Strong Convergence of the Parzen-Type Probabilistic Neural Network in a Time-Varying Environment

In this paper general regression neural networks are applied to handle nonstationary noise. Strong convergence is established. Experiments conducted on synthetic data show good performance in the case of finite length of data samples.

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