PCA-based probability neural network structure optimization

The structures of probability neural networks (PNN) are quite complicated when trained with large, highly redundant training samples. A principal component analysis (PCA)-based structure was developed to optimize the PNN structure. A probability multiplication formula was used as the theoretical foundation. The PNN structure was optimized based on statistical results from the PCA for the training samples. A learning algorithm was introduced into the PNN to reduce uncertainties parameter. Test results show that with large, highly redundant training samples, the optimized PNN has a simpler structure than the traditional PNN to get a similar result.