos, Brazil

fYear

2000

fDate

2000

Firstpage

44

Lastpage

49

Abstract

This paper applies a parallel scheme for adaptive control that uses only one neural network to a CSTR (continuous stirred tank reactor). Convergence of the identification error is investigated by Lyapunov´s second method. The training process of the neural network is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm

Keywords

Kalman filters; Lyapunov methods; adaptive control; backpropagation; chemical technology; convergence; feedforward neural nets; filtering theory; identification; neurocontrollers; process control; CSTR; Lyapunov second method; adaptive control; backpropagation; chemical reactor benchmark; continuous stirred tank reactor; extended Kalman filter algorithm; feedforward neural networks; identification error convergence; neural network training; parallel adaptive control; Adaptive control; Backpropagation algorithms; Chemical reactors; Continuous-stirred tank reactor; Feedforward neural networks; Network topology; Neural networks; Recurrent neural networks; Testing; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. Proceedings. Sixth Brazilian Symposium on

Conference_Location

Rio de Janeiro, RJ

ISSN

1522-4899

Print_ISBN

0-7695-0856-1

Type

conf

DOI

10.1109/SBRN.2000.889711

Filename

889711