Study Of PET Industry Soft Sensor Based On Support Vector Machine

Statistical Learning Theory (SLT) is a small-sample statistics which concerns mainly the statistic principles when samples are limited, especially the properties of learning procedure in such cases SLT provides us a new framework for the general learning problem and let the most exist methods to be used. Support Vector Machine is a kind of powerful learning method which put Statistical Learning Theory to use. It is based on structure risk minimization (SRM), use the risk function to minimize experiential risk and trust scope. It has many advantages, such as using kernel function to avoid local minimal point, sparse nature of solutions, limit used to control capacity or the number of support vectors, etc. It is believed that the study of SVM is becoming a new direction after neural network.In the paper support vector machine actuality and development is discussed particularly from such four sides: theory study of support vector machine, algorithm improvement, choosing kernel function and parameters, support vector machine expander. Standard support vector machine is used to model some important quality index of polyester industry offline. Two methods, increment learning and least square changing threshold to set a...