Adaptive Nonstationary Fuzzy Neural Network

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dc.authoridWang, Jian/0000-0002-4316-932X
dc.contributor.authorChang, Qin
dc.contributor.authorZhang, Zhen
dc.contributor.authorWei, Fanyue
dc.contributor.authorWang, Jian
dc.contributor.authorPedrycz, Witold
dc.contributor.authorPal, Nikhil R.
dc.date.accessioned2024-05-19T14:46:01Z
dc.date.available2024-05-19T14:46:01Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractFuzzy neural network (FNN) plays an important role as an inference system in practical applications. To enhance its ability of handling uncertainty without invoking high computational cost, and to take variations in rules into consideration as well, we propose a new inference framework-nonstationary fuzzy neural network (NFNN). This NFNN is composed of a series of zero -order TSK FNNs with the same structure but using slightly perturbed fuzzy sets in the corresponding neurons, which is inspired from the non -stationary fuzzy sets and can mimic the variation in human's decision -making process. In order to obtain a concise and adaptive rule base for NFNN, a modified affinity propagation (MAP) clustering method is proposed. The MAP can determine the number of rules in an adaptive manner, and is used to initialize the rule parameters of NFNN, which we call Adaptive NFNN (ANFNN). Numerical experiments have been carried out over 17 classification datasets and three regression datasets. The experimental results demonstrate that ANFNN exhibits better accuracy, generalization ability, and fault -tolerance ability compared with the classical type -1 fuzzy neural network. In 15 of the 17 classification datasets, ANFNN achieves the same or better accuracy performance compared to interval type -2 FNNs with about half time consumed. This work confirms the feasibility of integrating simplestructured type -1 TSK FNNs to achieve the performance of interval type -2 FNNs, and proves that ANFNN can be a more accurate and reliable alternative to classical type -1 FNN.en_US
dc.identifier.doi10.1016/j.knosys.2024.111398
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.scopus2-s2.0-85184517760en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.knosys.2024.111398
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5422
dc.identifier.volume288en_US
dc.identifier.wosWOS:001180577500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectNonstationary Fuzzy Seten_US
dc.subjectNonstationary Fuzzy Neural Networken_US
dc.subjectFuzzy Neural Networken_US
dc.subjectClusteringen_US
dc.subjectConvergenceen_US
dc.titleAdaptive Nonstationary Fuzzy Neural Networken_US
dc.typeArticleen_US

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