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Deep fuzzy clustering inference network and its application to non-destructively estimating strength of cement microstructure

2026-04-14Artigo científico Acesso embargado
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dc.contributor.authorWu, Xu
dc.contributor.authorLiu, Shuangrong
dc.contributor.authorWang, Wenwei
dc.contributor.authorWang, Lin
dc.contributor.authorDeng, Xinbo
dc.contributor.authorLiu, Cong
dc.contributor.authorYang, Bo
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblElsevier Science B.V., Amsterdam.
dc.date.accessioned2026-02-11T13:33:01Z
dc.date.available2026-02-11T13:33:01Z
dc.date.embargoedUntil2028-01-30
dc.date.issued2026-04-14
dc.descriptionWu, X., Liu, S., Wang, W., Wang, L., Deng, X., Liu, C., & Yang, B. (2026). Deep fuzzy clustering inference network and its application to non-destructively estimating strength of cement microstructure. Neurocomputing, 674, Article 132900. https://doi.org/10.1016/j.neucom.2026.132900 --- This work was supported by National Natural Science Foundation of China under Grant No. 61872419, No. 62072213, No. 62403209. Shandong Provincial Natural Science Foundation No. ZR2022JQ30, No. ZR2022ZD01, No. ZR2023LZH015, No. ZR2024QF021. Key Research and Development Program of Shandong Province under Grant No. 2024CXPT084, No. 2025CXPT105. Key Research Project of Quancheng Laboratory, China under Grant No. QCL20250105, No. QCL20250304, No. QCLZD202303. Open Foundation of the State Key Laboratory of Silicate Materials for Architectures under Grant SYSJJ2025-02. High-level Innovation Platforms in Jinan under Grant No. 202534127. Youth Innovation Team Program of Shandong Higher Education Institutions, China under Grant No. 2024KJH104. University of Jinan Young Faculty Interdisciplinary Convergence Development Project 2025 under Grant No. XKJC-202507. “New 20 Rules for University” Program of Jinan City under Grant No. 2021GXRC077. University of Jinan Disciplinary Cross-Convergence Construction Project 2024(XKJC-202402)
dc.description.abstractA novel deep fuzzy clustering-based inference neural network (DFCINN) is proposed to develop the design methodology of the fuzzy clustering-based neural networks (FCNNs). The conventional FCNNs, while offering solutions to the rule explosion problem in neuro-fuzzy models through high-level information granularity, often falter in generalization with data of complex structures or high dimensionality. This limitation stems from their clustering-based rule generation strategy, which struggles to produce the expected rule base that has potential to establish distinct boundaries for decision-making among different classes under such conditions. To overcome these challenges, the DFCINN integrates a deep structural framework with a fuzzy clustering strategy, effectively capturing both inter-class heterogeneity and intra-class homogeneity, which aids in constructing the desired rule base. Moreover, the cascade learning method is developed to train the parameters of the fuzzy rules, considering both clustering-based and classification losses. The performance of the DFCINN is assessed using the datasets with varying characteristics and its results are compared with those of various other methods. Moreover, the DFCINN is applied to non-destructively estimate the strength grade of cement microstructure. Experimental results reveal that the performance of the DFCINN is superior to that of its competitors.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent13
dc.format.extent1019092
dc.identifier.doi10.1016/j.neucom.2026.132900
dc.identifier.issn0925-2312
dc.identifier.otherPURE: 151527970
dc.identifier.otherPURE UUID: b4cffbed-16bd-49e3-8e9b-e34142bc4817
dc.identifier.otherScopus: 105029381594
dc.identifier.otherWOS: 001689014100001
dc.identifier.urihttp://hdl.handle.net/10362/200285
dc.identifier.urlhttps://www.scopus.com/pages/publications/105029381594
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001689014100001
dc.language.isoeng
dc.peerreviewedyes
dc.subjectInference Neural Network
dc.subjectFuzzy Neural Network
dc.subjectCascade Learning Method
dc.subjectDeep Structure-based Neural Network
dc.subjectComputer Science Applications
dc.subjectCognitive Neuroscience
dc.subjectArtificial Intelligence
dc.titleDeep fuzzy clustering inference network and its application to non-destructively estimating strength of cement microstructureen
dc.typejournal article
degois.publication.titleNeurocomputing
degois.publication.volume674
dspace.entity.typePublication
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