Complex-valued wavelet network

摘要

In this paper, a complex-valued wavelet network (CWN) is proposed. The network has complex inputs, outputs, connection weights, dilation and translation parameters, but the nonlinearity of the hidden nodes remains a real-valued function (real-valued wavelet function). This kind of network is able to approximate an arbitrary nonlinear function in complex multi-dimensional space, and it provides a powerful tool for nonlinear signal processing involving complex signals. A complex algorithm is derived for the training of the proposed CWN. A numerical example on nonlinear communication channel identification is presented for illustration.