Affiliations: [a]
Department of Computer Engineering, Near East University, Mersin-10, Lefkosa, North Cyprus, Turkey
| [b] Joint MBA Program, Georgia State University, USA, Azerbaijan State Oil and Industry University, 20 Azadliq ave., Baku, Azerbaijan
| [c]
Department of Electrical and Electronics Engineering, Bogazici University, Bebek, Istanbul, Turkey
Correspondence:
[*]
Corresponding author. Rahib H. Abiyev, Department of Computer Engineering, Near East University, Mersin-10, Lefkosa, North Cyprus, Turkey. E-mail: rahib.abiyev@neu.edu.tr.
Abstract: 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.
