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Title: Neuro-fuzzy modelling based on a deterministic annealing approach

Creator:

Czabański, Robert

Date:

2005

Resource Type:

artykuł

Contributor:

Beliczyński, Bartłomiej - red.

Subtitle:

Intelligent Control and Signal Processing

Group publication title:

AMCS, Volume 15 (2005)

Abstract:

This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR.It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-thenrules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimationof unknown system parameters was made by means of a combination of both gradient and least-squares methods. Thenovelty of the learning algorithm consists in the application of a deterministic annealing optimization method. It leads to animprovement in the neuro-fuzzy modelling performance. To show the validity of the introduced method, two examples ofapplication concerning chaotic time series prediction and system identification problems are provided.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:46816

Pages:

561-576

Source:

AMCS, Volume 15, Number 4 (2005) ; click here to follow the link

Language:

eng

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Subject and keywords:

fuzzy systems neural networks neuro-fuzzy systems rules extraction deterministic annealing prediction

Object collections:

Last modified:

Sep 7, 2021

In our library since:

Aug 9, 2018

Number of object content hits:

115

All available object's versions:

https://www.zbc.uz.zgora.pl/publication/54905

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Neuro-fuzzy modelling based on a deterministic annealing approach Sep 7, 2021

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