To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance...To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.展开更多
Classical machine learning algorithms seem to be totally incapable of processing tremendous data,while quantum machine learning algorithms could deal with big data unhurriedly and provide exponential acceleration over...Classical machine learning algorithms seem to be totally incapable of processing tremendous data,while quantum machine learning algorithms could deal with big data unhurriedly and provide exponential acceleration over classical counterparts.In this paper,we propose two quantum support vector machine algorithms for multi classification.One is the quantum version of the directed acyclic graph support vector machine.The other one is to use the Grover search algorithm before measurement,which amplifies the amplitude of the phase storing of the classification result.For k classification,the former provides quadratic reduction in computational complexity when classifying.The latter accelerates the training speed significantly and more importantly,the classification result can be read out with a probability of at least 50%using only one measurement.We conduct numerical simulations on two algorithms,and their classification success rates are 96%and 88.7%,respectively.展开更多
基金Project(52005358)supported by the National Natural Science Foundation of ChinaProject(2018YFB1307902)supported by the National Key R&D Program of China+1 种基金Project(201901D111243)supported by the Natural Science Foundation of Shanxi Province,ChinaProject(2019-KF-25-05)supported by the Natural Science Foundation of Liaoning Province,China。
文摘To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.
基金supported by the Shandong Provincial Natural Science Foundation for Quantum Science(No.ZR2021LLZ002)the Fundamental Research Funds for the Central Universities(No.22CX03005A).
文摘Classical machine learning algorithms seem to be totally incapable of processing tremendous data,while quantum machine learning algorithms could deal with big data unhurriedly and provide exponential acceleration over classical counterparts.In this paper,we propose two quantum support vector machine algorithms for multi classification.One is the quantum version of the directed acyclic graph support vector machine.The other one is to use the Grover search algorithm before measurement,which amplifies the amplitude of the phase storing of the classification result.For k classification,the former provides quadratic reduction in computational complexity when classifying.The latter accelerates the training speed significantly and more importantly,the classification result can be read out with a probability of at least 50%using only one measurement.We conduct numerical simulations on two algorithms,and their classification success rates are 96%and 88.7%,respectively.
