A new probabilistic neural network model based on backpropagation algorithm

Since the traditional probabilistic neural network (PNN) cannot systematically solve the difficulty of estimating probability function and the high space complexity, this paper introduces backpropagation (BP) algorithm into the classical PNN. By designing appropriate error function and BP algorithm based on the steepest descent, an improved BP-PNN is presented, with its algorithm and effectiveness deduced. Three synthetic datasets and ten benchmark problems have been tested, compared with Probabilistic Neural Networks (PNN), Multi-Layered Perceptron (MLP) and Support Vector Machine (SVM). The results prove that (1) the accuracy of classification of BP-PNN is much higher than PNN, and it has a significant advantage compared with MLP and SVM; (2) BP-PNN has strong capacity to identify the importance of input indicators; (3) BP-PNN is a new pattern classification method to estimate the probabilistic function, reduce the space complexity and identify the importance of the indicators.