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

Zeng, Zhiqiang; Qu, Hongquan; Sun, Chengbin

doi:10.1109/jlt.2019.2930624

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…As can also be seen from Figure 5, for the real data set, the SCN model based on Equation ( 9) has the best prediction result, and the predicted value almost completely coincides with the real value, while the prediction result of other models is relatively poor. Therefore, both the simulation data set and real data set prove that the FPGA implementation of the SCN model based on Equation (9) proposed in this paper Figures 4 and 5 show the results of FPGA regression prediction model on the simulation data set and real data set. In the simulation data set, the number of hidden layer nodes of the SCN and ELM were 18 and 35 respectively, while in the real data set, the number of hidden layer nodes of the SCN and the ELM were 42 and 55 respectively.…”

Section: Resultsmentioning

confidence: 60%

Section: Resultsmentioning

confidence: 71%

“…The SCN restricts the assignment of input weights and bias by introducing inequality constraints. Under the supervisory mechanism, SCN has a universal approximation property with the increase of the number of hidden nodes [7][8][9][10]. Instead of training a model with a fixed architecture, the construction process of the SCN starts with a small sized network and then adds hidden nodes incrementally until an acceptable tolerance is achieved, then solves a global least squares problem with the current learner model to find the output weights.…”

Section: Introductionmentioning

confidence: 99%

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FPGA-Based Implementation of Stochastic Configuration Networks for Regression Prediction

Gao

Feng

Pan

et al. 2020

Sensors

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The implementation of neural network regression prediction based on digital circuits is one of the challenging problems in the field of machine learning and cognitive recognition, and it is also an effective way to relieve the pressure of the Internet in the era of intelligence. As a nonlinear network, the stochastic configuration network (SCN) is considered to be an effective method for regression prediction due to its good performance in learning and generalization. Therefore, in this paper, we adapt the SCN to regression analysis, and design and verify the field programmable gate array (FPGA) framework to implement SCN model for the first time. In addition, in order to improve the performance of the SCN model based on the FPGA, the implementation of the nonlinear activation function on the FPGA is optimized, which effectively improves the prediction accuracy while considering the utilization rate of hardware resources. Experimental results based on the simulation data set and the real data set prove that the proposed FPGA framework successfully implements the SCN regression prediction model, and the improved SCN model has higher accuracy and a more stable performance. Compared with the extreme learning machine (ELM), the prediction performance of the proposed SCN implementation model based on the FPGA for the simulation data set and the real data set is improved by 56.37% and 17.35%, respectively.

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Section: Resultsmentioning

confidence: 60%

Section: Resultsmentioning

confidence: 71%