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Neural Computing and Applications

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01.12.2013 | Original Article

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

verfasst von: Zarita Zainuddin, Pauline Ong

Erschienen in: Neural Computing and Applications | Sonderheft 1/2013

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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.

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Titel: Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation
verfasst von: Zarita Zainuddin
Pauline Ong
Publikationsdatum: 01.12.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1350-x

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