Advanced RNN Based NARMA Predictors
Journal of VLSI Signal Processing Systems
Modeling MPEG VBR video traffic using type-2 fuzzy logic systems
Granular computing
Short-term MPEG-4 video traffic prediction using ANFIS
International Journal of Network Management
Journal of High Speed Networks
A study on the network traffic of Connexion by Boeing: Modeling with artificial neural networks
Engineering Applications of Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Prediction of MPEG video source traffic using bilinear recurrent neural networks
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
On-line prediction of nonstationary variable-bit-rate video traffic
IEEE Transactions on Signal Processing
Feed Forward Bandwidth Indication (FFBI): Cooperation for an accurate bandwidth forecast
Computer Communications
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This paper investigates the application of a pipelined recurrent neural network (PRNN) to the adaptive traffic prediction of MPEG video signal via dynamic ATM networks. The traffic signal of each picture type (I, P, and B) of MPEG video is characterized by a general nonlinear autoregressive moving average (NARMA) process. Moreover, a minimum mean-squared error predictor based on the NARMA model is developed to provide the best prediction for the video traffic signal. However, the explicit functional expression of the best mean-squared error predictor is actually unknown. To tackle this difficulty, a PRNN that consists of a number of simpler small-scale recurrent neural network (RNN) modules with less computational complexity is conducted to introduce the best nonlinear approximation capability into the minimum mean-squared error predictor model in order to accurately predict the future behavior of MPEG video traffic in a relatively short time period based on adaptive learning for each module from previous measurement data, in order to provide faster and more accurate control action to avoid the effects of excessive load situation. Since those modules of PRNN can be performed simultaneously in a pipelined parallelism fashion, this would lead to a significant improvement in the total computational efficiency of PRNN. In order to further improve the convergence performance of the adaptive algorithm for PRNN, a learning-rate annealing schedule is proposed to accelerate the adaptive learning process. Another advantage of the PRNN-based predictor is its generalization from learning that is useful for learning a dynamic environment for MPEG video traffic prediction in ATM networks where observations may be incomplete, delayed, or partially available. The PRNN-based predictor presented in this paper is shown to be promising and practically feasible in obtaining the best adaptive prediction of real-time MPEG video traffic