Application of BP Neural Networks on Prediction of Operating Condition of Loom
ISCID), 2010 International Symposium（2010）
摘要
In order to forecast quickly the operating condition of the loom, optimize the parameters of loom production, so that the production efficiency of loom will be improved. This paper studies the prediction of the operating condition of the loom based on the neural networks. The neural networks technology is applied to forecast the operating condition of the loom production, establishes corresponding prediction model of loom production. With the help of neural networks samples are trained and checked, then are applied to forecast the operating condition of the loom production, the results are compared with the Bayesian theorem. The study indicates that network model based on the neural networks has reliability and high accuracy.
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关键词
production equipment,neural network,bayesian theorem,production efficiency,high accuracy,corresponding prediction model,bayes methods,prediction,paper study,backpropagation,neural networks sample,bp neural networks,loom production efficiency,neural networks technology,operating condition,network model,neural nets,loom operating condition prediction,bp neural network,predictive models,artificial neural networks,bayesian methods,mathematical model,operant conditioning,prediction model,production
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