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The Q-Fractionalism Reasoning Learning Method.

Mehran Mazandarani, Pan Jianfei

    IEEE Transactions on Neural Networks and Learning Systems
    |November 1, 2023
    PubMed
    Summary

    Q-fractionalism reasoning, a novel machine learning approach, combines Q-learning and fractional fuzzy inference systems. This method enhances control accuracy by enabling agents to reason about actions, improving real-time control performance.

    Area of Science:

    • Machine Learning
    • Control Systems
    • Fuzzy Logic

    Background:

    • Traditional fuzzy inference systems (FIS) often struggle with unobservable and uncertain states.
    • Real-time control of complex systems like linear switched reluctance motors (LSRM) requires advanced decision-making capabilities.

    Purpose of the Study:

    • To introduce and evaluate a novel machine learning method, Q-fractionalism reasoning.
    • To enhance the performance of real-time control systems by incorporating fractional-order reasoning.

    Main Methods:

    • The Q-fractionalism reasoning method integrates Q-learning with fractional fuzzy inference systems (FFISs).
    • It utilizes primary (unobservable) and secondary (observable) fuzzy states for agent decision-making.
    • The method incorporates a knowledge base and a fractional-order reasoning mechanism.

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    Main Results:

    • The Q-fractionalism reasoning demonstrated a significant improvement in control accuracy.
    • An experimental application on a linear switched reluctance motor (LSRM) showed approximately 70% higher accuracy compared to a typical FIS.
    • The method effectively handles unobservable states and improves the detectability of primary fuzzy states.

    Conclusions:

    • Q-fractionalism reasoning offers a superior approach for real-time control applications, particularly in systems with uncertainty.
    • The integration of fractional-order reasoning enhances the decision-making capabilities of intelligent agents.
    • This method provides a robust framework for improving control accuracy and system performance.