Chinese Journal of Intelligent Science and Technology ›› 2019, Vol. 1 ›› Issue (4): 319-326.doi: 10.11959/j.issn.2096-6652.201936

• Regular Papers •     Next Articles

Variation and learning:fuzzy system and fuzzy inference

GARIBALDI Jonathan M1,Hongyu CHEN2(),Xiaoshuang LI2   

  1. 1 School of Computer Science,University of Nottingham,Nottinghamshire NG8 1BB,UK
    2 The State Key Laboratory for Management and Control of Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
  • Revised:2019-11-22 Online:2019-12-20 Published:2020-02-29


As a decision support system,fuzzy system can deal with uncertainty and has a clear representation of uncertainty knowledge and inference process.But one problem that exists is that computerized decision support systems,including systems that use fuzzy methods,do not have a clear assessment method to determine whether they can be allowed to be used in the real world.A conceptual framework of indistinguishable lines as a key component in evaluating computerized decision support systems was proposed,and some case studies were given.The case proves that the performance of human experts is not perfect,and the fuzzy system can simulate human performance at the technical level,including the variation of human experts.In summary,fuzzy methods are necessary for the representation and reasoning of uncertainty of the knowledge-based systems.Variation is an important form of learning.When evaluating AI systems,imperfect performance should be accepted.

Key words: artificial intelligence, approximate reasoning, uzzy inference system, fuzzy set, human reasoning

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