Description: presents a RBF network nonlinear dynamic system modeling online resource optimization network (RON) method. RON resources distribution network in the learning process of introducing a sliding window and on-line optimization of the network structure of thinking, so that the network can According to the most recent information within the error automatically optimizing the network structure, so that the RBF network can meet the targets of the online changes also enable the network to maintain the small-scale level, and to ensure that the network generalization ability. Use sliding window technology allows RON learning parameters is robust, and easier to convergence. 3 standard examples demonstrate the effectiveness of the algorithm.
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