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    Area of Science:

    • Artificial Intelligence
    • Computational Intelligence
    • Fuzzy Systems

    Background:

    • Fuzzy rule interpolation (FRI) enables fuzzy rule-based systems (FRBSs) to infer outcomes with sparse rule bases.
    • Traditional FRI methods rely on distance metrics for rule selection, which can be computationally intensive.
    • Extensive distance calculations pose challenges for real-time applications and large datasets.

    Purpose of the Study:

    • To introduce a novel rule-ranking-based FRI method (RT-FRI) to overcome the limitations of distance-centric approaches.
    • To develop a more efficient rule selection procedure for FRBSs.
    • To address the monotonicity demands of aggregation functions with a variant (DMRT-FRI).

    Main Methods:

    • Proposed RT-FRI method utilizing rule ranking scores derived from antecedent attribute aggregation.
    • Introduced DMRT-FRI variant to ensure directional monotonicity for aggregation functions.
    • Compared RT-FRI and DMRT-FRI against traditional distance-based FRI methods.

    Main Results:

    • RT-FRI demonstrates significant efficiency improvements over distance-based FRI methods.
    • DMRT-FRI achieves a favorable balance between accuracy and computational efficiency.
    • The rule-ranking approach streamlines the rule selection process in FRBSs.

    Conclusions:

    • RT-FRI offers a computationally efficient alternative for fuzzy rule interpolation.
    • DMRT-FRI provides a robust solution balancing accuracy and speed for FRBS applications.
    • The proposed methods enhance the scalability and performance of FRBSs with sparse rule bases.