The Research on Estimate Model of hand’s strength in EVA with Radial Basis Function Neural Network (RBFNN)
- Paper number
IAC-13,A1,6,2,x17351
- Author
Mr. Zhou Shihua, Astronaut Center of China, China
- Year
2013
- Abstract
\begin{center}TENG Peng, WANG Chun-Hui, ZHOU Shi-hua, WANG Li, XU Yong-zhong\end{center}\begin{center}National Key Laboratory of Science and Technology on Human Factors Engineering, Astronaut Research and Training Center of China, Beijing, China, 100094\end{center} {\bf Key words}: EVA, Hand Performance, Estimate Model, RBFNN {\bf Objective}: To develop An Estimate Model of hand’s strength in EVA. {\bf Methods}: On the basis of experiment data analysis, the research of hand’s strength Estimate technology has been investigated , which is about the EVA. Firstly, the hand’s strength data on large sample with bare hand was selected as input vectors, correspondingly the hand’s strength data with EVA glove in 40KPa was selected as output vectors, and then the Radial Basis Function Neural Network (RBFNN) of hand’s strength was established. Secondly, the grip strength data with bare hand and EVA glove in 40KPa was selected to verify the precision of RBFNN’s output. {\bf Results}: The strength with EVA glove in 40KPa can be estimated by RBFNN Estimate Model according to strength with bare hand, and the maximal relative error is 14.1\%, average relative error is 14.1\%. {\bf Conclusion}: RBFNN Estimate Model can offer good sensibility and desired results. The result will provide method and technology for human-machine interface design of EVA equipment/tool.- Abstract document
- Manuscript document
(absent)