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Kernels are employed in support vector machines (SVM) to map the nonlinear model into a higher dimensional feature space where the linear learning is adopted. Every kernel has its advantages and disadvantages. Preferably, the dasiagoodpsila characteristics of two or more kernels should be combined. In this paper, the mathematical formulation of multiple kernel learning is given. To enhance the robust...
This paper presents a method for the identification of Hammerstein models based on support vector regression (SVR). First, the intermediate linear model was established through converting the nonlinear equations of Hammerstein to a class of linear one by the function expansion. Second, training samples for intermediate linear model were obtained by operating measured data synthetically, and coefficients...
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