Abstract
Probabilistic neural network (PNN) is a kind of supervised neural network, proposed by Specht as an alternative to back-propagation neural network. The key advantages of PNN are that, training requires only a single pass, and decision surfaces are guaranteed to approach the Bayes-optimal decision boundaries, as the number of training samples grows. Furthermore, shape of the decision surface can be made as complex as necessary, or as simple as desired, by choosing an appropriate value of the smoothing parameter; erroneous samples can be tolerated, and sparse samples are adequate for network performance. This paper reviews the PNN, modified PNN, various learning approaches employed to train the PNN and some comparisons of various types of PNN. Experimental results have been carried out to verify the ability of modified PNN in achieving good classification rate over traditional PNN, BPNN and KNN.
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Ramakrishnan, S., El Emary, I.M.M. Comparative study between traditional and modified probabilistic neural networks. Telecommun Syst 40, 67–74 (2009). https://doi.org/10.1007/s11235-008-9138-5
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DOI: https://doi.org/10.1007/s11235-008-9138-5