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Adaptive Multioutput Gradient RBF Tracker for Nonlinear and Nonstationary Regression.

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    This study introduces an adaptive multioutput gradient radial basis function (AMGRBF) tracker for modeling complex nonlinear and nonstationary data. The novel AMGRBF tracker enhances online regression accuracy and efficiency for dynamic systems.

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    Area of Science:

    • Machine Learning
    • Control Systems
    • Nonlinear Dynamics

    Background:

    • Multioutput regression for nonlinear and nonstationary data is an understudied area.
    • Existing methods lack efficiency in tracking fast time-varying systems.

    Purpose of the Study:

    • To develop an adaptive multioutput gradient radial basis function (AMGRBF) tracker.
    • To enable online modeling of multioutput nonlinear and nonstationary processes.

    Main Methods:

    • A compact MGRBF network with a two-step training procedure was developed.
    • An adaptive MGRBF (AMGRBF) tracker was proposed to update the network structure online.
    • The AMGRBF tracker replaces underperforming nodes with new ones that encode emerging system states.

    Main Results:

    • The AMGRBF tracker demonstrated superior adaptive modeling accuracy compared to existing methods.
    • The proposed tracker showed improved performance over deep-learning-based models.
    • The method achieved efficient online computational complexity.

    Conclusions:

    • The AMGRBF tracker effectively models complex nonlinear and nonstationary data in real-time.
    • This approach offers significant advancements in online multioutput regression for dynamic systems.