Yarn Quality Prediction for Small Samples Based on AdaBoost Algorithm

In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,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 comparison.The prediction experiments of the yarn evenness and the yarn strength were implemented.Determination 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 generalization ability of these models.In 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,respectively.The determination coefficient of the yarn strength prediction result of the AdaBoost model is slightly higher than that of the other two models.Considering 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 models.In 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 sizes.It is proved that the AdaBoost model not only has good prediction accuracy but also has good prediction stability and generalization ability for small samples.

Research on the Application of Reinforcement Learning Model in Vocational Education System

Vocational education can effectively improve the vocational skills of employees,improve people’s traditional concept of vocational education,and focus on the training of vocational skills for students by using new educational methods and concepts,so that they can master key vocational skills and develop key abilities.In this paper,three different learning models,Deep Knowledge Tracing(DKT),Dynamic Key-Value Memory Networks(DKVMN)and Double Deep Q-network(DDQN),are used to evaluate the indicators in the vocational education system.On the one hand,the influence of learning degree on the performance of the model is compared,on the other hand,the performance evaluation of three models under the same learning effect is compared,so as to obtain the best learning model applied to the field of skill training.In order to accurately evaluate the learning status of students,the loss function curves under three models are compared.Finally,the error rate of students in vocational skills education tends to be zero,and the learning process of intensive learning effectively improves students’mastery of skills and key abilities.