On the Cesàro-Means-Based Orthogonal Series Approach to Learning Time-Varying Regression Functions

In this paper an incremental procedure for nonparametric learning of time-varying regression function is presented. The procedure is based on the Cesaro-means of orthogonal series. Its tracking properties are investigated and convergence in probability is shown. Numerical simulations are performed using the Fejer’s kernels of the Fourier orthogonal series.

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