摘要
基于小波神经网络,文中提出了可变尺度小波神经网络,可减少隐层神经元数目,精简网络结构。为防止网络训练陷入局部最小点,提出使用熵函数作为网络的代价函数,并将基于熵函数的可变尺度小波神经网络应用于通信信道均衡。仿真实验表明所提方法比线性均衡器具有更好的抗非线性能力,与传统的小波神经网络相比可减少网络神经元个数和迭代次数,并能使网络收敛到全局最小点。
Based on the wavelet neural network, this paper proposes a new network named varying scales wavelet neural network to reduce wavelet-neuron numbers and simplify network structure. In order to prevent the network from getting into the local minimal point, entropy function is used as penalty function. The new network is applied to channel equalization. Simulation demonstrates that this network has less wavelet-neurons and recursive steps while converging to global minimal point than usual wavelet neural network equalizer.
出处
《信息技术》
2005年第7期99-102,共4页
Information Technology
关键词
小波神经网络
可变尺度
熵函数
信道均衡
wavelet neural network
varying scale
entropy function
channel equalization
