Fuzzy BP neural network based glass tempering process parameter setting method
A technology of BP neural network and process parameters, applied in glass tempering, biological neural network models, glass manufacturing equipment, etc., can solve the problems of not meeting modern production scale, high requirements for technical personnel, increasing production costs, etc., and achieve error rate Low, high setting accuracy, quality-enhancing effect
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[0033] The present invention will be described in detail below in conjunction with specific embodiments.
[0034] A method for setting process parameters of glass tempering based on fuzzy BP neural network, comprising:
[0035](1) Obtain several initial samples, each initial sample includes the glass type of a batch of glass, the thickness of the glass, the number of glass blocks in a heat, the total area of the glass, and the corresponding process parameters.
[0036] In this embodiment, the initial samples are extracted from a large amount of historical data. The number of initial samples in this embodiment is 180. Historical data is input in txt format. In order to ensure that it can be input and extract initial samples, the historical data must meet specific format restrictions. In this embodiment, all historical data are input in txt format, and then the initial samples are directly extracted from the historical data in txt format through corresponding extraction code...
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