文章基本信息

标题：Optimal Classification of Epileptic EEG Signals Using Neural Networks and Harmony Search Methods
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作者：Gao, Xiao-Zhi ; Wang, Jing ; Tanskanen, Jarno M. A. 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2014
卷号：9
期号：1
页码：230-239
DOI：10.4304/jsw.9.1.230-239
语种：English
出版社：Academy Publisher
摘要：In this paper, the Harmony Search (HS)-aided BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Three HS methods, the original version and two new variations recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training.
关键词：Harmony Search (HS) method;ElectroEncephaloGram (EEG);BP neural networks;optimization;Opposition-Based Learning (OBL);memetic computing;bee foraging algorithm;signal classification