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- W2352979795 abstract "Support vector machines(SVM)are a new-generation machine learning technique based on the statistical learning theory. They can solve small-sample learning problems better by using Structural Risk Minimization in place of Experiential Risk Minimization. Moreover, SVMs can change a nonlinear learning problem in to a linear learning problem in order to reduce the algorithm complexity by using the kernel function idea. A nonlinear predictive control framework is presented, in which nonlinear plants are modeled on a support vector machine. The predictive control law is derived by a new stochastic search optimization algorithm. At last a simulation example is given to demonstrate the proposed approach." @default.
- W2352979795 created "2016-06-24" @default.
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- W2352979795 date "2003-01-01" @default.
- W2352979795 modified "2023-09-25" @default.
- W2352979795 title "Support Vector Machine Based Nonlinear Model Predictive Control" @default.
- W2352979795 hasPublicationYear "2003" @default.
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