基于径向基函数概率神经网络的心律失常自动识别

讨论了基于径向基函数（ＲＢＦ）的概率神经网络的基本网络结构和网络的学习和运行过程，并且与ＢＰ算法的径向基神经网络进行了对比，同时也测试了网络的容错能力，结果表明，基于ＲＢＦ的概率神经网络，学习速度大大提高，同时减小了ＢＰ陷入局部极小的问题，有一定的抗噪声能力，基于ＲＢＦ的概率神经网络模型在心律失常自动识别中获得了很好的应用。

Radial Basis Function Based Probabilistic Neural Network in Arrhythmia Classification

An arrhythmia detection algorithm using radial basis function (RBF) based probabilistic neural network (PNN) was proposed. RBFNN has been widely applied in pattern recognition, but often with BP algorithm, spoiling its performance. This article discussed the configuration, learning and running of the network, compared it with BP algorithm based RBF and tested its tolerance to errors. The results of the experiments show that it avoids the problem of local minimum of BP algorithm and has capability in noise tolerance, while raises the learning rate. It has an efficient application in arrhythmia classification.