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Research on motion compensation method based on neural network of radial basis function

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The machining precision not only depends on accurate mechanical structure but also depends on motion compensation method. If manufacturing precision of mechanical structure cannot be improved, the motion compensation is a reasonable way to improve motion precision. A motion compensation method based on neural network of radial basis function (RBF) was presented in this paper. It utilized the infinite approximation advantage of RBF neural network to fit the motion error curve. The best hidden neural quantity was optimized by training the motion error data and calculating the total sum of squares. The best curve coefficient matrix was got and used to calculate motion compensation values. The experiments showed that the motion errors could be reduced obviously by utilizing the method in this paper.

motion compensationneural networkradial basis function

Zuo Yunbo

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Beijing Information Science and Technology University, Beijing 100192,China

supported by the Project of National Natural Science Foundation of ChinaProject of Science and Technique Development Plan of Beijing Municipal Commission of Education

51275052KM201311232022

2014

仪器仪表学报
中国仪器仪表学会

仪器仪表学报

CSTPCDCSCD北大核心EI
影响因子：2.372
ISSN：0254-3087
年,卷(期)：2014.(z2)
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