Laser & Optoelectronics Progress, Volume. 56, Issue 14, 140602(2019)
Fiber Intrusion Signal Recognition Algorithm Based on Stochastic Configuration Network
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Fig. 3. Dropout-SCN model: standard multi-input and multi-output network model is presented
Fig. 4. Original signals. (a) Digging signal; (b) knocking signal; (c) electric drill signal
Fig. 5. Scatter plot of output weight distributions of SCN, Dropout-SCN and SCN with L2 regularization
Fig. 6. Effect of Dropout probability on test error under number of hidden nodes L=70
Table 1. Results of optical fiber test data
View tableTable 1. Results of optical fiber test data
Network model Hidden node number Correct prediction number Accuracy /% SCN 50 214 89.17 SCN with L2 regularization 50 217 90.42 Dropout-SCN 50 222 92.50 SCN 70 217 90.42 SCN with L2 regularization 70 221 92.08 Dropout-SCN 70 227 94.58
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Zhiyong Sheng, Zhiqiang Zeng, Hongquan Qu, Wei Li. Fiber Intrusion Signal Recognition Algorithm Based on Stochastic Configuration Network[J]. Laser & Optoelectronics Progress, 2019, 56(14): 140602
Paper Information
Category: Fiber Optics and Optical Communications
Received: Dec. 12, 2018
Accepted: Feb. 25, 2019
Published Online: Jul. 12, 2019
The Author Email: Zeng Zhiqiang (13101040127@mail.ncut.edu.cn)
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