In this paper, a method for nonparametric regression estimation in a time-varying environment is presented. The orthogonal series-based kernels are used to design learning procedures tracking non-stationary systems ch...
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In this paper, a method for nonparametric regression estimation in a time-varying environment is presented. The orthogonal series-based kernels are used to design learning procedures tracking non-stationary systems changes under non-stationary noise. The presented procedures, constructed in the spirit of generalized regression neural networks, are a very effective tool to deal with stream data. The convergences in probability and with probability one are proved, experimental results are given and discussed. (C) 2017 Elsevier Inc. All rights reserved.
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