Z-number based fuzzy neural network for system identification

In this paper, a novel Z-number based Fuzzy Neural Network (Z-FNN) based on the integration of Z-valued fuzzy logic and neural networks is proposed. Z-valued fuzzy rule base is presented and its inference process is described using interpolative approximate reasoning. Accordingly, the structure of t...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2023-12, Vol.45 (6), p.11203-11216
Main Authors: Abiyev, Rahib H., Aliev, Rafik, Kaynak, Okyay
Format: Article
Language:English
Subjects:
Artificial neural networks
Fuzzy logic
Fuzzy systems
Genetic algorithms
Machine learning
Nonlinear systems
Reasoning
System identification
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Summary:In this paper, a novel Z-number based Fuzzy Neural Network (Z-FNN) based on the integration of Z-valued fuzzy logic and neural networks is proposed. Z-valued fuzzy rule base is presented and its inference process is described using interpolative approximate reasoning. Accordingly, the structure of the Z-FNN is proposed using a distance measure and interpolative approximate reasoning scheme. Based on presented architecture the learning algorithm of Z-FNN is designed. The updating of the unknown parameters of the network is carried out using Genetic Algorithms (GA). The proposed Z-FNN system is utilized for dynamic plant identification. The effectiveness of Z-FNN has been tested by comparing its performance with the performances of other fuzzy systems available in the literature. The proposed approach has been proven to be a suitable alternative for the identification of nonlinear systems characterized by uncertain and imprecise information.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-232741