Abstract
As circumstance temperature of space camera changes, flex of structural components and distortion of optical components lead to change of focal length and image quality. Radial Basis Function (RBF) network is used to approximate the complex nonlinear relation between focalization quantity, image quality, temperature level and axial temperature difference of space camera. After the RBF Network is trained with thermo-optical experiment data, temperature level and axial temperature difference could be input to the network to obtain colder value of best image position. In this way focusing forecast under different temperatures can be realized. Results of focusing forecast experiment validate this method.
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Wu, X., Liu, J., Yu, D. (2009). Space Camera Focusing Forecast Based on RBF Network. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2009. Communications in Computer and Information Science, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04962-0_23
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DOI: https://doi.org/10.1007/978-3-642-04962-0_23
Publisher Name: Springer, Berlin, Heidelberg
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