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A study of membership functions on mamdani-type fuzzy inference system for industrial decision-making.

机译：基于mamdani型工业决策的模糊推理系统的隶属度函数研究。

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摘要

The complexity of product design in industry has been continuously increasing. More factors are required to be taken into account simultaneously before a decision about the new product could be determined. For this reason, decision-making process costs much more time and it may even be impossible to determine the optimal decision by normal calculations. Therefore, Fuzzy Inference System based on Fuzzy Logic is introduced as a quick decision-making tool to arrive at a good decision within much shorter time.;This thesis focuses on studying the features of membership functions in Mamdani-type fuzzy inference process. It is aimed at making the black box of fuzzy inference system to be transparent by adjusting the membership functions to control the relations between input and output variables. Systematic trial and error is implemented based on the Fuzzy Logic Toolbox from MATLAB, and conclusions developed from experiments help eliminate the uncertainties of membership functions, so that the inference process turns to be more precise and reliable. Firstly, Single-Input Single-Output (SISO) Fuzzy Inference System is discussed through the adjustment of membership functions, and the influence on input-output relations are concluded. Next, Two-Input Single-Output (TISO) Fuzzy Inference System is simulated to verify the conclusions from SISO Fuzzy Inference System, and general features of membership functions on affecting input-output relation are developed. Then, an approach using weights on input variables, for practical decision-making process, is derived. Finally, a design problem of timing system of automobile engine is chosen as case study to examine the validity of conclusions on practical decision-making problem.
机译：工业中产品设计的复杂性一直在增加。在决定新产品之前，需要同时考虑更多因素。因此，决策过程要花费更多时间，甚至可能无法通过正常计算来确定最佳决策。因此，引入基于模糊逻辑的模糊推理系统作为一种快速的决策工具，可以在更短的时间内做出良好的决策。本论文着重研究了Mamdani型模糊推理过程中隶属函数的特征。其目的是通过调整隶属函数来控制输入和输出变量之间的关系，使模糊推理系统的黑匣子变得透明。基于MATLAB的Fuzzy Logic Toolbox进行系统的反复试验，通过实验得出的结论有助于消除隶属函数的不确定性，从而使推理过程变得更加精确和可靠。首先，通过调整隶属度函数，讨论了单输入单输出模糊推理系统，并总结了其对输入输出关系的影响。接下来，对两输入单输出（TISO）模糊推理系统进行了仿真，以验证SISO模糊推理系统的结论，并开发了隶属函数对输入输出关系的影响的一般特征。然后，推导了使用权重输入变量进行实际决策的方法。最后，以汽车发动机正时系统的设计问题为例，研究了实际决策问题结论的正确性。

著录项

  • 作者

    Wang, Chonghua.;

  • 作者单位

    Lehigh University.;

  • 授予单位 Lehigh University.;
  • 学科 Engineering Mechanical.;Applied Mathematics.;Logic.
  • 学位 M.S.
  • 年度 2015
  • 页码 215 p.
  • 总页数 215
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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