CAJ | 학술논문

通过提取磨粒形状特征参数、颜色特征参数和表面纹理等特征参数对磨粒形态进行量化表征，并以此为输入矢量，引入遗传算法（GA）改进BP神经网络对磨粒进行自动分类识别，建立遗传算法改进的BP神经网络模型，并给出具体的算法实现过程。分别应用遗传算法改进的BP神经网络模型和未引入遗传算法改进的BP神经网络模型对磨粒图像进行智能识别。实验结果表明，遗传算法改进的BP神经网络综合了遗传算法的全局优化和BP算法局部搜索速度快的特点，网络识别率较高，具有较好的全局性。
통과제취마립형상특정삼수、안색특정삼수화표면문리등특정삼수대마립형태진행양화표정，병이차위수입시량，인입유전산법（GA）개진BP신경망락대마립진행자동분류식별，건립유전산법개진적BP신경망락모형，병급출구체적산법실현과정。분별응용유전산법개진적BP신경망락모형화미인입유전산법개진적BP신경망락모형대마립도상진행지능식별。실험결과표명，유전산법개진적BP신경망락종합료유전산법적전국우화화BP산법국부수색속도쾌적특점，망락식별솔교고，구유교호적전국성。
A improved back propagation(BP)neural network by Genetic algorithm was introduced to realize the auto-matic classification and recognition of wear debris,based on the qualitative characterization of the morphological features of the wear debris making use of the characteristic parameters of wear debris shape,color,and surface texture.A neural net-work model based on the improved back propagation (BP)neural network by Genetic algorithm was established to classify and recognize the wear debris using those parameters as the input vectors.The algorithm of the established model was de-tailed.By comparing the results of automatic recognizing the wear debris by the improved BP neural network and the pres-ented BP neural network,it shows that the improved back propagation (BP)neural network combines the global optimiza-tion feature of genetic algorithm and the fast speed feature in local search of BP algorithm,which has a high recognition rate and better global search feature.