摘要
提出一种RBF神经网络算法应用于线性混叠信号的盲分离。所用的RBF神经网络算法是从输入信号的数据中训练出中心值和宽度值,再训练通过用最大熵值的代价函数推导的权值。所用的代价函数保证了网络的输出尽可能独立,使信号能正确地分离。仿真验证了所用的算法能减少分离时间和提高分离效率。对比ME算法,该算法更好。
A radial basis function （RBF） neural network approach to blind source separation in linear mixture is presented. After calculating center value vector and width value vector with input datum, weight value vector that is deduced by maximizing entropy （ME） of cost function is calculated in this RBF neural network. This cost function results in the independence of the outputs with desirable moments such that the original sources are separated properly. Simulation results show that the separation time is reduced and the separation effect is very good. Compared with ME of algorithm, the effect of this algorithm is better.
出处
《科学技术与工程》
2006年第19期3083-3087,共5页
Science Technology and Engineering
基金
国家自然科学基金(60472067)
广东省自然科学基金(04205783)资助
关键词
RBF神经网络
盲分离
最大熵值法
代价函数
radial basis function neural network cost function blind source separation maximizing entropy （ME）
