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arXiv · 1905.04596

Deep Layered LMS Predictor

Lubna Shibly Mokatren·Ahmet Enis Cetin·Rashid Ansari
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Abstract

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.

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BibTeXRIS

Lubna Shibly Mokatren, Ahmet Enis Cetin, Rashid Ansari. 2019-05-11. Deep Layered LMS Predictor. https://arxiv.org/abs/1905.04596

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