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8 December 2023 GSP LMSMCC algorithm based on neural network with trainable step size and kernel parameter
Chengjin Li, Haiquan Zhao
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Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 129430E (2023) https://doi.org/10.1117/12.3014422
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China
Abstract
In adaptive filtering, the maximum correntropy criterion (MCC) is a strategy that can effectively deal with impulse noise interference. The least mean square (LMS) algorithm of the graph signal processing (GSP) based on the MCC (GSP LMSMCC) shows good performance against impulse noise and Gaussian noise. Nevertheless, the GSP LMSMCC algorithm has two parameters that need to be analyzed, namely the step factor and kernel parameter, and a fixed step size cannot achieve better coordination between steady-state error and convergence rate. Therefore, based on the advantages of deep learning such as the ability to train on parameters, we extend the iterative formulation of the GSP LMSMCC algorithm to a multilayer network with the step size and kernel parameter trainable at each layer, where the input of each layer is the noisy graph signal and estimation of the graph signal, and the output is the next estimation of the graph signal after iteration. We train the network with the signal dataset and back propagation. Simulation results show that the method proposed not only avoids the discussion of the two related parameters, but also achieves a better compromise between steady-state error and convergence speed.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Chengjin Li and Haiquan Zhao "GSP LMSMCC algorithm based on neural network with trainable step size and kernel parameter", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 129430E (8 December 2023); https://doi.org/10.1117/12.3014422
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KEYWORDS
Neural networks
Computer simulations
Deep learning
Signal processing
Digital filtering
Interference (communication)
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