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Related Experiment Videos

Fuzzy inference neural network for fuzzy model tuning.

K M Lee1, D H Kwak, L K Hyung

  • 1Dept. of Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Taejon.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 1, 1996
PubMed
Summary

This study introduces a novel fuzzy neural network for tuning fuzzy models. This method automates fine-tuning of fuzzy rules, improving system behavior efficiently.

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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

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Area of Science:

  • Computational Intelligence
  • Machine Learning
  • Fuzzy Systems

Background:

  • Manual definition of fuzzy rules is intuitive but fine-tuning is difficult.
  • Existing methods for tuning fuzzy models can be complex and restrictive.

Purpose of the Study:

  • To propose an automated tuning method for fuzzy models.
  • To develop a flexible fuzzy neural network applicable to various fuzzy rule structures and defuzzification methods.

Main Methods:

  • A fuzzy neural network model is proposed to embody fuzzy models.
  • The model enables fuzzy inference and parameter tuning for linguistic terms and rule importance.

Main Results:

  • The proposed fuzzy neural network effectively tunes fuzzy model parameters.

Related Experiment Videos

  • Experimental results demonstrate the applicability and efficiency of the tuning method.

    • Conclusions:

    • The developed fuzzy neural network offers a viable solution for automated fuzzy model tuning.
    • This approach simplifies the optimization of fuzzy systems, enhancing their performance.