【摘要】 水质系统是一个开放的、复杂的、非线性动力学系统,具有时变复杂性,针对水质预测方法的研究虽然已经取得了一些成果,但也存在预测精度与计算复杂度等难题。为此,本文提出一种基于最小二乘支持向量回归的水质预测算法。支持向量机是机器学习中一种常用的分类模型,通过核函数将非线性数据从低维映射到高维空间,在高维空间实现线性分类和回归,最小二乘支持向量回归(LS-SVR)利用所有的样本参与回归拟合,使得回归的损失函数不再只与小部分支持向量样本有关,而是由所有样本参与学习修正误差,提高预测精度;同时该算法将标准SVR求解问题由不等式的约束条件及凸二次规划问题转化成线性方程组来求解,提高了运算速度,解决了非线性复杂特性的水质预测问题。更多还原

【Abstract】 The water quality system is an open, complex, and nonlinear dynamic system with time-varying complexity. Although some achievements have been made in the research of water quality prediction methods, there are still some difficulties such as prediction accuracy and computational complexity. Therefore, this paper proposes a water quality prediction algorithm based on least squares support vector regression. Support vector machine(SVM) is a kind of commonly used machine learning classification model, nonlinear data are mapped from low-dimensional space to high-dimensional space through the kernel function, linear classification and regression are realized in the high dimensional space, the least squares support vector regression(LS-SVR) uses all samples to participate in regression fitting, which makes the regression loss function be no longer only related to a small number of support vector samples, but all samples participate in learning to correct error and improve the prediction precision. At the same time, by this algorithm, the standard SVR solving problem is transformed from inequality constraint conditions and convex quadratic programming problem into solving linear equations, which increases operation speed and solves the water quality prediction problem with nonlinear complex characteristics.更多还原