引入反向传播机制的概率神经网络模型

既有的概率神经网络模型存在概率函数难以估计和空间复杂度高的缺点,提出引入反向传播机制的改进模型用以弥补以上不足. 改进模型继承了概率神经网络模型的分类原理和结构特征,同时应用了多层感知器神经网络模型的反向传播算法进行函数和参数学习,由此解决了函数估计和空间复杂度高的问题. 通过三组数值实验的验证,结果表明该模型还有着较强入指标重要性的识别能力和较高的分类精度. 该改进模型是一个新的、适用范围较广和准确度较高的模式分类方法,可辅助管理决策,具有实际意义.

Introducing the back-propagation into probabilistic neural network

Due to the difficulty of estimating probability function and the high space complexity of the existing probabilistic neural network (PNN), an improved PNN model is presented by introducing the mechanism of back-propagation (BP). The improved model inherits the principle and structure of PNN, and meanwhile applies the BP algorithm of multilayer perceptron (MLP) to train probability function and parameters. The above two aspects help overcome the PNN's shortcomings. Three numerical experiments have been designed to verify the improved model, and their results indicate that the new model has strong capacity to identify the importance of input indicators and own high accuracy of classification. In conclusion, the BP-PNN model is a new pattern classification method with widespread applicability and can support management decision.