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This paper proposes a new factorized f-step radial basis function network (FS-RBF) model for model predictive control (MPC). The strategy is to develop a f-step predictor for nonlinear dynamic systems and implement it with a RBF network. In contrast to the popular NARX-RBF model, the developed FS-RBF model is capable of making a designated sequence of future output prediction without requiring the...
An adaptive structure radial basis function (RBF) network model is proposed in this paper to model nonlinear processes with operating point migration. The recursive orthogonal least squares algorithm (ROLS) is adopted to select new centers on-line, as well as to train the network weights. Based on the R matrix in the orthogonal decomposition, an initial center bank is formed and updated in each sample...
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