基于模式相关的改进BP神经网络算法

Improved BP Neural Network Algorithm Based on Mode Correlation

针对传统的BP神经网络模式分类算法在各个网络输出值较为接近或者模式类之间的网络输出值接近的情况下容易发生误判的问题,提出一种基于模式相关的BP神经网络分类算法,并结合具体电路,运用该方法进行建模、仿真.实验结果表明,采用模式相关的BP神经网络分类算法能够充分利用网络输出层各个节点的所有输出,增强了网络的输出特性,便于正确、方便的进行模式分类,且分类效果良好,具有一定的通用性.

The traditional BP neural network classification algorithm has the inherent shortcomings such as miscarriage of justice when each network＇s output value or network output value between the mode classes is similar. So the fault diagnosis method of BP neural network based on mode correlation is proposed. Then the modeling and simulation are performed with a specific circuit. Experimental results show that the BP neural network which combining with mode correlation has made full use of all of the value which is outputted by every node of the network output layer and strengthened the network output performance. This method has made mode classification more convenient, better classification results and has certain universality..