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Related Experiment Videos

Automatic generation of fuzzy inference systems via unsupervised learning.

Meng Joo Er1, Yi Zhou

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University, S1, 50 Nanyang Ave, Singapore 639798, Singapore. emjer@ntu.edu.sg

Neural Networks : the Official Journal of the International Neural Network Society
|July 26, 2008
PubMed
Summary

This study introduces Enhanced Dynamic Self-Generated Fuzzy Q-Learning (EDSGFQL) for automated Fuzzy Inference System (FIS) generation. This novel unsupervised learning approach dynamically creates, adjusts, and deletes fuzzy rules, outperforming existing methods in mobile robot simulations.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Fuzzy Inference Systems (FISs) are crucial for control and decision-making.
  • Traditional FIS generation often relies on supervised learning or manual design.
  • Automating FIS generation, especially using unsupervised methods, remains a challenge.

Purpose of the Study:

  • To present a novel approach, Enhanced Dynamic Self-Generated Fuzzy Q-Learning (EDSGFQL), for automatic FIS generation.
  • To utilize unsupervised learning (UL), including reinforcement learning (RL), for FIS structure identification and parameter estimation.
  • To demonstrate the dynamic creation, deletion, and adjustment of fuzzy rules.

Main Methods:

  • Employed UL clustering for input space partitioning in FIS generation.
  • Integrated RL for dynamic fuzzy rule adjustment and deletion based on reinforcement signals.
  • Developed EDSGFQL to automate the entire FIS generation process.

Main Results:

  • EDSGFQL successfully generated efficient FISs for mobile robot tasks.
  • The approach demonstrated superior performance compared to existing methods in simulations.
  • Dynamic rule management enabled adaptive and effective FIS creation.

Conclusions:

  • The proposed EDSGFQL methodology offers an effective unsupervised approach to automatic FIS generation.
  • This method enhances FIS efficiency through dynamic rule adaptation.
  • EDSGFQL shows significant promise for applications in robotics and intelligent control systems.