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    A new weighted Broad Learning System (WBLS) effectively handles industrial process noise and outliers. This robust method improves data modeling by assigning differential weights to samples, enhancing generalization.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Science

    Background:

    • The Broad Learning System (BLS) is a novel neural network known for its effective and efficient learning capabilities.
    • BLS has gained significant attention due to its excellent performance in various applications.
    • Noise and outliers in industrial processes can negatively impact the accuracy and reliability of traditional modeling techniques.

    Purpose of the Study:

    • To propose a weighted Broad Learning System (WBLS) to address the challenges posed by noise and outliers in industrial processes.
    • To develop a unified framework within WBLS for calculating weighted penalty factors.
    • To enhance the robustness and generalization of machine learning models in the presence of noisy data.

    Main Methods:

    • Introduction of a weighted penalty factor to constrain the contribution of each sample during modeling.
    • Assigning higher weights to normal samples and lower weights to abnormal samples (outliers) to mitigate their influence.
    • Utilizing the weighted ridge regression algorithm for computing the algorithm solution.
    • Development of weighted incremental learning algorithms to handle additional noisy samples without complete retraining.

    Main Results:

    • The proposed WBLS effectively eliminates the adverse effects of noise and outliers on data modeling.
    • Weighted incremental learning algorithms provide a unified framework for weight computation and efficient model updates.
    • Experimental results on public datasets and a real-world industrial application demonstrate superior generalization and robustness compared to existing methods.

    Conclusions:

    • The weighted Broad Learning System (WBLS) offers a powerful solution for robust modeling in industrial processes with noisy data.
    • The developed weighted incremental learning algorithms enable efficient adaptation to new data while maintaining model integrity.
    • WBLS demonstrates significant improvements in generalization and robustness, making it a valuable tool for real-world applications.