Modeling hysteresis and its inverse model using neural networks based on expanded input space method

A neural network-based approach of identification for hysteresis and its inverse model is proposed. In this method, a hysteretic operator is proposed to extract the change tendency of hysteresis. Then, an expanded input space is constructed to transform the multivalued mapping into one-to-one mapping so that the neural networks are capable of implementing identification for hysteresis. Similar to the method of modeling hystereis, an inverse hyteretic operator is proposed to construct an inverse model for hysteresis. Then the experimental results are presented to illustrate the potential of the proposed modeling technique.