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A multi-step predictive control algorithm based on least squares support vector machines (LS-SVM) model for complex systems with strong nonlinearity is presented. The nonlinear offline model of the controlled plant is built by LS-SVM with the radial basis function (RBF) kernel. Based on LS-SVM multi-step predictive outputs, the real process multi-step predictive outputs are expanded into Taylor series...
In order to identify the inverse model for nonlinear dynamic systems, a multiple support vector machines (MSVM) based method was presented. According to their differential orders for the dynamic system, the input and output variables were allocated into multiple calculational subspaces. Taking advantage of its nonlinear regression performance, each subspace was represented by the least squares support...
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