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    This study introduces a novel fuzzy neural network (FNN) called DK-SOFNN, which overcomes data limitations by integrating empirical knowledge. This approach enhances model structure and parameter tuning for improved generalization in nonlinear system modeling.

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    Area of Science:

    • Artificial Intelligence
    • Computational Intelligence
    • Machine Learning

    Background:

    • Fuzzy neural networks (FNNs) are effective for nonlinear system modeling due to knowledge leveraging and adaptive learning.
    • Insufficient data hinders FNNs from achieving optimal structure and generalization performance.
    • Existing FNNs struggle with structure determination when data is limited.

    Purpose of the Study:

    • To propose a data-knowledge-driven self-organizing FNN (DK-SOFNN) to address the generalization limitations of FNNs with insufficient data.
    • To enhance FNN structure acquisition and parameter optimization through novel strategies.
    • To improve the robustness and applicability of FNNs in complex modeling tasks.

    Main Methods:

    • A structure compensation strategy is employed to extract structural information from empirical knowledge for DK-SOFNN.
    • A parameter reinforcement mechanism is developed to guide parameter evolution based on the current model structure.
    • Theoretical analysis of DK-SOFNN is conducted for both fixed and dynamic structure cases, deriving convergence conditions.

    Main Results:

    • The proposed DK-SOFNN successfully acquires a complete model structure by mining empirical knowledge.
    • The parameter reinforcement mechanism ensures parameter evolution is suitable for the dynamic model structure, leading to robust models.
    • Convergence conditions for DK-SOFNN are established, providing guidance for practical implementation.

    Conclusions:

    • DK-SOFNN effectively overcomes the challenge of insufficient data in FNNs by integrating knowledge and adaptive learning.
    • The combination of structure compensation and parameter reinforcement leads to improved generalization and robustness.
    • The DK-SOFNN demonstrates significant potential, validated through benchmark problems and industrial applications.