Paper Title:
BP Neural Network in Prediction of the Constant-Current Hydrostatic Bearing Static Stiffness
  Abstract

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.

  Info
Periodical
Advanced Materials Research (Volumes 199-200)
Edited by
Jianmin Zeng, Zhengyi Jiang, Taosen Li, Daoguo Yang and Yun-Hae Kim
Pages
271-274
DOI
10.4028/www.scientific.net/AMR.199-200.271
Citation
J. Tang, X. D. Huang, C. G. Fang, "BP Neural Network in Prediction of the Constant-Current Hydrostatic Bearing Static Stiffness", Advanced Materials Research, Vols. 199-200, pp. 271-274, 2011
Online since
February 2011
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Price
$32.00
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