基于片相似各项异性扩散的BP神经网络的磨粒识别研究  

Research on Wear Debris Recognition Algorithm Based on IFNN

作　　者：崔海[1] 康剑莉[2] 

机构地区：[1]浙江纺织服装职业技术学院,浙江宁波315211 [2]温州职业技术学院,浙江温州325035

出　　处：《浙江水利水电学院学报》2016年第3期77-80,共4页Journal of Zhejiang University of Water Resources and Electric Power

摘　　要：通过磨粒图像的各向异性扩散算法实现对磨粒图像特征参数的提取,然后依据磨粒图像特征参数的提取,并基于一种变尺度的BP网络学习算法,对一种基于BP神经网络的磨粒分类器进行了实验测试.证明基于片相似各项异性扩散的BP神经网络的磨粒识别方法具有较高的分类精度、具有较快的学习收敛速度,且有较高识别率.According to the qualitative characterization of the morphological features of the wear debris, feature vector math-ematics models of wear debris circular degree, slimsy and long and degree, scattering degree, and hollow degree are built, which are based on Foruier parameter refining method. Then the advantages of fuzzy system and neural network are taken to establish a kind of improved fuzzy neural network （IFNN） models and algorithm, which is used to realize the automatic classification and recognition of wear debris. An improved learning algorithm with the modified fuzzy weight is proposed on the basis of the fuzzy neurons model for the max-min fuzzy operator. The amount of calculation for the improved FNN model is reduced greatly and the convergence velocity is improved. The experiment results show that the recognition method based on the IFNN is good at algorithm convergence speed and recognition accuracy.

关 键 词：磨粒 特征参数 BP神经网络 分类器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]