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Automatic generation and reduction of the semi-fuzzy knowledge base in symbolic processing and numerical calculation.

机译：在符号处理和数值计算中自动生成和减少半模糊知识库。

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摘要

Typical fuzzy expert systems can only model human behavior on a rule-base level, but they cannot create the comprehensible rules which are difficult to acquire from experts. There is also a lack of logical dimension reduction method for the reduction of an existing rule base generated by experts or analytical modelling. We have proposed an inductive learning method with semantic intervals (SI) sufficiently approximated from normal convex fuzzy sets for generation (Zhao et al 1992) as well as reduction (Turksen and Zhao 1992) of the semi-fuzzy knowledge bases by using input-output data collected from objective processes. The validity of the approximation above is proved by the criterion of uncertainty compromise in approximation to adjacent fuzzy sets. The semi-fuzzy knowledge base consists of two main parts, i.e., a data base with the triangular semi-fuzzy sets (TSFSs) derived from the SI and a rule base containing the rule sets with the TSFSs.; The SI plays a key role in symbolic processing for inductive learning. To explore the validation, verification for this automatic knowledge acquisition scheme, an equivalence between the inductive learning with SI and a valid pseudo-Boolean logic simplification is proved. Based on the equivalence, the reliability, implementability and learnability are analyzed and acknowledged for the automatic generation and reduction of the rules with the TSFSs.; The TSFSs are functional numerical calculations of an inference engine. The interval valued compositional rule of inference (Turksen 1989) is extended as an adequate inference engine on the TSFSs to carry out the linguistic and numerical values.; The advantage of introducing the SI with the associated TSFS (the SI-TSFS pair) is to integrate symbolic processing and numerical calculations. The reduced semi-fuzzy knowledge base is generated through the SI-TSFS pair to overcome the difficulty of the fuzzy logic simplification. Originally this difficulty exists in the conventional fuzzy qualitative modelling technique. Furthermore, the derivation of the SI-TSFS is consistent with the separation theorem (Zadeh 1965).; In practical applications even when the condition for the equivalence is not satisfied, the proposed scheme can still provide the semi-fuzzy knowledge base with better testing results in both the classification and inference of a singleton numerical value. The proposed method has been shown to be successful in the modelling of continuous and discrete complex processes such as chemical vinylon synthesis, a repair parts service center, search and rescue satellite-aided tracking (SARSAT), human operation of a chemical plant and stock market activities.
机译：典型的模糊专家系统只能在规则基础上对人类行为进行建模，但无法创建难以从专家那里获得的可理解规则。还缺少用于缩减专家或分析模型生成的现有规则库的逻辑维数缩减方法。我们提出了一种归纳学习方法，该方法的语义间隔（SI）可以从正则凸模糊集充分逼近以生成（Zhao等，1992），并且可以通过使用输入输出来简化半模糊知识库（Turksen和Zhao，1992）。从客观过程中收集的数据。上面的近似方法的有效性通过不确定性折衷的标准证明了，它近似于相邻的模糊集。半模糊知识库包括两个主要部分，即具有从SI派生的三角形半模糊集（TSFS）的数据库和包含带有TSFS的规则集的规则库。 SI在归纳学习的符号处理中起着关键作用。为了探索这种自动知识获取方案的验证，验证，证明了使用SI的归纳学习与有效的伪布尔逻辑简化之间的等价性。在等效性的基础上，分析并确认了使用TSFS自动生成和减少规则的可靠性，可实施性和可学习性。 TSFS是推理引擎的函数数值计算。区间值组成推理规则（Turksen 1989）被扩展为TSFS上的适当推理引擎，以执行语言和数值。将SI与关联的TSFS（SI-TSFS对）一起引入的优点是集成了符号处理和数值计算。简化的半模糊知识库是通过SI-TSFS对生成的，克服了模糊逻辑简化的困难。最初，这种困难存在于常规的模糊定性建模技术中。此外，SI-TSFS的推导与分离定理一致（Zadeh 1965）。在实际应用中，即使不满足等价条件，该方案仍然可以为半模糊知识库提供更好的单例数值分类和推理结果。该方法已经证明可以成功地建模连续和离散的复杂过程，例如化学维尼纶合成，维修零件服务中心，搜索和救援卫星辅助跟踪（SARSAT），化工厂的人为操作和股票市场活动。

著录项

  • 作者

    Zhao, Hong.;

  • 作者单位

    University of Toronto (Canada).;

  • 授予单位 University of Toronto (Canada).;
  • 学科 Engineering Industrial.; Engineering System Science.; Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 1995
  • 页码 303 p.
  • 总页数 303
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 一般工业技术;系统科学;人工智能理论;
  • 关键词

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