A genetic based neuro-fuzzy controller for thermal processes

This paper presents a neuro-fuzzy network where all its parameters can be tuned simultaneously using Genetic Algorithms. The approach combines the merits of fuzzy logic theory, neural networks and genetic algorithms. The proposed neuro-fuzzy network does not require a priori knowledge about the syst...

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Autores principales: Goel, Ashok Kumar, Saxena Chandra, Suresh, Bhanot, Surekha
Formato: Articulo
Lenguaje:Inglés
Publicado: 2005
Materias:
Ciencias Informáticas
Neural nets
Algorithms
fuzzy logic
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/9507
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr05-7.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-9507
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Neural nets
Algorithms
fuzzy logic
spellingShingle Ciencias Informáticas
Neural nets
Algorithms
fuzzy logic
Goel, Ashok Kumar
Saxena Chandra, Suresh
Bhanot, Surekha
A genetic based neuro-fuzzy controller for thermal processes
topic_facet Ciencias Informáticas
Neural nets
Algorithms
fuzzy logic
description This paper presents a neuro-fuzzy network where all its parameters can be tuned simultaneously using Genetic Algorithms. The approach combines the merits of fuzzy logic theory, neural networks and genetic algorithms. The proposed neuro-fuzzy network does not require a priori knowledge about the system and eliminates the need for complicated design steps like manual tuning of input-output membership functions, and selection of fuzzy rule base. Although, only conventional genetic algorithms have been used, convergence results are very encouraging. A well known numerical example derived from literature is used to evaluate and compare the performance of the network with other modelling approaches. The network is further implemented as controller for two simulated thermal processes and their performances are compared with other existing controllers. Simulation results show that the proposed neuro-fuzzy controller whose all parameters have been tuned simultaneously using GAs, offers advantages over existing controllers and has improved performance.
format Articulo
Articulo
author Goel, Ashok Kumar
Saxena Chandra, Suresh
Bhanot, Surekha
author_facet Goel, Ashok Kumar
Saxena Chandra, Suresh
Bhanot, Surekha
author_sort Goel, Ashok Kumar
title A genetic based neuro-fuzzy controller for thermal processes
title_short A genetic based neuro-fuzzy controller for thermal processes
title_full A genetic based neuro-fuzzy controller for thermal processes
title_fullStr A genetic based neuro-fuzzy controller for thermal processes
title_full_unstemmed A genetic based neuro-fuzzy controller for thermal processes
title_sort genetic based neuro-fuzzy controller for thermal processes
publishDate 2005
url http://sedici.unlp.edu.ar/handle/10915/9507
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr05-7.pdf
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AT goelashokkumar geneticbasedneurofuzzycontrollerforthermalprocesses
AT saxenachandrasuresh geneticbasedneurofuzzycontrollerforthermalprocesses
AT bhanotsurekha geneticbasedneurofuzzycontrollerforthermalprocesses
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