Variation and learning:fuzzy system and fuzzy inference

GARIBALDI Jonathan M; Hongyu CHEN; Xiaoshuang LI

doi:10.11959/j.issn.2096-6652.201936

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Variation and learning:fuzzy system and fuzzy inference

Regular Papers | 更新时间：2024-06-05

- Variation and learning:fuzzy system and fuzzy inference
- Chinese Journal of Intelligent Science and Technology Vol. 1, Issue 4, Pages: 319-326(2019)
- 作者机构：
  
  1. 诺丁汉大学计算机科学学院，英国诺丁汉郡 NG8 1BB
  2. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.201936
  CLC： TP18
- Published Online：2019-12，
  
  Published：20 December 2019
- 稿件说明：
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GARIBALDI Jonathan M, Hongyu CHEN, Xiaoshuang LI. Variation and learning:fuzzy system and fuzzy inference[J]. Chinese journal of intelligent science and technology, 2019, 1(4): 319-326.
DOI：

GARIBALDI Jonathan M, Hongyu CHEN, Xiaoshuang LI. Variation and learning:fuzzy system and fuzzy inference[J]. Chinese journal of intelligent science and technology, 2019, 1(4): 319-326. DOI： 10.11959/j.issn.2096-6652.201936.

摘要

作为一种决策支持系统，模糊系统不仅具有处理不确定性信息的能力，又能够明确表达不确定性知识和推理过程。但现存的一个问题是，对于包括采用模糊方法的系统在内的计算机决策支持系统，目前还未出现能够明确评估系统实际可行性的方法。提出了不可区分性的概念框架，并将其作为评估计算机决策支持系统的关键部分，给出了相关案例研究。案例证明人类专家的评判并非完美，模糊系统能够在技术层面模拟人类的决策，包括人类专家在评判时表现出的差异性。使用模糊方法进行基于知识不确定性的表达与推理是非常必要的，而差异则是学习时不可避免的表现形式，在评估人工智能系统时应接受其不完美的决策。

Abstract

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.

关键词

Keywords

references

GARIBALDI J M . The need for fuzzy AI [J ] . IEEE/CAA Journal of Automatica Sinica , 2019 , 6 ( 3 ): 610 - 622 .

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TURING A M . Computing machinery and intelligence [J ] . Mind , 1950 , 59 ( 236 ): 433 - 460 .

KEITH R D , BECKLEY S , GARIBALDI J M , et al . A multicentre comparative study of 17 experts and an intelligent computer system for managing labour using the cardiotocogram [J ] . British Journal of Obstetrics and Gynaecology , 1995 , 102 ( 9 ): 688 - 700 .

GARIBALDI J M , OZEN T . Uncertain fuzzy reasoning:a case study in modelling expert decision making [J ] . IEEE Transactions on Fuzzy Systems , 2007 , 15 ( 1 ): 16 - 30 .

ZADEH L A . The concept of a linguistic variable and its application to approximate reasoning [J ] . Information Sciences , 1975 , 8 ( 8 ): 199 - 249 .

GARIBALDI J M , ZHOU S , WANG X , et al . Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models [J ] . Journal of Biomedical Informatics , 2012 , 45 ( 3 ): 447 - 459 .

ZUO H , LU J , ZHANG G , et al . Fuzzy transfer learning using an infinite Gaussian mixture model and active learning [J ] . IEEE Transactions on Fuzzy Systems , 2019 , 27 ( 2 ): 291 - 303 .

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