Title :
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
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;
Conference_Titel :
Neural Networks, 2000. Proceedings. Sixth Brazilian Symposium on
Conference_Location :
Rio de Janeiro, RJ
Print_ISBN :
0-7695-0856-1
DOI :
10.1109/SBRN.2000.889711
