Prediction of oxygen consumption in steelmaking based on LAOA-TSVR

To solve the issue of oxygen consumption forecasting, the researchers suggested a twin support vector machine for regression (LAOA-TSVR) prediction model based on an improved arithmetic optimization algorithm. The model has beneficial generalization, high prediction accuracy, and the ability to jump out of the local optimum and other characteristics. The group used the method of mechanism analysis to determine the main influencing factors of oxygen consumption. To confirm the model’s prediction effect, it is compared to the Back Propagation, Radial Basis Function, and Twin Support Vector Regression prediction models. The LAOA-TSVR oxygen consumption forecasting prediction model was then tested on actual steel mill production. The test phase consisted of 200 production cycles, and the results revealed that the LAOA-TSVR model had an 85,1 % hit rate for oxygen consumption within 5 m3/t. The model can suit the actual needs of predicting oxygen consumption in steel

Wind turbine gearbox condition monitoring based on class of support vector regression models and residual analysis

The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold–Mariano and Durbin–Watson tests are carried out to establish the robustness of the tested models

Supplier selection with support vector regression and twin support vector regression

Tedarikçi seçimi sorunu son zamanlarda literatürde oldukça ilgi görmektedir. Güncel literatür, yapay zeka tekniklerinin geleneksel istatistiksel yöntemlerle karşılaştırıldığında daha iyi bir performans sağladığını göstermektedir. Son zamanlarda, destek vektör makinesi, araştırmacılar tarafından çok daha fazla ilgi görse de, buna dayalı tedarikçi seçimi çalışmalarına pek sık rastlanmamaktadır. Bu çalışmada, tedarikçi kredi endeksini tahmin etmek amacıyla, destek vektör regresyon (DVR) ve ikiz destek vektör regresyon (İDVR) teknikleri kullanılmıştır. Pratikte, tedarikçi verisini içeren örneklemler sayıca oldukça yetersizdir. DVR ve İDVR daha küçük örneklemlerle analiz yapmaya uyarlanabilir. Tedarikçilerin belirlenmesinde DVR ve İDVR yöntemlerinin tahmin kesinlikleri karşılaştırılmıştır. Gerçek örnekler İDVR yönteminin DVR yöntemine kıyasla üstün olduğunu göstermektedir.Suppliers’ selection problem has attracted considerable research interest in recent years. Recent literature show that artificial intelligence techniques achieve better performance than traditional statistical methods. Recently, support vector machine has received much more attention from researchers, while studies on supplier selection based on it are few. In this paper, we applied the support vector regression (SVR) and twin support vector regression (TSVR) techniques to predict the supplier credit index. In practice, the suppliers’ samples are very insufficient. SVR and TSVR are adaptive to deal with small samples. The prediction accuracies for SVR and TSVR methods are compared to choose appropriate suppliers. The actual examples illustrate that TSVR methods are superior to SVR

Extending twin support vector machine classifier for multi-category classification problems

© 2013 – IOS Press and the authors. All rights reservedTwin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems. TWSVM not only overcomes the difficulties in handling the problem of exemplar unbalance in binary classification problems, but also it is four times faster in training a classifier than classical support vector machines. This paper proposes one-versus-all twin support vector machine classifiers (OVA-TWSVM) for multi-category classification problems by utilizing the strengths of TWSVM. OVA-TWSVM extends TWSVM to solve k-category classification problems by developing k TWSVM where in the ith TWSVM, we only solve the Quadratic Programming Problems (QPPs) for the ith class, and get the ith nonparallel hyperplane corresponding to the ith class data. OVA-TWSVM uses the well known one-versus-all (OVA) approach to construct a corresponding twin support vector machine classifier. We analyze the efficiency of the OVA-TWSVM theoretically, and perform experiments to test its efficiency on both synthetic data sets and several benchmark data sets from the UCI machine learning repository. Both the theoretical analysis and experimental results demonstrate that OVA-TWSVM can outperform the traditional OVA-SVMs classifier. Further experimental comparisons with other multiclass classifiers demonstrated that comparable performance could be achieved.This work is supported in part by the grant of the Fundamental Research Funds for the Central Universities of GK201102007 in PR China, and is also supported by Natural Science Basis Research Plan in Shaanxi Province of China (Program No.2010JM3004), and is at the same time supported by Chinese Academy of Sciences under the Innovative Group Overseas Partnership Grant as well as Natural Science Foundation of China Major International Joint Research Project (NO.71110107026)