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    Area of Science:

    • Optimization
    • Artificial Intelligence
    • Control Systems

    Background:

    • Quadratic programming with equality constraint (QPEC) problems are widely used in various industries.
    • Noise interference poses a significant challenge in solving QPEC problems in real-world applications.
    • Existing recurrent neural network models often struggle with noise interference.

    Purpose of the Study:

    • To propose a novel Modified Noise-Immune Fuzzy Neural Network (MNIFNN) model.
    • To enhance the noise tolerance and robustness of neural network-based QPEC solvers.
    • To improve the adaptability of the QPEC solving model through fuzzy logic.

    Main Methods:

    • Development of a Modified Noise-Immune Fuzzy Neural Network (MNIFNN).
    • Integration of proportional, integral, and differential elements for enhanced robustness.
    • Utilization of two fuzzy logic systems (FLSs) to generate adaptive design parameters based on residual and residual integral terms.

    Main Results:

    • The proposed MNIFNN model exhibits inherent noise tolerance.
    • The MNIFNN demonstrates superior robustness compared to Traditional Gradient Recurrent Neural Network (TGRNN) and Traditional Zeroing Recurrent Neural Network (TZRNN) models.
    • Numerical simulations confirm the effectiveness of the MNIFNN in handling noise interference.

    Conclusions:

    • The MNIFNN is an effective and robust model for solving QPEC problems, especially in noisy environments.
    • The adaptive fuzzy parameter design enhances the model's applicability.
    • The MNIFNN offers a promising alternative to existing methods for noise-sensitive QPEC applications.