Probabilistic Neural Network Based on Data Field

This paper proposed to decrease the structure of probabilistic neural network based on Gaussian potential of data field.Core idea is following:Introduce data field to estimate probabilistic density of training set of each class and select their maximum to construct the network;iteratively train the initial network by appending the maximum density sample unrecognized of each class to pattern layer and modify the weight of samples until satisfying desired accuracy.Incremental computing density ensures faster iteration and higher possible convergence.And introduce resampling technique to boost the generalization accuracy.Experiments show that the proposed algorithms have concise explanation,moderate fitness and effective calculation.