UTHM Institutional Repository

Calibrating wavelet neural networks by distance orientation similarity fuzzy C-means for approximation problems

Ong, Pauline and Zainuddin, Zarita (2016) Calibrating wavelet neural networks by distance orientation similarity fuzzy C-means for approximation problems. Applied Soft Computing, 42. pp. 156-166. ISSN 15684946

[img]
Preview
PDF
ong_pauline_U.pdf

Download (1MB)

Abstract

Improperly tuned wavelet neural network (WNN) has been shown to exhibit unsatisfactory generaliza-tion performance. In this study, the tuning is done by an improved fuzzy C-means algorithm, that utilizesa novel similarity measure. This similarity measure takes the orientation as well as the distance intoaccount. The modified WNN was first applied to a benchmark problem. Performance assessments withother approaches were made subsequently. Next, the feasibility of the proposed WNN in forecasting thechaotic Mackey–Glass time series and a real world application problem, i.e., blood glucose level predic-tion, were studied. An assessment analysis demonstrated that this presented WNN was superior in termsof prediction accuracy.

Item Type: Article
Uncontrolled Keywords: clustering; distance similarity; function approximation; orientation similarity; time series; wavelet neural networks
Subjects: Q Science > QA Mathematics > QA297 Numerical analysis. Analysis
Divisions: Faculty of Mechanical and Manufacturing Engineering > Department of Engineering Mechanics
Depositing User: Normajihan Abd. Rahman
Date Deposited: 10 May 2016 04:38
Last Modified: 10 May 2016 04:38
URI: http://eprints.uthm.edu.my/id/eprint/8005
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year