Process Control

Design of the Neural Model Structure Based on Genetic Algorithms

I. Sekaj
Slovak University of Technology in Bratislava

Abstract

Genetic algorithm based neural model structure design of a non-linear dynamic system is described. The genetic algorithm represents an optimisation procedure, where the cost function, which is minimized consists of the non-linear dynamic process neural model simulation and a selected performance index evaluation. Using this approach the neural model of the process has been optimised from point of view its internal architecture. A multilayer perceptron (MLP) artificial neural network has been used, where the training was realized by the Levenberg-Marquardt method.

Full paper

043.pdf

Session

Intelligent Control Systems (Poster)

Reference

Sekaj, I.: Design of the Neural Model Structure Based on Genetic Algorithms. Editors: Fikar, M., Kvasnica, M., In Proceedings of the 17th International Conference on Process Control ’09, Štrbské Pleso, Slovakia, 273–276, 2009

BibTeX
@inProceedings{pc09-043,
author = {Sekaj, I.},
title = {Design of the Neural Model Structure Based on Genetic Algorithms},
booktitle = {Proceedings of the 17th International Conference on Process Control '09},
year = {2009},
pages = {273-276},
editor = {Fikar, M. and Kvasnica, M.},
address = {Štrbské Pleso, Slovakia},
publisher = {Slovak University of Technology in Bratislava},
url = {http://www.kirp.chtf.stuba.sk/pc09/data/papers/043.pdf}}
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