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

WMND Basnayake^1, , MDT Attygalle¹, Liwan Liyanage-Hansen² and KDW Nandalal³

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

Cite this paper

WMND Basnayake, MDT Attygalle, Liwan Liyanage-Hansen and 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

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”.

Keywords

edge distortion, nonlinear, non-stationary, wavelet, NAR-ANN

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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