Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation

Zainuddin, Zarita and Pauline, Ong (2013) Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation. Neural Computing and Applications, 23 (NIL). pp. 247-259. ISSN 0941-0643

Text AJ 2017 (580).pdf Restricted to Registered users only Download (553kB) | Request a copy

Abstract

Specifying the number and locations of the translation vectors for wavelet neural networks (WNNs) is of paramount significance as the quality of approximation may be drastically reduced if initialization of WNNs parameters was not done judiciously. In this paper, an enhanced fuzzy C-means algorithm, specifically the modified point symmetry–based fuzzy C-means algorithm (MPSDFCM), was proposed, in order to determine the optimal initial locations for the translation vectors. The proposed neural network models were then employed in approximating five different nonlinear continuous functions. Assessment analysis showed that integration of the MPSDFCM in the learning phase of WNNs would lead to a significant improvement in WNNs prediction accuracy. Performance comparison with the approaches reported in the literature in approximating the same benchmark piecewise function verified the superiority of the proposed strategy.

Item Type:	Article
Uncontrolled Keywords:	Clustering; Function approximation; Fuzzy C-means; Symmetry; Wavelet neural networks
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions:	Faculty of Mechanical and Manufacturing Engineering > Department of Mechanical Engineering
Depositing User:	Miss Nur Rasyidah Rosli
Date Deposited:	28 Nov 2021 08:23
Last Modified:	28 Nov 2021 08:23
URI:	http://eprints.uthm.edu.my/id/eprint/4209

Actions (login required)

View Item