Structure optimization of BiLinear Recurrent Neural Networks and its application to Ethernet network traffic prediction

The BiLinear Recurrent Neural Network (BLRNN) based on the bilinear polynomial has been successfully applied to several nonlinear time series prediction problems. In order to obtain faster convergence in training the BLRNN, two procedures are applied to the network: (i) a structure simplification procedure whereby the multiplications for bilinear components in the BLRNN are reduced and (ii) a pruning procedure entailing application of a genetic algorithm. A structurally optimized BLRNN is thereby obtained. The computational complexity of the optimized BLRNN is reduced by about half in terms of the number of weights while preserving its generalization ability. Experiments on application of the structurally optimized BLRNN to a network traffic prediction problem are conducted on a real-world Ethernet network traffic data set. Results show that the optimized BLRNN-based prediction scheme can reduce the training time by roughly half when compared with the original BLRNN while preserving the prediction accuracy of the latter in terms of Normalized Mean Square Error (NMSE).