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Proceedings Paper

A neural-fuzzy model with confidence measure for controlled stressed-lap surface shape presentation
Author(s): Minyou Chen; Yongjian Wan; Fan Wu; Kaigui Xie; Mingyu Wang; Bin Fan
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Paper Abstract

In computer controlled large aspheric mirror polishing process, it is crucially important to build an accurate stressed-lap surface model for shape control. It is desirable to provide a practical measure of prediction confidence to access the reliability of the resulting models. To build a reliable prediction model for representing the surface shape of stressed lap polishing process in large aperture and highly aspheric optical surface, this paper proposed a predictive model with its own confidence interval estimate based on a fuzzy neural network. The calculation of confidence interval accounts for the training data distribution and accuracy of the trained model with the given input-output data. Simulation results show that the proposed confidence interval estimation reflects the data distribution and extrapolation correctly, and works well in high-dimensional sparse data set of the detected stressed lap surface shape changes. The original data from the micro-displacement sensor matrix were used to train the neural network model. The experiment results showed that the proposed model can represent the surface shape of the stressed-lap accurately and facilitate the computer controlled optical polishing process.

Paper Details

Date Published: 21 May 2009
PDF: 9 pages
Proc. SPIE 7282, 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies, 72823M (21 May 2009); doi: 10.1117/12.831065
Show Author Affiliations
Minyou Chen, Chongqing Univ. (China)
Yongjian Wan, Institute of Optics and Electronics (China)
Fan Wu, Institute of Optics and Electronics (China)
Kaigui Xie, Chongqing Univ. (China)
Mingyu Wang, Chongqing Univ. (China)
Bin Fan, Institute of Optics and Electronics (China)


Published in SPIE Proceedings Vol. 7282:
4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies
Li Yang; John M. Schoen; Yoshiharu Namba; Shengyi Li, Editor(s)

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