粗糙集神经网络故障诊断系统的优化方法研究

神经网络的联想能力不足影响它在故障诊断中进一步应用，该文根据粗糙集理论擅长于处理不完整小样本数据的优点，提出了使用粗糙集理论优化BP神经网络故障诊断系统的基本策略，构建了优化的粗集神经网络模型。通过对轴承故障数据和磨削工况分析表明，使用该模型可以有效地减少输入层神经元的个数，改进网络内部结构，提高神经网络模型的学习效率和诊断的准确率，在故障诊断中有良好的应用前景。

OPTIMIZING STRATEGY ON ROUGH SET NEURAL NETWORK FAULT DIAGNOSIS SYSTEM

The association deficiency of neural network affects its further application in pattern recognition. Rough set theory(RS) is excellent in disposal of small incomplete sample data. Based on this, the basic optimizing strategy of RS BP neural network fault diagnosis is presented in this paper. Using the strategy, an optimized neural network model is established. In this model, RS is employed as a preprocess course, and the results of RS preprocess decide the model structure. So the model can decrease the number of the network input nerve cells effectively, and ameliorate network inner structural. The bearing fault data and grinding condition data are analyzed for the model. 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.