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Reseach Article

Modeling of Venice Lagoon Time series with Improved Kalman Filter based neural networks

Published on July 2012 by Archana R, A Unnikrishnan, R. Gopikakumari
Advanced Computing and Communication Technologies for HPC Applications
Foundation of Computer Science USA
ACCTHPCA - Number 5
July 2012
Authors: Archana R, A Unnikrishnan, R. Gopikakumari
c1859889-6e8e-41fd-8ec4-251f7bbcda0e

Archana R, A Unnikrishnan, R. Gopikakumari . Modeling of Venice Lagoon Time series with Improved Kalman Filter based neural networks. Advanced Computing and Communication Technologies for HPC Applications. ACCTHPCA, 5 (July 2012), 10-15.

@article{
author = { Archana R, A Unnikrishnan, R. Gopikakumari },
title = { Modeling of Venice Lagoon Time series with Improved Kalman Filter based neural networks },
journal = { Advanced Computing and Communication Technologies for HPC Applications },
issue_date = { July 2012 },
volume = { ACCTHPCA },
number = { 5 },
month = { July },
year = { 2012 },
issn = 0975-8887,
pages = { 10-15 },
numpages = 6,
url = { /specialissues/accthpca/number5/7580-1034/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Advanced Computing and Communication Technologies for HPC Applications
%A Archana R
%A A Unnikrishnan
%A R. Gopikakumari
%T Modeling of Venice Lagoon Time series with Improved Kalman Filter based neural networks
%J Advanced Computing and Communication Technologies for HPC Applications
%@ 0975-8887
%V ACCTHPCA
%N 5
%P 10-15
%D 2012
%I International Journal of Computer Applications
Abstract

The identification of nonlinear and chaotic systems is an important and challenging problem. Neural network models, particularly Recurrent Neural Networks (RNN) trained with suitable algorithms, have received particular attention in the area of nonlinear identification due to their potentialities to approximate any nonlinear behavior. A method of nonlinear identification based on the RNN model trained with improved nonlinear Kalman filter is proposed in this paper. The neural network weights are estimated using the Extended Kalman Filter(EKF) algorithm, augmented by the Expectation Maximization(EM) algorithm is used to derive the initial states and covariance of the Kalman filter. It was shown that not only could this chaotic approach provide an accurate identification, but it was also more effective in the sense that the approach had a smaller mean squares error (MSE). An experimental case study using the famous Venice lagoon time series is analyzed by the proposed algorithm. The minimum embedding dimension of the time series is calculated using the method of false nearest neighbors. The Lyapunov exponents of the model are calculated, from the state space evolution, The numerical results presented here indicate that the traditional Extended Kalman Filter algorithm combined with EM techniques are effective in building a good NN model for nonlinear identification.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Network Extended Kalman Filter Expectation Maximization Recurrent Neural Networks Lyapunov Exponent Chaotic Systems Venice Lagoon Time Series