Title :
Prediction of MPEG Traffic Data Using a Bilinear Recurrent Neural Network with Adaptive Training
Author_Institution :
Dept. of Inf. Eng., Myong Ji Univ., Yongin
Abstract :
A time-series prediction model using a Bilinear Recurrent Neural Network (BRNN) is proposed in this paper. The BRNN model used in this paper is the Multiresolution architecture with an adaptive training mode. The Multiresolution Bilinear Recurrent Neural Network (MBRNN) is based on the BLRNN that has been proven to have robust abilities in modeling and predicting time series. The proposed MBRNN-based predictor is applied to real-time MPEG video traffic data. The performance of the proposed MBRNN-based predictor is evaluated and compared with the conventional MultiLayer Perceptron Type Neural Network (MLPNN)-based predictor and BRNN-based predictor. When compared with the MLPNN-based predictor and the BRNN-based predictor, the proposed MBRNN-based predictor shows significant improvement in terms of the Normalized Mean Square Error (NMSE) criterion.
Keywords :
learning (artificial intelligence); recurrent neural nets; time series; video coding; MPEG video traffic data; adaptive training; multiLayer perceptron type neural network; multiresolution bilinear recurrent neural network; normalized mean square error; time-series prediction model; Adaptive systems; Mean square error methods; Multi-layer neural network; Multilayer perceptrons; Neural networks; Predictive models; Recurrent neural networks; Robustness; Telecommunication traffic; Traffic control; Recurrent Neural Network; prediction; time-series;
Conference_Titel :
Computer Engineering and Technology, 2009. ICCET '09. International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-3334-6
DOI :
10.1109/ICCET.2009.224
