Aiming at the limitations of rapid fault diagnosis of blast furnace,a novel strategy based on cost-conscious least squares support vector machine(LS-SVM)is proposed to solve this problem.Firstly,modified discrete particle swarm optimization is applied to optimize the feature selection and the LS-SVM parameters.Secondly,cost-conscious formula is presented for fitness function and it contains in detail training time,recognition accuracy and the feature selection.The CLS-SVM algorithm is presented to increase the performance of the LS-SVM classifier.The new method can select the best fault features in much shorter time and have fewer support vectors and better generalization performance in the application of fault diagnosis of the blast furnace.Thirdly,a gradual change binary tree is established for blast furnace faults diagnosis.It is a multi-class classification method based on center-of-gravity formula distance of cluster.A gradual change classification percentage is used to select sample randomly.The proposed new method raises the speed of diagnosis,optimizes the classification accuracy and has good generalization ability for fault diagnosis of the application of blast furnace.
Considering strip steel surface defect samples,a multi-class classification method was proposed based on enhanced least squares twin support vector machines(ELS-TWSVMs)and binary tree.Firstly,pruning region samples center method with adjustable pruning scale was used to prune data samples.This method could reduce classifier′s training time and testing time.Secondly,ELS-TWSVM was proposed to classify the data samples.By introducing error variable contribution parameter and weight parameter,ELS-TWSVM could restrain the impact of noise samples and have better classification accuracy.Finally,multi-class classification algorithms of ELS-TWSVM were proposed by combining ELS-TWSVM and complete binary tree.Some experiments were made on two-dimensional datasets and strip steel surface defect datasets.The experiments showed that the multi-class classification methods of ELS-TWSVM had higher classification speed and accuracy for the datasets with large-scale,unbalanced and noise samples.
