A New Supervised Learning Algorithm for Probabilistic Neural Network
Puyin Liu · Mohu xitong yu shuxue · 2006
A new supervised learning algorithm for the PNN is developed: the learning vector quantization is employed to group training samples and the Genetic algorithms (GA’s) is used for training the network’s smoothing parameters and hidden central vector for determining hidden neurons. Simulations results show that, the advantage of our method in the classification accuracy is over other unsupervised learning algorithms for PNN.