[1]CHEN Dewang,WU Yiran,OU Jixiang,et al.Development and prospects of third-generation fuzzy systems for next-generation artificial intelligence[J].CAAI Transactions on Intelligent Systems,2026,21(3):566-576.[doi:10.11992/tis.202506002]
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Development and prospects of third-generation fuzzy systems for next-generation artificial intelligence

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 3 Page number: 566-576 Column: 综述 Public date: 2026-05-05
Title:
Development and prospects of third-generation fuzzy systems for next-generation artificial intelligence
Author(s):
CHEN Dewang1; 2;  WU Yiran1;  OU Jixiang3;  SHEN Zhen4;  LI Lingxi5;  XIONG Gang1; 4
1. School of Transportation, Fujian University of Technology, Fuzhou 350118, China;
2. School of Electronics and Information Engineering, West Anhui University, Lu’an 237012, China;
3. School of Information Engineering, Yango University, Fuzhou 350015, China;
4. State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
5. Department of Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis, Indianapolis IN46204, America
Keywords:
next-generation artificial intelligence; fuzzy systems; deep learning; optimization; big data; neural networks; rule-based systems; interpretablity
CLC:
TP39
DOI:
10.11992/tis.202506002
Abstract:
The Grand Research Initiative for Explainable and Universally Applicable Next-Generation Artificial Intelligence (AI) Methods, aligned with national strategic priorities for AI development, focuses on fundamental scientific challenges in AI. This initiative aims to develop a new generation of AI methodologies, addressing the lack of interpretability often associated with neural networks. Compared with neural network algorithms whose internal mechanisms are difficult to explain, fuzzy systems offer strong interpretability and robustness and play an important role in modeling and decision-making under uncertainty. The development of fuzzy systems has progressed through three stages: traditional fuzzy systems based on expert experience, adaptive fuzzy systems for low-dimensional small data, and deep optimization fuzzy systems for high-dimensional big data. Current research focuses on the third-generation fuzzy systems, that is, deep optimization fuzzy systems. Through hierarchical architecture, adaptive rule generation mechanisms, and deep integration with optimization techniques such as evolutionary algorithms and gradient descent, these systems can automatically extract effective features and latent rules from massive high-dimensional data and achieve feature dimensionality reduction and reconstruction through layer-by-layer abstraction. Consequently, deep optimization fuzzy systems effectively mitigate the “rule explosion” problem encountered by traditional fuzzy systems in high-dimensional settings, significantly improve computational efficiency and generalization capability, and achieve a balance between interpretability and high accuracy. This is expected to open up a new direction for the future development of artificial intelligence.
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