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Related Experiment Video

Updated: Jan 19, 2026

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AdaBoost-SCN algorithm for optical fiber vibration signal recognition.

Hongquan Qu, Tingliang Feng, Yanping Wang

    Applied Optics
    |September 11, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the AdaBoost-SCN algorithm to improve fiber vibration signal recognition. The new method enhances accuracy and generalization for optical fiber prewarning systems, especially with limited data.

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    Area of Science:

    • Optical Engineering
    • Signal Processing
    • Machine Learning

    Background:

    • Optical Fiber Prewarning Systems (OFPS) rely on accurate fiber vibration signal recognition.
    • Neural networks, particularly Stochastic Configuration Networks (SCNs), are common but struggle with limited data.
    • Existing SCNs show limited recognition rates when vibration signal datasets are small.

    Purpose of the Study:

    • To enhance the recognition rate of fiber vibration signals using limited data.
    • To improve the generalization capability of Stochastic Configuration Networks (SCNs).
    • To introduce an improved algorithm for optical fiber prewarning systems.

    Main Methods:

    • Proposed the AdaBoost-SCN algorithm, integrating multiple SCNs as base classifiers within the AdaBoost framework.
    • Utilized a small vibration signal dataset for training and testing.
    • Compared the performance of AdaBoost-SCN against the original SCN.

    Main Results:

    • The AdaBoost-SCN algorithm achieved a 12.1% higher testing accuracy compared to the original SCN on a small vibration signal set.
    • Demonstrated significant improvement in recognition rates for limited vibration signal samples.
    • Showcased enhanced generalization ability of SCNs through the AdaBoost integration.

    Conclusions:

    • The AdaBoost-SCN algorithm effectively boosts fiber vibration signal recognition accuracy, especially with limited data.
    • This approach enhances the practical applicability of SCNs in optical fiber prewarning systems.
    • The proposed method offers a robust solution for improving signal recognition and system reliability.