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Optimization of fuzzy systems by dynamic switching of reasoning methods.

机译：通过动态切换推理方法来优化模糊系统。

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摘要

The major idea of this dissertation is to use different fuzzy reasoning methods, e.g., aggregation operators and defuzzification methods, for optimization of fuzzy systems. This approach extends the known methods for optimization of fuzzy systems which are based essentially on optimization of the membership functions and rules. In terms of systems theory, the former approach is related to optimization of the structure of the system while the latter is related to optimization of the parameters of the membership functions and rules. The validity of this concept is demonstrated on a number of examples.;The performance of a fuzzy system depends on which reasoning method is chosen. However, the best performing reasoning method depends significantly on the reasoning environment. Hence allowing for dynamic switching of reasoning methods in a fuzzy system as the reasoning situation changes can lead to better performance, even when the choice is only between two different reasoning methods.;The purpose of this dissertation is to construct a generalized framework which dynamically changes the reasoning method to be used in a fuzzy system as the reasoning situation changes. In particular, the Dynamic Switching Fuzzy System (DSFS) model is proposed to dynamically switch and adjust among different reasoning methods. Furthermore, it is shown how parameterized reasoning methods (e.g., BADD defuzzification method) can be tuned by DSFS. Fuzzy meta-rules are used to implement such tuning. Additionally, it is shown that tuning reasoning methods during defuzzification is computationally more efficient than tuning the rules or membership functions.;Finally, practical methods for automatic design and tuning of fuzzy systems are presented and applied to a complex control problem: swing-up control of a two-link robot called the Acrobot. A combination of Genetic Algorithms, Dynamic Switching Fuzzy Systems (DSFS), and Meta-Rule techniques is used to realize a high performance Meta-Rule Enhanced TSK controller for the Acrobot. These methods are integrated; they result in reduced design time and system complexity.
机译：本文的主要思想是使用不同的模糊推理方法，例如聚合算子和去模糊方法，来优化模糊系统。这种方法扩展了模糊系统优化的已知方法，该方法主要基于隶属函数和规则的优化。就系统理论而言，前一种方法与系统结构的优化有关，而后者与隶属函数和规则的参数优化有关。大量的例子证明了这一概念的有效性。模糊系统的性能取决于选择哪种推理方法。但是，最佳执行推理方法在很大程度上取决于推理环境。因此，即使仅在两种不同的推理方法之间进行选择，当推理情况发生变化时，也允许在模糊系统中动态切换推理方法，从而可以提高性能。本论文的目的是构建一个动态变化的广义框架。当推理情况发生变化时，在模糊系统中使用的推理方法。特别地，提出了动态切换模糊系统（DSFS）模型来在不同的推理方法之间动态切换和调整。此外，它显示了如何通过DSFS调整参数化的推理方法（例如BADD去模糊方法）。模糊元规则用于实现这种调整。此外，还表明在去模糊化过程中调整推理方法在计算上比调整规则或隶属函数更有效。;最后，提出了用于自动设计和模糊系统调整的实用方法，并将其应用于复杂的控制问题：摆动控制一个称为Acrobot的双链接机器人。遗传算法，动态切换模糊系统（DSFS）和Meta-Rule技术的组合用于为Acrobot实现高性能的Meta-Rule增强型TSK控制器。这些方法是集成的。它们减少了设计时间和系统复杂性。

著录项

  • 作者

    Smith, Michael Harvey.;

  • 作者单位

    University of California, Berkeley.;

  • 授予单位 University of California, Berkeley.;
  • 学科 Artificial Intelligence.;Computer Science.
  • 学位 Ph.D.
  • 年度 1994
  • 页码 172 p.
  • 总页数 172
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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