A DYNAMIC UPDATING ALGORITHM OF SMOOTHING PARAMETER VALUES OF PROBABILISTIC NEURAL NETWORKS
The paper proposes a dynamic updating algorithm for smoothing parameter values of probabilistic neural networks, which allows improving forecasting accuracy and pattern recognitionquality for these types of networks. Thus, it increases an efficiency of decision support systems.
probabilistic neural networks, smoothing parameter, interval forecasting, classification.
