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 AdaBo...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.展开更多
Words, as we know, cannot be torn apart either from the concepts they express or from the functions they perform in each communicative context. In this regard, the unity of words and concepts is considered an indispen...Words, as we know, cannot be torn apart either from the concepts they express or from the functions they perform in each communicative context. In this regard, the unity of words and concepts is considered an indispensible part of all communication. Being a great hindrance on the way to efficient scientific communication, competence in general academic vocabulary seems to be of paramount importance. The reflection of the process of scientific work can be found in the general scientific vocabulary in order to establish a convenient, rational and simple system in this register. The present work focused on the lexical dimensions of scientific discourse in the hope to illuminate the path to bring about a better understanding of one of the crucial aspects of this register which seems to be overlooked while teaching EAP to non-native learners, a pragmatically oriented system which might provide a scientifically controlled ＂tool＂, to non-native learners.展开更多
文摘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.
文摘Words, as we know, cannot be torn apart either from the concepts they express or from the functions they perform in each communicative context. In this regard, the unity of words and concepts is considered an indispensible part of all communication. Being a great hindrance on the way to efficient scientific communication, competence in general academic vocabulary seems to be of paramount importance. The reflection of the process of scientific work can be found in the general scientific vocabulary in order to establish a convenient, rational and simple system in this register. The present work focused on the lexical dimensions of scientific discourse in the hope to illuminate the path to bring about a better understanding of one of the crucial aspects of this register which seems to be overlooked while teaching EAP to non-native learners, a pragmatically oriented system which might provide a scientifically controlled ＂tool＂, to non-native learners.
