BP Neural Network in Prediction of the Constant-Current Hydrostatic Bearing Static Stiffness

Jun Tang; Xiao Diao Huang; Cheng Gang Fang

doi:10.4028/www.scientific.net/AMR.199-200.271

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 199-200BP Neural Network in Prediction of the...

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.

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Periodical:

Advanced Materials Research (Volumes 199-200)

Pages:

271-274

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.199-200.271

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Online since:

February 2011

Authors:

Jun Tang, Xiao Diao Huang, Cheng Gang Fang

Keywords:

BP Neural Network (BPNN), Constant-Current Hydrostatic Bearing, Static Stiffness Formula

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