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摘要
Aiming at the large distribution errors in traditional chemicals distribution systems for dyeing machines, a multi-layer fully connected neural network model was proposed based on the prediction of recommended pre-stop value and predicted time. The network model was firstly trained using data recorded in the distribution process, and the data to be distributed was fed into the trained network model for calculation so as to obtain the recommended pre-stop value and predicted time. The recommended pre-stop value and empirical pre-stop value were used to obtain the final pre-stop value according to the variable ratio algorithm. The system worked to determine the closing time of the distribution valve according to the final pre-stop value. The predicted time was used to evaluate whether the distribution process was timed out. Four pre-stop modes were used for chemicals distribution experiments over 1 000 times, and the results show that the standard deviation of distribution error predicted by network model is 23.8 g, the mean absolute error is 16.1 g. It is superior to the other three pre-stop modes with better chemical distribution accuracy. ? 2022 China Textile Engineering Society.
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