BP Neural Network in Prediction of the Constant-Current Hydrostatic Bearing Static Stiffness
SKZT3500 NC rotary table adopts constant-current hydrostatic bearing and unloading guide two sets of hydraulic system. Aiming at the characteristics of two sets of hydraulic system, this paper deduces the constant-current hydrostatic bearing static stiffness formula. Then, the theory and algorithm of BP neural network were applied to predict the constant-current hydrostatic bearing static stiffness, based on experimental measurements in a physical prototype and neural network toolbox of MATLAB. Testing results show that BP neural network can accurately forecast the constant-current hydrostatic bearing of the static stiffness.
Jianmin Zeng, Zhengyi Jiang, Taosen Li, Daoguo Yang and Yun-Hae Kim
J. Tang et al., "BP Neural Network in Prediction of the Constant-Current Hydrostatic Bearing Static Stiffness", Advanced Materials Research, Vols. 199-200, pp. 271-274, 2011