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Paper data
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Title:
Recursive Non-Linear Autoregressive Models (RNAR): Application to Traffic Prediction of MPEG Video Sources

Author(s):
Doulamis Nikolaos, Electrical & Computer Engineering Department, National Technical University of Athens, 9, Heroon Pol
Doulamis Anastasios, Electrical & Computer Engineering Department, National Technical University of Athens, , Heroon Poly
Ntalianis Klimis, Electrical & Computer Engineering Department, National Technical University of Athens, , Heroon Poly

Page numbers in the proceedings:
Volume I pp 368-371

Session:
Nonlinear Signal and Systems / Adaptive Methods

Paper abstract
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In this paper, an efficient algorithm for recursive estimation of a Non-linear Autoregression (NAR) model is proposed. In particular, the model parameters are dynamically adapted through time so that a) the model response, after the parameter updating, satisfies the current conditions and b) a minimal modification of the model parameters is accomplished. The first condition is expressed by applying a first-order Taylor series to the non-linear function, which models the NAR system. The second condition implies the solution to be as much as close to the previous model state. The proposed recursive scheme is evaluated for the traffic prediction of real-life MPEG coded video sources.

Paper
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A PDF version is available here

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