关键词: 动态学习, 混合型约束玻尔兹曼机, 分形理论, 结合面

Abstract: To forecast the parameters of surface topography under different processing methods,a prediction method for parameters of surface topography was proposed based on hybrid Restricted Boltzmann Machine (RBM).Aiming at the problems that the generalization ability of RBM was poor and the fixed training rate was unfavorable for the network to be free from the minimal point,a sparse autoencoder was utilized to extract the features of prediction values,and a hybrid RBM neural network was designed to predict the parameters values of surface topography.In unsupervised training,a principle of dynamic learning rate was employed to improve the network so as to increase the accuracy of eigenvector mapping.For the purpose of improving the training speed at unsupervised learning stage,the rule of comparison and dispersion was adopted to conduct the rapid training of neural network.Through using the training model of hybrid RBM,the parameters of surface topography on joint surface could be obtained by arbitrarily inputting machining parameters.To guarantee that the parameters of joint surface could be directly applied to engineering,based on the parameters of surface topography,the authors the actual process of contact model application was deduced with fractal theory.In this way,the stiffness and damping value of each node with uneven load under the microstate of joint surface were introduced to a finite element model.Compared with the experimental values of the same specimen,the correctness of implementation method for joint surface was proved,which could provide a basis for optimizing the structure and improving the precision of numerically-controlled machine tools.

Key words: dynamic learning, hybrid restricted boltzmann machine, fractal theory, joint surface