The Parzen kernel approach to learning in non-stationary environment

In this paper a method for nonparametric regression estimation in non-stationary environment is presented. The Parzen kernels are used to design the recursive general regression neural networks to track changes of non-stationary system under non-stationary noise. The probabilistic properties of the proposed method are investigated. Experimental results are presented and discussed.

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