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Deep fuzzy clustering inference network and its application to non-destructively estimating strength of cement microstructure
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Resumo(s)
A 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.
Descrição
Wu, 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)
Palavras-chave
Inference Neural Network Fuzzy Neural Network Cascade Learning Method Deep Structure-based Neural Network Computer Science Applications Cognitive Neuroscience Artificial Intelligence