A fast learning algorithm based on extreme learning machine for regular fuzzy neural network

Issue title: Special Section: Similarity, correlation and association measures - dedicated to the memory of Lotfi Zadeh

Guest editors: Ildar Batyrshin, Valerie Cross, Vladik Kreinovich and Maria Rifqi

Article type: Research Article

Authors: He, Chunmei^{a; b; *} | Liu, Yaqi^a | Yao, Tong^a | Xu, Fanhua^a | Hu, Yanyun^a | Zheng, Jinhua^a

Affiliations: [a] College of Information Engineering, Xiangtan University, Xiangtan, China | [b] College of Computer Science and Engineering, NUST, Nanjing, China

Correspondence: [*] Corresponding author. Chunmei He, College of Information Engineering, Xiangtan University, Xiangtan 411105, China. Email: [email protected].

Abstract: The regular fuzzy neural network (RFNN) is a kind of fuzzy neural network by fuzzifying the feed-forward neural network. The RFNN can directly deal with the language information and it has the merits of fuzzy system and neural network. It is presented a fast learning algorithm based on the extreme learning machine (ELM) for the RFNN in this paper. The RFNN referred here is a three-layer feed-forward fuzzy neural network and the connected weights in the RFNN are all fuzzy numbers. A simulation example is given to approximately realize the fuzzy if-then rules by the RFNN. The results show that the RFNN trained by the proposed algorithm has good performance and approximation ability.

Keywords: Regular fuzzy neural network, learning algorithm, extreme learning machine

DOI: 10.3233/JIFS-18046

Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3263-3269, 2019