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Optimizing method of anti-glare glass chemical erosion process parameters based on bp neural network

A BP neural network, process parameter optimization technology, applied in neural learning methods, biological neural network models, etc.

Active Publication Date: 2018-09-28
ZHENGZHOU UNIVERSITY OF AERONAUTICS
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Problems solved by technology

Aiming at the limitations of the response surface method in dealing with complex systems, a parameter optimization method based on BP neural network to establish a nonlinear network model is proposed to solve the parameter optimization problem affected by complex factors

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  • Optimizing method of anti-glare glass chemical erosion process parameters based on bp neural network
  • Optimizing method of anti-glare glass chemical erosion process parameters based on bp neural network
  • Optimizing method of anti-glare glass chemical erosion process parameters based on bp neural network

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Embodiment Construction

[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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Abstract

The present invention relates to a BP neural network-based anti-dazzle glass chemical erosion technological parameter optimization method. The method comprises the following steps of S1 processing data; S2 determining an optimization range of the erosion temperature and the erosion time; S3 utilizing a BP neural network to construct a relation model of the erosion temperature, the erosion time and the glass transmittance; S4 carrying out the BP neural network optimization search. According to the present invention, the BP neural network is utilized to optimize an anti-dazzle glass chemical erosion technological parameter, and the BP neural network has the high mapping capability, so that any nonlinear mapping from input to output can be realized. By utilizing the high mapping capability and generalization capability of the BP neural network to establish a nonlinear relation of the temperature, the time and the transmittance, a parameter optimization problem influenced by complicated factors can be solved.

Description

technical field [0001] The invention relates to the technical field of anti-glare glass chemical erosion preparation technology, in particular to a method for optimizing anti-glare glass chemical erosion process parameters based on BP neural network. Background technique [0002] At present, the anti-glare glass process, which uses chemical etching to acidify the glass surface, is widely used. Time and temperature in the erosion process are two important factors affecting the transmittance. In order to improve the transmittance of anti-glare glass, it is necessary to optimize the design of process parameters according to the erosion condition factors temperature and time. Response surface methodology (response surface methodology, RSM) was originally proposed by Box and Wilson, is an important parameter optimization method, including experimental design, model fitting and process optimization stages, that is, by establishing the relationship between the response and signific...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/02G06N3/08C03C15/00
CPCC03C15/00G06N3/02G06N3/084
Inventor 禹建丽李金钟
Owner ZHENGZHOU UNIVERSITY OF AERONAUTICS
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