摘要
为了提高对不同认知状态下脑电信号 (EEG)的分类正确率 ,提出一种GMDH型神经网络及改进的训练算法。此网络结构在演化中生成 ,分类规则由简单多项式表示 ,训练算法可防止出现过拟合。此网络用于区分算术运算和休息状态下的脑电信号 ,正确率达到 84 5 % ,与标准前向型神经网络 (FNN)比较 ,显示了较好的分类效果。
A GMDH-type neural network and its modified training algorithm were presented in this paper to improve the classifying accuracy of EEG with different mental tasks. The network was formed through evolution, the classification rules were described by a concise set of polynomials and the training algorithm was able to prevent overfitting effectively. Experimental results showed the GMDH-type nearal could classify the EEG of math or relaxtasks with accuracy of 84. 5 % . It was indicated that GMDH-type neural network exhibited higher classifying accuracy compared to the feedforward neural network (FNN).
出处
《中国生物医学工程学报》
EI
CAS
CSCD
北大核心
2005年第1期66-69,共4页
Chinese Journal of Biomedical Engineering
基金
国家自然科学基金资助项目 (60 3 75 0 17)。
关键词
GMDH型神经网络
前向型神经网络
脑电信号
多项式
Algorithms
Biomedical engineering
Feedforward neural networks
Polynomials
Psychophysiology
