Optimal classification of epileptic EEG signals using neural networks and harmony search methods

被引：2

作者：

机构：

[1] College of Information Engineering, Shanghai Maritime University

[2] Department of Automation and Systems Technology, Aalto University School of Electrical Engineering

[3] Laboratory of Image Processing and Pattern Recognition, Beijing Normal University

[4] Department of Biomedical Engineering, Tampere University of Technology

[5] School of Foundational Education, Peking University Health Science Center

来源：

| 1600年 / Academy Publisher卷 / 09期

关键词：

Bee foraging algorithm; Bp neural networks; Electroencephalogram (EEG); Harmony search (HS) method; Memetic computing; Opposition-based learning (OBL); Optimization; Signal classification;

D O I：

10.4304/jsw.9.1.230-239

中图分类号：

学科分类号：

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. © 2014 ACADEMY PUBLISHER.

引用

页码：230 / 239

页数：9

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