人工晶体学报2017，Vol.46Issue(8)：1649-1652,4.
用BP神经网络模型预测Ni-Al2O3复合涂层Al2O3粒子复合量研究
Study on the Al2O3 Contents in Ni-Al2O3 Composite Coatings Predicted by Using BP Neural Network Model
摘要
Abstract
A 4×9×1 type of BP neural network model was set up to predict the Al2O3 contents in Ni-Al2O3 composite coatings by using the artificial neural network technology.The content and 3D surface pattern were analyzed by using XRD diffraction and atomic force microscopy (AFM).The results show that when the number of hidden layers is 9, the minimum root mean square error is 1.13%, the fitting similarity R is about 0.99937, which indicates that the BP neural network model can accurately predict the Al2O3 contents.When the duty ratio is 60%, the cathode current density of 4 A/dm2, pH=4, the bath temperature of 55 ℃, Ni-Al2O3 composite coating has a dense structure, and the crystalline is fine.关键词
BP神经网络模型/Ni-Al2O3复合涂层/Al2O3复合量/预测Key words
BP neural network model/Ni-Al2O3 composite coating/Al2O3 content/prediction分类
化学化工引用本文复制引用
李源彬,岳文喜..用BP神经网络模型预测Ni-Al2O3复合涂层Al2O3粒子复合量研究[J].人工晶体学报,2017,46(8):1649-1652,4.基金项目
国家自然科学基金(51474072) （51474072）
