BP neural network-based anti-dazzle glass chemical erosion technological parameter optimization method
A BP neural network and process parameter optimization technology, applied in neural learning methods, biological neural network models, etc.
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[0026] The specific implementation manner and working principle of the present invention will be further described in detail below in conjunction with the accompanying drawings.
[0027] The anti-glare glass chemical erosion process parameter optimization method based on BP neural network comprises the following steps:
[0028] S1: Data processing, establishing the anti-glare glass chemical erosion process data set, which includes the erosion temperature, erosion time data and glass transmittance data corresponding to the erosion temperature and erosion time during the anti-glare glass chemical erosion process, and Normalize the glass transmittance in the dataset:
[0029] ;
[0030] Among them, μ is the mean of all sample data, σ is the standard deviation of all sample data, For the normalized glass transmittance data, is the glass transmittance data before normalization;
[0031] In the experimental data of chemical etching process research on anti-glare glass, tempe...
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