概率神经网络(Probabilistic Neural Network)是由D.F.Speeht博士在1989年首先提出，是径向基网络的一个分支，属于前馈网络的一种。它具有如下优点：学习过程简单、训练速度快；分类更准确，容错性好等。从本质上说，它属于一种有监督的网络分类器，基于贝叶斯最小风险准则。(The rate neural network, first proposed in 1989, is a branch of the RBF network and is one of the feedforward networks. It has the following advantages: the learning process is simple, the training speed is fast, the classification is more accurate, the fault tolerance is good, and so on. In essence, it belongs to a supervised network classifier based on Bayesian minimum risk criteria.)