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非线性控制系统的支持向量机辨识建模研究
针对非线性控制系统辨识建模难的问题, 系统研究了基于支持向量机的非线性控制系统的辨识建模理论和方法,
然后利用回归型支持向量机( Support Vector Regression, SVR) 设计了一个非线性控制系统的辨识建模系统 仿真试验结果表明, SVR 具有很高的建模精度和较强的泛化能力, 从而验证了该辨识方法的有效性和先进性。-Nonlinear Control Systems Support Vector Machine Identification Modeling modeling for nonlinear control system identification difficult problem, the system studied based on support vector machine identification modeling nonlinear control systems theory and method, and then use the support vector regression machines (Support Vector Regression, SVR) designed a nonlinear control system identification modeling system simulation results showed that, SVR modeling with high accuracy and generalization ability, in order to verify the validity of the identification method and advanced.
针对非线性控制系统辨识建模难的问题, 系统研究了基于支持向量机的非线性控制系统的辨识建模理论和方法,
然后利用回归型支持向量机( Support Vector Regression, SVR) 设计了一个非线性控制系统的辨识建模系统 仿真试验结果表明, SVR 具有很高的建模精度和较强的泛化能力, 从而验证了该辨识方法的有效性和先进性。-Nonlinear Control Systems Support Vector Machine Identification Modeling modeling for nonlinear control system identification difficult problem, the system studied based on support vector machine identification modeling nonlinear control systems theory and method, and then use the support vector regression machines (Support Vector Regression, SVR) designed a nonlinear control system identification modeling system simulation results showed that, SVR modeling with high accuracy and generalization ability, in order to verify the validity of the identification method and advanced.
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非线性控制系统的支持向量机辨识建模研究.pdf