Skip to Main Content
This paper presents a new probabilistic neural network model, called IPNN (for Incremental Probabilistic Neural Network), which is able to learn continuously probability distributions from data flows. The proposed model is inspired in the Specht's general regression neural network, but have several improvements which makes it more suitable to be used in on-line and robotic tasks. Moreover, IPNN is able to automatically define the network structure in an incremental and on-line way, with new units added whenever necessary to represent new training data. The experiments performed using the proposed model shows that IPNN is able to approximate continuous functions using few probabilistic units.