Hysteresis Model of Piezoceramics Based on Chaotic Neural Networks

A novel G-S chaotic neural network is proposed to resolve the hysteresis model of piezoceramics.The network has three layers: input layer,hidden layer and output layer.The input layer comprises the delay link,which maks the historical input capable to affect the current response.The learning algorithm is a process of chaos optimization,which can make the network avoid the local mi-(nima) problem and false saturation phenomenon.The network can reduce the modeling error for the piezoelectric actuator of a nanometer positioning system.Experimental results proved validity of the algorithm.