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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Abbreviations
- ANNs:
-
Artificial neural networks
- DSL:
-
Distance symmetry level
- FCM:
-
Fuzzy C-means
- GA:
-
Genetic algorithm
- KM:
-
K-means
- MLPs:
-
Multilayer perceptrons
- MPSDFCM:
-
Modified point symmetry–based fuzzy C-means
- MS:
-
Minkowski
- MSE:
-
Mean-squared error
- OSL:
-
Orientation symmetry level
- PSD:
-
Point symmetry distance
- PSDFCM:
-
Point symmetry–based fuzzy C-means
- SSL:
-
Symmetry similarity level
- SVMs:
-
Support vector machines
- WNNs:
-
Wavelet neural networks
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Acknowledgment
The second author is grateful to Universiti Sains Malaysia, which supplied a generous Post-Doctoral Fellowship and made it possible to undertake this research.
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Zainuddin, Z., Ong, P. Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation. Neural Comput & Applic 23 (Suppl 1), 247–259 (2013). https://doi.org/10.1007/s00521-013-1350-x
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DOI: https://doi.org/10.1007/s00521-013-1350-x