21 May 2009 A neural-fuzzy model with confidence measure for controlled stressed-lap surface shape presentation
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Proceedings Volume 7282, 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies; 72823M (2009) https://doi.org/10.1117/12.831065
Event: AOMATT 2008 - 4th International Symposium on Advanced Optical Manufacturing, 2008, Chengdu, Chengdu, China
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.
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Minyou Chen, Minyou Chen, Yongjian Wan, Yongjian Wan, Fan Wu, Fan Wu, Kaigui Xie, Kaigui Xie, Mingyu Wang, Mingyu Wang, Bin Fan, Bin Fan, } "A neural-fuzzy model with confidence measure for controlled stressed-lap surface shape presentation", 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; https://doi.org/10.1117/12.831065
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