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- W2944367816 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." @default.
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- W2944367816 date "2019-05-11" @default.
- W2944367816 modified "2023-09-29" @default.
- W2944367816 title "Deep Layered LMS Predictor." @default.
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