A hybrid neural network based modeling for hysteresis

This paper presents a hybrid neural network (NN) model for hysteresis in mechanical or piezoelectric systems. It is proved that the Preisach-type hysteresis can be transformed to the general continuous mappings such as one-to-one or multi-value-to-one mapping, which can be approximated by the neural network based universal approximators. The proposed hybrid neural model consists of two neural networks, i.e. a double-threshold neural network (DTNN) is proposed to memorize the historic information of the input; after that a multi-layer neural network (MNN) is utilized to approximate hysteresis nonlinearity based on the information stored in the DTNN.