科研之友
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摘要
Growing and pruning radial basis GAP-RBF) is extended for identification and control of multivariable nonlinear systems in this work. The proposed MGAP-RBF algorithm utilizes a sliding data window in the growing criterion and limits the number of hidden neurons by introducing a soft constraint in the pruning strategy to reduce the effect of disturbance and to improve learning speed, respectively. The performance of the proposed method is tested through some benchmark problems, and the results show that the proposed method can gain faster speed than the original GAP-RBF method and Ran algorithm, and more importantly, it can obtain an overwhelming advantages especially for some large-scale data sets with some complex attributes. Finally, the proposed method is applied to online PID tuning on a greenhouse environment control process. Simulation results show the proposed MGAP-RBF algorithm has better performance than the traditional RBF method and the original GAP-RBF method, in particular, it is faster and provides a more compact network with reduced computational complexity than the original GAP-RBF method.
