@article{ajams2019714,
author={{Basnayake, WMND and Attygalle, MDT and Liyanage-Hansen, Liwan and Nandalal, KDW},
title={Modified 1D Multilevel DWT Segmented ANN Algorithm to Reduce Edge Distortion},
journal={American Journal of Applied Mathematics and Statistics},
volume={7},
number={1},
pages={25--31},
year={2019},
url={http://pubs.sciepub.com/ajams/7/1/4},
issn={2328-7292},
abstract={In spite of the ability of Arti?cial 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 ˇ°1D Multilevel DWT Segmented ANN Algorithmˇ± where the modified algorithm presented in this paper will be called as ˇ°Denoised 1D Multilevel DWT Segmented ANN Algorithmˇ±.},
doi={10.12691/ajams-7-1-4}
publisher={Science and Education Publishing}
}