Publish In |
International Journal of Management and Applied Science (IJMAS)-IJMAS |
Journal Home Volume Issue |
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Issue |
Volume-2,Issue-12, Special Issue-1 ( Dec, 2016 ) | |||||||||
Paper Title |
A Harmony Search-Based Learning Algorithm For Epileptic Seizure Prediction | |||||||||
Author Name |
Kee Huong Lai, Zarita Zainuddin, Pauline Ong | |||||||||
Affilition |
Department of Business Studies, Faculty of Business, Economics and Accounting, HELP University, No. 15, Jalan Sri Semantan 1, Off Jalan Semantan, Bukit Damansara, 50490 Kuala Lumpur, Malaysia School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Malaysia. Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia | |||||||||
Pages |
164-169 | |||||||||
Abstract |
The learning phase of wavelet neural network entails the task of finding the optimal set of parameter, which includes wavelet activation function, translation centers, dilation parameter, synaptic weight values, and bias terms. Apart from the traditional gradient descent-based approach, metaheuristic algorithms can also be used to determine these parameters. In this work, the harmony search algorithm is employed to find the optimal solution for both synaptic weight values and bias terms in the learning of wavelet neural network. The standard harmony search algorithm is modified accordingly in the aspect of initialization of harmony memory, as well as during the improvisation stage. The proposed harmony search-based learning algorithm is used in the task of epileptic seizure prediction. Simulation results show that the proposed algorithm outperforms other metaheuristic algorithms in terms of sensitivity. Index Terms- Epileptic Seizure Prediction, Harmony Search, Learning Algorithm, Wavelet Neural Network. | |||||||||
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