BETA NEURO-FUZZY SYSTEMS

In this paper we present the Beta function and its main properties. A key feature of the Beta function, which is given by the central limit theorem, is also shown. We then introduce a new category of neural networks based on a new kernel: the Beta function. Next, we investigate the use of Beta fuzz...

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Main Author: ADEL M. ALIMI
Format: Article
Language:English
Published: Gdańsk University of Technology 2003-01-01
Series:TASK Quarterly
Subjects:
Online Access:https://journal.mostwiedzy.pl/TASKQuarterly/article/view/2209
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author ADEL M. ALIMI
author_facet ADEL M. ALIMI
author_sort ADEL M. ALIMI
collection DOAJ
description In this paper we present the Beta function and its main properties. A key feature of the Beta function, which is given by the central limit theorem, is also shown. We then introduce a new category of neural networks based on a new kernel: the Beta function. Next, we investigate the use of Beta fuzzy basis functions for the design of fuzzy logic systems. The functional equivalence between Beta-based function neural networks and Beta fuzzy logic systems is then shown with the introduction of Beta neuro-fuzzy systems. By using the Stone-Weierstrass theorem and expanding the output of the Beta neuro-fuzzy system into a series of Beta fuzzy-based functions, we prove that one can uniformly approximate any real continuous function on a compact set to any arbitrary accuracy. Finally, a learning algorithm of the Beta neuro-fuzzy system is described and illustrated with numerical examples.
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spelling doaj.art-fb79c1eb48c944d5a1202a67ed5e5faf2022-12-22T02:52:57ZengGdańsk University of TechnologyTASK Quarterly1428-63942003-01-0171BETA NEURO-FUZZY SYSTEMSADEL M. ALIMI0University of Sfax, Research Group on Intelligent Machines, National School of Engineers ENIS, Department of Electrical Engineering In this paper we present the Beta function and its main properties. A key feature of the Beta function, which is given by the central limit theorem, is also shown. We then introduce a new category of neural networks based on a new kernel: the Beta function. Next, we investigate the use of Beta fuzzy basis functions for the design of fuzzy logic systems. The functional equivalence between Beta-based function neural networks and Beta fuzzy logic systems is then shown with the introduction of Beta neuro-fuzzy systems. By using the Stone-Weierstrass theorem and expanding the output of the Beta neuro-fuzzy system into a series of Beta fuzzy-based functions, we prove that one can uniformly approximate any real continuous function on a compact set to any arbitrary accuracy. Finally, a learning algorithm of the Beta neuro-fuzzy system is described and illustrated with numerical examples. https://journal.mostwiedzy.pl/TASKQuarterly/article/view/2209beta functionkernel based neural networksSugeno fuzzy modelneuro-fuzzy systemsuniversal approximation propertylearning algorithms
spellingShingle ADEL M. ALIMI
BETA NEURO-FUZZY SYSTEMS
TASK Quarterly
beta function
kernel based neural networks
Sugeno fuzzy model
neuro-fuzzy systems
universal approximation property
learning algorithms
title BETA NEURO-FUZZY SYSTEMS
title_full BETA NEURO-FUZZY SYSTEMS
title_fullStr BETA NEURO-FUZZY SYSTEMS
title_full_unstemmed BETA NEURO-FUZZY SYSTEMS
title_short BETA NEURO-FUZZY SYSTEMS
title_sort beta neuro fuzzy systems
topic beta function
kernel based neural networks
Sugeno fuzzy model
neuro-fuzzy systems
universal approximation property
learning algorithms
url https://journal.mostwiedzy.pl/TASKQuarterly/article/view/2209
work_keys_str_mv AT adelmalimi betaneurofuzzysystems