Titlebook: Artificial Intelligence and Soft Computing; 11th International C Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad Conference proceedings

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Fachgespr?che auf der 14. GI-JahrestagungIn this paper we study probabilistic neural networks based on the Parzen kernels. Weak convergence is established assuming time-varying noise. Simulation results are discussed in details.

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On the Strong Convergence of the Orthogonal Series-Type Kernel Regression Neural Networks in a Non-sStrong convergence of general regression neural networks is proved assuming non-stationary noise. The network is based on the orthogonal series-type kernel. Simulation results are discussed in details.

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Weak Convergence of the Parzen-Type Probabilistic Neural Network Handling Time-Varying NoiseIn this paper we study probabilistic neural networks based on the Parzen kernels. Weak convergence is established assuming time-varying noise. Simulation results are discussed in details.

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