  In this study, we present a new approach to design a Least Mean Squares (LMS)
predictor. This approach exploits the concept of deep neural networks and their
supremacy in terms of performance and accuracy. The new LMS predictor is
implemented as a deep neural network using multiple non linear LMS filters. The
network consists of multiple layers with nonlinear activation functions, where
each neuron in the hidden layers corresponds to a certain FIR filter output
which goes through nonlinearity. The output of the last layer is the
prediction. We hypothesize that this approach will outperform the traditional
adaptive filters.
