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.