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Article

Modified 1D Multilevel DWT Segmented ANN Algorithm to Reduce Edge Distortion

1Department of Statistics, University of Colombo, Colombo, Sri Lanka

2School of Computing, Engineering and Mathematics, Western Sydney University, Western Sydney, Australia

3Department of Civil Engineering, University of Peradeniya, Peradeniya, Sri Lanka


American Journal of Applied Mathematics and Statistics. 2019, Vol. 7 No. 1, 25-31
DOI: 10.12691/ajams-7-1-4
Copyright © 2019 Science and Education Publishing

Cite this paper:
WMND Basnayake, MDT Attygalle, Liwan Liyanage-Hansen, KDW Nandalal. Modified 1D Multilevel DWT Segmented ANN Algorithm to Reduce Edge Distortion. American Journal of Applied Mathematics and Statistics. 2019; 7(1):25-31. doi: 10.12691/ajams-7-1-4.

Correspondence to: WMND  Basnayake, Department of Statistics, University of Colombo, Colombo, Sri Lanka. Email: nadeeka@stat.cmb.ac.lk

Abstract

In spite of the ability of Artificial Neural Network (ANN) to handle nonlinear relationships in data, there are instances where ANNs have not been able to predict accurately in the presence of non-stationarity. A novel algorithm that has the ability to treat the nonstationary and nonlinearity in a time series had been presented in [1]. This paper presents a modification done to the algorithm via addressing the edge distortion that arises in the real time execution. The proposed algorithm in [1] was named as “1D Multilevel DWT Segmented ANN Algorithm” where the modified algorithm presented in this paper will be called as “Denoised 1D Multilevel DWT Segmented ANN Algorithm”.

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