[1]CHEN Dewang,WANG Rui,KONG Lingkun,et al.Third-generation artificial intelligence based on fuzzy systems[J].CAAI Transactions on Intelligent Systems,2025,20(5):1071-1081.[doi:10.11992/tis.202407011]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
20
Number of periods:
2025 5
Page number:
1071-1081
Column:
综述
Public date:
2025-09-05
- Title:
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Third-generation artificial intelligence based on fuzzy systems
- Author(s):
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CHEN Dewang1; WANG Rui1; KONG Lingkun2; HAN Zeming1
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1. School of Transportation, Fujian University of Technology, Fuzhou 350118, China;
2. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
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- Keywords:
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third-generation artificial intelligence; fuzzy systems; three-space fusion model; knowledge-driven; data-driven; universal approximation; fuzzy expert systems; data-driven fuzzy system identification
- CLC:
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TP39
- DOI:
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10.11992/tis.202407011
- Abstract:
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Artificial Intelligence (AI) has undergone two stages of development. The first-generation AI relied on knowledge-driven algorithmic systems focused on control logic, while the second-generation AI shifted toward data-driven systems emphasizing data learning to simulate the biological intelligence of human beings. Both approaches have their strengths. However, they face limitations such as limited computational power and issues with interpretability. To address these shortcomings, third-generation AI theories and methodologies aim to develop systems that are noise-resistant, robust, and interpretable. This paper discusses the modeling methods for third-generation AI, which are based on integrating knowledge-driven and data-driven approaches. It also examines the potential of combining third-generation AI with fuzzy systems. By leveraging the robust and interpretable characteristics of fuzzy systems, the advancement of third-generation AI can be further accelerated. The insights presented offer a valuable reference for shaping the future development of third-generation AI.