標題: Optimal nonlinear adaptive prediction and modeling of MPEG video in ATM networks using pipelined recurrent neural networks
作者: Chang, PR
Hu, JT
交大名義發表
電信工程研究所
National Chiao Tung University
Institute of Communications Engineering
關鍵字: MPEG;nonlinear autoregressive moving average (NARMA);pipelined recurrent neural network (PRNN)
公開日期: 1-八月-1997
摘要: 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 pro,ide 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 minimun 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.
URI: http://dx.doi.org/10.1109/49.611161
http://hdl.handle.net/11536/405
ISSN: 0733-8716
DOI: 10.1109/49.611161
期刊: IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume: 15
Issue: 6
起始頁: 1087
結束頁: 1100
顯示於類別:期刊論文


文件中的檔案:

  1. A1997XN84300010.pdf