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A Novel Fiber Intrusion Signal Recognition Method for OFPS Based on SCN With Dropout

Accurate and fast recognition of fiber intrusion signals has always been a fundamental task in the Optical Fiber Pre-warning System (OFPS). However, currently existing recognition models tend to focus on one aspect and lack a comprehensive approach. In this paper, a dropout-based Stochastic Configuration Network (SCN) optical fiber intrusion signal recognition model is first proposed, which is named DropoutSCN. By combining dropout with randomized algorithm models, it not only enhances the fast learning ability, but also improves the generalization performance of the recognition model. In the experiment, compared with traditional Artificial Neural Network (ANN), Random Vector Functional Link (RVFL), and original SCN models, the DropoutSCN model proposed in this paper has the lowest root-mean-square error (RMSE). In terms of time efficiency, it reduces the time delay by about 2.5 times compared with the traditional ANN. In addition, this paper applies dropout to SCN, which provides a feasible thinking and reference for the study of randomized algorithm models.

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Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.37 No.20 October 15 2019

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 5221 - 5230

Bahasa

English

ISBN/ISSN

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Klasifikasi

-

Tipe Isi

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Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

RANDOMIZED ALGORITHM
DROPOUT
OPTICAL FIBER PRE-WARNING SYSTEM
SIGNAL RECOGNITION
STOCHASTIC CONFIGURATION NETWORK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Wei Li, Zhiqiang Zeng, Hongquan Qu, Chengbin Sun

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