Prediction of Network Traffic Using Dynamic Bilinear Recurrent Neural Network

Prediction of a network traffic using Dynamic_Bilinear Recurrent Neural Network (D-BLRNN) is proposed and presented in this paper. D-BLRNN was developed to enhance the prediction capability of the BLRNN further by introducing dynamic learning control and Optimization Layer by Layer porcedure. Experiments are conducted on a real-world Ethernet network traffic data set. Results show that the dynamic BLRNN-based prediction scheme outperforms the conventional Multi-Layer Perceptron Type Neural Network in terms of normalized mean square error.

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