An Improved Simplified Support Vector Machines And Its Application In The Soft-sensing Of Purification Process

Support vector machine (SVM) proposed by Vapnik is a novel machine learning method based on the statistic learning theory. The structure risk minimization and kernel method are proposed to solve the problems with limited samples, non-linearity, high-dimension of feature space perfectly. However, the large computing cost and uncertain kernel method and standard uncertainty of parameter optimization don't contribute to the development of SVM. Hence, the researchers have studied it for these years.Firstly, the concept of Machine learning, the development of Statistic Learning Theory and the main principles of SVM are introduced, and the reduction method based on clustering is compared with the method based on structure of SVM. Aimed at the large computing costs of SVM,2 steps Pre-extraction Support Vectors (2s-PSV) method, an improved method, is proposed. The sample space is divided into sample blocks by the given scale, then the blocks containing abnormal samples and redundant samples are rejected while the ones having candidate samples are selected. Next, the extended boundary samples picked by relative boundary distance consititute the final candidate sample set. The 2s-PSV method is suitable for the sample set with any type of distribution, and high in reduction ratio and training precision.Secondly, using the 2s-PSV method and introducing the linear hybrid kernel function, an Improved Simplified Support Vector Machine (IS-SVM) is proposed. The training samples are mapped to a high dimension feature space by the combination kernel. Then, the samples are reduced by the 2s-PSV method. The hybrid kernel consisting of Gaussian kernel and linear kernel improves the generalization and interpolation of IS-SVM. And 2s-PSV method cuts the redundant samples and extracts the candidate samples, increases the calculation accuracy and decreases the computing time of IS-SVM. To prove the effectiveness of IS-SVM, SVM and IS-SVM are tested respectively by big multi-dimensional data sets from UCI Machine Learning Repository. The comparison shows that the IS-SVM is higher in training precision and lower in computational complexity.Finally, IS-SVM is applied to the soft-sensing model of cadmium ion concentration in purification process of zinc hydrometallurgy. The simulation results show that the precision of IS-SVM satisfies the requirement of industrial production.