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Abstract: 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... (Update)
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BibTeX entry: (Update)
@misc{ anastasios-adaptable,
author = "Nikolaos Doulamis Anastasios",
title = "Adaptable Neural Networks for Modeling Recursive Non-linear Systems",
url = "citeseer.ist.psu.edu/739646.html" }
Citations (may not include all citations):704 Neural Networks: A Comprehensive Foundation (context) - Haykin - 1994
530 Linear and non Linear Programming (context) - Luenberger - 1984
45 Predictive Dynamic Bandwidth Allocation for Efficient Transp.. - Chong, Li et al. - 1995
43 Recurrent Neural Networks and Robust Time Series Prediction (context) - Connor, Martin et al.
22 On Line Retrainable Neural Networks: Improving the Performan.. - Doulamis, Doulamis et al. - 2000
18 Prentice Hall (context) - Adaptive, Theory - 1996
14 Quadratic filters for signal processing (context) - Sicuranza - 1992
7 Efficient Modeling of VBR MPEG-1 Video Sources (context) - Doulamis, Doulamis et al. - 2000
5 Optimal Nonlinear Adaptive Prediction and Modeling of MPEG V.. (context) - Chang, Hu - 1997
3 Modeling and Queuing Analysis of Variable-Bit-Rate MPEG Vide.. - Doulamis, Doulamis et al. - 1998
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