In this paper, we propose an improved online self-organizing radial basis function neural network (IOS-RBF) modeling method which can dynamically add or merge hidden neuron while tuning the parameters. First, the initial center of the network is determined using the sample output clustering method, which is able to utilize the prior information contained in the output data. Second, a new neuron self-organization adjustment strategy is proposed, which is able to dynamically optimize the network structure according to the generalization ability of the network and the correlation between the nodes, and then, the network parameters are trained using the sliding window Levenberg-Marquardt (LM) algorithm to accelerate the network convergence speed. Finally, the effectiveness of IOS-RBF is demonstrated by two benchmark simulation experiments.
In this paper, a novel self-organizing radial basis function neural network (RBFNN)-based nonlinear model predictive control (RBF-NMPC) scheme is proposed. First, the RBFNN is initialized on the training data using clustering and extreme learning machine (ELM) algorithms, and it serves as a dynamic predictor of an unknown plant. In addition, an adaptive growing and merging strategy is utilized in the neural network so that the RBFNN can automatically adjust its structure. Second, an improved Levenberg-Marquardt (LM) algorithm with a fixed time window is used to increase convergence speed while tuning network parameters. Then, the optimal control signal is calculated by gradient method. Finally, the validity of the developed method is demonstrated by a simulation of continuous stirred tank reactor system.
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