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传统支持向量机是近几年发展起来的一种基于统计学习理论的学习机器,在非线性函数回归估计方面有许多应用。最小二乘支持向量机用等式约束代替传统支持向量机方法中的不等式约束,利用求解一组线性方程得出对象模型,避免了求解二次规划问题。本文采用最小二乘支持向量机解决了航空煤油干点的在线估计问题,结果表明,最小二乘支持向量机学习速度快、精度高,是一种软测量建模的有效方法。在相同样本条件下,比RBF网络具有较好的模型逼近性和泛化性能,比传统支持向量机可节省大量的计算时间。
Traditional support vector machine is a learning machine based on statistical learning theory developed in recent years. It has many applications in nonlinear function regression estimation. The least squares support vector machine (SVM) replaces the inequality constraints in the traditional support vector machine (SVM) with equality constraints, and solves a set of linear equations to solve the quadratic programming problem. The least squares support vector machine (LSSVM) is used to solve the problem of online estimation of dry kerosene kerosene. The results show that LSSVM is fast and accurate, and it is an effective method for soft sensor modeling. Under the same sample conditions, it has better model approximation and generalization performance than RBF network, which can save a lot of computation time compared with traditional support vector machines.