In order to solve the problems of weak prediction stability and generalizationability of a neural network algorithm model in the yarn quality prediction research for smallsamples,a prediction model based on an AdaBo...
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In order to solve the problems of weak prediction stability and generalizationability of a neural network algorithm model in the yarn quality prediction research for smallsamples,a prediction model based on an AdaBoost algorithm(AdaBoost model) was established.A prediction model based on a linear regression algorithm(LR model) and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model) were established for *** prediction experiments of the yarn evenness and the yarn strength were *** coefficients and prediction errors were used to evaluate the prediction accuracy of these models,and the K-fold cross validation was used to evaluate the generalizationability of these *** the prediction experiments,the determination coefficient of the yarn evenness prediction result of the AdaBoost model is 76% and 87% higher than that of the LR model and the MLP model,*** determination coefficient of the yarn strength prediction result of the AdaBoost model is slightly higher than that of the other two *** that the yarn evenness dataset has a weaker linear relationship with the cotton dataset than that of the yarn strength dataset in this paper,the AdaBoost model has the best adaptability for the nonlinear dataset among the three *** addition,the AdaBoost model shows generally better results in the cross-validation experiments and the series of prediction experiments at eight different training set sample *** is proved that the AdaBoost model not only has good prediction accuracy but also has good prediction stability and generalizationability for smallsamples.
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