自学习模糊脉冲神经网络的旋转机械诊断

针对旋转机械故障分类边界的模糊性和传统的神经网络算法难以解决应用问题的实例规模和网络规模之间的矛盾问题,提出了一种自学习模糊脉冲神经网络算法,该算法通过脉冲序列的种群编码和无监督学习较好的克服了旋转机械故障分类边界的聚类分析无效性问题.应用表明该算法有效解决了旋转机械故障的边界模糊性问题,较大提高了故障诊断的准确率.

Fault diagnosis of rotating machinery based on self-learning fuzzy spiking neural network

For fuzziness classific boundry of fault diagnosis of rotating machinery and traditional neural network algorithms difficulted to solve contradiction between application problems example scale and netwok scale,a methord of self-learning fuzzy spiking neural network is put forward. The methord overcomes unavailability of cluster analysis on classific boundry of fault diagnosis of rotating machinery by species encoding of pulse sequence and unsupervised learning. The method shows that it effectively solves boundary fuzziness problem on fault diagnosis of rotating machinery,and greatly improves efficiency of fault diagnosis.