Generalization Evaluation of a Nonlinear Auto-Regressive Neural Network for PON Technologies
2023 SBMO/IEEE MTT-S INTERNATIONAL MICROWAVE AND OPTOELECTRONICS CONFERENCE, IMOC(2023)
摘要
Building robust models with high generalization capability that can efficiently solve problems in a wide range of situations is essential to promote the development of enabling technologies. In this context, this paper evaluates the generalization capacity of a nonlinear autoregressive with external input neural network (NARXNET) for a passive optical network (PON). We adopted this type of neural network, given its applicability for nonlinear filtering processes, in which the target output is a noise-free version of the input signal. The built NARXNET was assessed with eight extra distinct experimental data sets demonstrating average RMSE and R2 values of 0.295 and 0.961, respectively, with a high generalization capability. Furthermore, NARXNET showed a fast training time of 240 seconds and an improved eye diagram for on-off keying (OOK) modulation format on a PON system.
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关键词
Generalization capacity,nonlinear autoregressive neural networks,enabling technologies,passive optical networks
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