Recurrent neural control of a continuous bioprocess using first and second order learning

Carlos Román Mariaca-Gaspar, Julio César Tovar Rodríguez, Floriberto Ortiz-Rodríguez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The propose of this paper is to introduce a new Kalman Filter based in a Recurrent Neural Network topology (KFRNN) and a recursive Levenberg-Marquardt (L-M) algorithm. Such algorithm is able to estimate the states and parameters of a highly nonlinear continuous fermentation bioprocess in noisy environment. The control scheme is direct adaptive and also contains feedback and feedforward recurrent neural controllers. The proposed control scheme is applied for real-time identification and control of continuous stirred tank bioreactor model, taken from the literature, where a fast convergence, noise filtering and low mean squared error of reference tracking were achieved.

Idioma originalInglés
Título de la publicación alojadaAdvances in Artificial Intelligence - 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, Revised Selected Papers
Páginas211-222
Número de páginas12
EdiciónPART 2
DOI
EstadoPublicada - 2013
Evento11th Mexican International Conference on Artificial Intelligence, MICAI 2012 - San Luis Potosi, México
Duración: 27 oct. 20124 nov. 2012

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NúmeroPART 2
Volumen7630 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia11th Mexican International Conference on Artificial Intelligence, MICAI 2012
País/TerritorioMéxico
CiudadSan Luis Potosi
Período27/10/124/11/12

Huella

Profundice en los temas de investigación de 'Recurrent neural control of a continuous bioprocess using first and second order learning'. En conjunto forman una huella única.

Citar esto