Memristive Radial Basis Function Neural Network for Parameters Adjustment of PID Controller
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Springer International Publishing Switzerland 2014. Radial basis function (RBF) based-identification proportional integralderivative (PID) can automatically adjust the parameters of PID controller with strong self-organization, self-learning and self-adaptive ability. However, the compound controller has complex weight updating algorithm and large calculation. Memristor, applied well to the investigation of storage circuit and artificial intelligence, is a nonlinear element with memory function. Thus, it can be introduced to RBF neural network as electronic synapse to save and update the synaptic weights. This paper builds a model of memristive RBF-PID (MRBF-PID), and proposes the updating algorithm of weight upon memristance. The proposed MRBF-PID is used for the control of a nonlinear system. Its controlling effect is showed by numerical simulation experiment.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
author list (cited authors)
Li, X., Duan, S., Wang, L., Huang, T., & Chen, Y.