doi: 10.7763/IJAPM.2013.V3.235
A Hybrid Algorithm for the Initialization of Wavelet Neural Networks: Application in Epileptic Seizure Classification
Zarita Zainuddin
1
,
Kee Huong Lai
1
,
Pauline Ong
2
- 1 the School of Mathematical
- 2 Faculty of Mechanical and Manufacturing
Abstract
Wavelet neural networks (WNNs) are a variant of artificial neural networks (ANNs), which are powerful mathematical modeling techniques that are used to model and study a variety of complex real-life problems. During the unsupervised learning stage, the translation vectors of the hidden nodes need to be determined. The conventional k-means and fuzzy c-means clustering algorithms have been used for this purpose. Nevertheless, these methods are prone and sensitive to initial cluster centers that have been randomly chosen. In this paper, a new hybrid clustering algorithm is presented. The type-2 fuzzy c-means clustering algorithm is hybridized with the metaheuristic harmony search algorithm. The new algorithm is then used to determine the translation vectors of WNNs. By incorporating the evolutionary harmony search algorithm in the clustering algorithm, the hybrid algorithm is able to escape from local minima while searching for the global minimum. To validate the effectiveness of the proposed algorithm, a real world problem of epileptic seizure classification problem is studied. Simulation results showed that the hybridized algorithm outperformed the stand-alone clustering algorithms.
Keywords
- Wavelet neural networks
- k-means clustering
- fuzzy c-means clustering
- harmony search algorithm
- epileptic seizure classification
How to Cite
Zarita Zainuddin, Kee Huong Lai, and Pauline Ong, "A Hybrid Algorithm for the Initialization of Wavelet Neural Networks: Application in Epileptic Seizure Classification," International Journal of Applied Physics and Mathematics, vol. 3, no. 5, pp. 352-358, 2013. https://doi.org/ 10.7763/IJAPM.2013.V3.235
Copyright & License
Copyright © 2013 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).