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    This study introduces a novel cascade network using a correntropy measure for parameter adjustment, improving approximation capabilities and noise robustness in regression tasks.

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    Area of Science:

    • Machine Learning
    • Artificial Neural Networks
    • Signal Processing

    Background:

    • Objective functions are crucial for tuning parameters in constructive neural networks.
    • Existing methods often focus on universal approximation capabilities.
    • Robustness to noise remains a challenge in many network architectures.

    Purpose of the Study:

    • To introduce a new objective function for parameter adjustment in cascade networks using a correntropy measure.
    • To demonstrate the universal approximation capability of the proposed network.
    • To evaluate the network's performance and robustness against impulsive noise.

    Main Methods:

    • Utilized a correntropy measure with a sigmoid kernel within the objective function.
    • Adjusted input parameters of newly added nodes in a cascade network.
    • Compared performance against eight other objective functions and a standard feedforward network on real-world regression datasets.

    Main Results:

    • The proposed network demonstrated the ability to approximate any continuous nonlinear mapping with probability one.
    • Convergence of the network was guaranteed.
    • Significant reduction in root mean square error and increased robustness to impulsive noise were observed compared to existing methods.

    Conclusions:

    • The correntropy measure offers a superior objective function for cascade network training.
    • The proposed method enhances both approximation accuracy and noise resilience.
    • This approach provides a robust solution for regression problems with noisy data.