基于多层神经网络和PReLU函数的后非线性BSS算法

Post-nonlinear BSS Algorithm Based on Multilayer Neural Network and PReLU Function

本文提出了一种以多层神经网络来估计概率密度函数的后非线性盲源分离算法.该算法将PReLU函数作为激活函数,并对概率密度函数进行自适应逼近,以最小互信息作为基本准则来构建目标函数测试独立性.最后用改进后的自然梯度算法推导出分离矩阵和迭代公式,以此来更新目标函数.仿真实验证明所提算法可以有效分离非线性混合信号.

In this paper,a post-nonlinear blind signal separation algorithm based on multilayer neural network is proposed to estimate the probability density function,which uses PReLU function as activation function to perform adaptive approximation to the probability density function,and contrasting independence based on cost function by taking the minimum mutual information as basic criterion.At last,the separation matrix and iteration formula are derived by using the improved algorithm,can effectively separation nonlinear mixed signals.