Abstract
In the paper we study global (integral) properties of the Parzen-type recursive algorithm dealing with streaming data in the presence of the time-varying noise. The mean integrated squared error of the regression estimate is shown to converge under several conditions. Simulations results illustrate asymptotic properties of the algorithm and its convergence for a wide spectrum of a time-varying noise.
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This work was supported by the Polish National Science Center under Grant No. 2014/15/B/ST7/05264.
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Cao, J., Rutkowski, L. (2018). On the Global Convergence of the Parzen-Based Generalized Regression Neural Networks Applied to Streaming Data. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_3
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DOI: https://doi.org/10.1007/978-3-319-91253-0_3
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