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Volume 13 Issue 8
Aug.  2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  >  13(8): 1865-1882
S. Wang, K. Wang, S. Zhang, and C. Yang, “Uncertainty explicit learning: An interval generalized neural network with adaptivity for anomaly identification under data uncertainty,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 8, pp. 1865–1882, Aug. 2026. doi: 10.1109/JAS.2025.125939
Citation: S. Wang, K. Wang, S. Zhang, and C. Yang, “Uncertainty explicit learning: An interval generalized neural network with adaptivity for anomaly identification under data uncertainty,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 8, pp. 1865–1882, Aug. 2026. doi: 10.1109/JAS.2025.125939
shu

Uncertainty Explicit Learning: An Interval Generalized Neural Network With Adaptivity for Anomaly Identification Under Data Uncertainty

doi: 10.1109/JAS.2025.125939
Funds:  This work was supported in part by the National Natural Science Foundation of China (62373277, 62394340, 62373378, 62373271), the Hunan Young Talents in Science and Technology Innovation (2024RC3028), and the Natural Science Foundation of Tianjin (23JCZDJC01140)
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    Abstract
  • In real industrial processes, the collected process data often suffer from measurement uncertainty due to noise interference, sensor drift, and harsh operating environment. The existence of uncertainty may undermine the correlation structure of original data, resulting in the failure of traditional machine learning methods in anomaly identification. In this study, an interval generalized radial basis function neural network (IGRBFNN) based on key fault feature extraction is proposed for abnormal condition identification under data uncertainty. Specifically, kernel density estimation (KDE) is first introduced to explicitly characterize the uncertainty-contaminated data in interval form. For interval process data, an interval linear discriminant analysis (ILDA) method is developed to maximize the projection distance between different fault categories and minimize the projection distance within the same fault category, thus achieving key fault feature extraction under the mask of uncertainty. Subsequently, by embedding a generalized Gaussian function with adaptive characteristics and combining the mathematical theory of interval analysis, an IGRBFNN model is further constructed to enhance the generalization ability of traditional neural networks. Finally, motivated by autonomous clustering, an interval generalized self-organizing map (IGSOM) network is developed to intelligently learn the model parameters of IGRBFNN. Extensive experiments on two cases demonstrate the flexibility and applicability of the constructed anomaly identification model.

     

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