摘要
针对pH中和过程具有强非线性、时变性的特点,提出一种基于支持向量机的pH中和过程模型辨识方法.该方法采用结构风险最小化准则,保证网络具有较强的推广特性,通过求解凸二次规划确保网络结构全局最优化自动生成.利用支持向量机建立辨识pH中和过程辨识模型.仿真结果表明模型辨识精度高,泛化性能好,模型有效且易于实现.
Aimed at the strong non-linearity and time-varying properties in a pH neutralization process, its identification model was proposed on the basis of support vector machine. The model was based on structure risk minimization （SRM） principle and a stronger generalization ability of network could be promised. By solving a quadratic convex programming problem, a global optimization could be automatically generated. The identification model was established based on support vector machines and the simulation result of the model showed that the model obtained had high identification precision, good generalization performance, and was valid and easy to realized.
出处
《兰州理工大学学报》
CAS
北大核心
2009年第2期84-87,共4页
Journal of Lanzhou University of Technology
基金
国家自然科学基金(60572055)
关键词
PH
中和过程
支持向量机
模型
pH
neutralization process
support vector machines
model
