Fuzzy Adaptive Knowledge-Based Inference Neural Networks: Design and Analysis

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dc.authoridSeo, Kisung/0000-0002-5256-0582
dc.contributor.authorLiu, Shuangrong
dc.contributor.authorOh, Sung-Kwun
dc.contributor.authorPedrycz, Witold
dc.contributor.authorYang, Bo
dc.contributor.authorWang, Lin
dc.contributor.authorSeo, Kisung
dc.date.accessioned2024-05-19T14:41:44Z
dc.date.available2024-05-19T14:41:44Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractA novel fuzzy adaptive knowledge-based inference neural network (FAKINN) is proposed in this study. Conventional fuzzy cluster-based neural networks (FCBNNs) suffer from the challenge of a direct extraction of fuzzy rules that can capture and represent the interclass heterogeneity and intraclass homogeneity when the data possess complex structures. Moreover, the capability of the cluster-based rule generator in FCBNNs may decrease with the increase of data dimensionality. These drawbacks impede the generation of desired fuzzy rules, and affect the inference results depending on the fuzzy rules, thereby limiting their generalization ability. To address these drawbacks, an adaptive knowledge generator (AKG), consisting of the observation paradigm (OP) and clustering strategy (CS), is effectively designed to improve the generalization ability in FAKINN. The OP distills the characteristic information (CI) from data to highlight the homogeneity and heterogeneity of objects, and the CS, viz., the weighted condition-driven fuzzy clustering method (WCFCM), is proposed to summarize the CI to construct fuzzy rules. Moreover, the feedback between the OP and CS can control the dimensionality of CI, which endows FAKINN with the potential to tackle high-dimensional data. The main originality of the study focuses on the AKG and WCFCM that are proposed to develop the structural design methodology of FNNs. The performance of FAKINN is evaluated on various benchmarks with 27 comparative methods, and two real-world problems are adopted to validate its effectiveness. Experimental results show that FAKINN outperforms the comparison methods.en_US
dc.description.sponsorshipKorea Government (MSIT)en_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1109/TCYB.2024.3353753
dc.identifier.issn2168-2267
dc.identifier.issn2168-2275
dc.identifier.pmid38416627en_US
dc.identifier.urihttps://doi.org10.1109/TCYB.2024.3353753
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5151
dc.identifier.wosWOS:001179004300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Cyberneticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectFuzzy Neural Networksen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectKnowledge Based Systemsen_US
dc.subjectLinguisticsen_US
dc.subjectRobustnessen_US
dc.subjectFeature Extractionen_US
dc.subjectRadial Basis Function Networksen_US
dc.subjectFuzzy Adaptive Knowledge Baseen_US
dc.subjectFuzzy Clusteringen_US
dc.subjectFuzzy Neural Network (Fnn)en_US
dc.subjectObserveren_US
dc.subjectSuccessive Learningen_US
dc.titleFuzzy Adaptive Knowledge-Based Inference Neural Networks: Design and Analysisen_US
dc.typeArticleen_US

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