基于粗糙集理论的人工神经网络故障诊断系统

以粗糙集理论中的信息系统属性为主要工具 ,将复杂的RBF神经网络分层约简 ,剔除其中不必要的属性 ,构建了优化的粗集 -神经网络模型。通过对实例分析 ,使用该模型可以有效地减少输入层神经元个数 ,提高神经网络模型的学习效率和诊断的准确性 ,在故障诊断中有良好的应用前景

Artificial neural network fault diagnosis system based on rough set theory

In this paper, knowledge representation system of rough set theory is taken as a major tool to delaminate the complex RBF neural network and in which unnecessary properties are eliminated. An optimized neural network model is established. Through analyzing for instance, the model can decrease the number of the network input nerve cells effectively. The results show that the strategy has better study efficiency and diagnosis accuracy. It is estimated that the optimized strategy may be further applied in fault diagnosis.