A neural networks based model of inverse hysteresis

A novel and simple approach based on transformation using neural networks is proposed in this paper to model the inverse behavior of hysteresis. In this approach, a continuous transformation is used to construct an elementary inverse hysteresis operator (EIHO), which can extract the change tendency of inverse hysteresis. Then based on the EIHO, an expanded input space is constructed to transform the multi-valued mapping of inverse hysteresis into a one-to-one mapping. Based on the constructed expanded input space, a neural network is employed to approximate the inverse hysteresis. Both experiment and simulation are implemented to validate the effectiveness of the proposed approach. These results indicate that the proposed approach has derived satisfactory modeling performance.