Approaching the post nonlinearity of blind mixtures by hybrid neural network
It is very difficult to approach the post nonlinearity of blind mixtures. The recent neural networks for separating the post nonlinear blind mixtures are limited to the diagonal nonlinearity. In this paper a hybrid neural network is proposed to separate the post nonlinearly mixed blind signals with cross-channel disturbance. This hybrid network consists of a new neural blind de-mixer for approximating the post nonlinearity and a common network for separating the predicted linear mixtures. The blind de-mixer is made up of two subnets, which in total produce a "weak" nonlinear operator and can approach relatively strong nonlinearity by parameter-tuning. A six-step batch learning algorithm based on the fixed-point algorithm and information backpropagation is deduced. Preliminary results on a blind signal separation problem of two sources and four different types of post nonlinearity indicate the effectiveness of our model.