Probabilistic neural networks for incremental learning over time-varying streaming data with application to air pollution monitoring

Danuta Rutkowska, Piotr Duda, Jinde Cao, Maciej Jaworski, Marek Kisiel-Dorohinicki, Dacheng Tao, Leszek Rutkowski

Abstract: Highlights•The incremental version of the Probabilistic Neural Network (IPNN) based on the orthogonal series, able to work in non-stationary environments.•Mathematical proofs of the convergence of the proposed estimators for different types of concept drift.•Extension to the classification task, using estimated density functions as discriminant functions.•Application of the proposed IPNN to air pollution monitoring.