Modified 1D multilevel DWT Segmented ANN Algorithm to reduce edge distortion

W. M. N. D. Basanayake; M. D. T. Attygalle; Liwan Liyanage-Hansen; K. D. W. Nandalal

doi:10.12691/ajams-7-1-4

Modified 1D multilevel DWT Segmented ANN Algorithm to reduce edge distortion

W. M. N. D. Basanayake, M. D. T. Attygalle, Liwan Liyanage-Hansen, K. D. W. Nandalal

Research output: Contribution to journal › Article › peer-review

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. 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 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".

Original language	English
Pages (from-to)	25-31
Number of pages	7
Journal	American Journal of Applied Mathematics and Statistics
Volume	7
Issue number	1
DOIs	https://doi.org/10.12691/ajams-7-1-4
Publication status	Published - 2019

Open Access - Access Right Statement

© The Author(s) 2019. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Keywords

algorithms
wavelets (mathematics)

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10.12691/ajams-7-1-4

Cite this

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title = "Modified 1D multilevel DWT Segmented ANN Algorithm to reduce edge distortion",

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. 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 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 = "algorithms, wavelets (mathematics)",

author = "Basanayake, {W. M. N. D.} and Attygalle, {M. D. T.} and Liwan Liyanage-Hansen and Nandalal, {K. D. W.}",

year = "2019",

doi = "10.12691/ajams-7-1-4",

language = "English",

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journal = "American Journal of Applied Mathematics and Statistics",

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T1 - Modified 1D multilevel DWT Segmented ANN Algorithm to reduce edge distortion

AU - Basanayake, W. M. N. D.

AU - Attygalle, M. D. T.

AU - Liyanage-Hansen, Liwan

AU - Nandalal, K. D. W.

PY - 2019

Y1 - 2019

N2 - 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. 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 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".

AB - 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. 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 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".

KW - algorithms

KW - wavelets (mathematics)

UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:52032

UR - http://article.sciappliedmathematics.com/pdf/ajams-7-1-4.pdf

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DO - 10.12691/ajams-7-1-4

M3 - Article

SN - 2328-7292

VL - 7

SP - 25

EP - 31

JO - American Journal of Applied Mathematics and Statistics

JF - American Journal of Applied Mathematics and Statistics

IS - 1

ER -

Modified 1D multilevel DWT Segmented ANN Algorithm to reduce edge distortion

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