A neural-network-based inverse hysteresis model
基于神经网络的迟滞逆模型

A new neural-network-based inverse hysteresis model is proposed in this paper. The continuous transformation technique is used to construct an elementary inverse hysteresis operator (EIHO), which extracts the elementary information of inverse hysteresis. The output of the EIHO is then used as one of the input signals of the neural network (NN) so that the multi valued mapping of inverse hysteresis is transformed into a one-to-one mapping. In this way, neural networks can be used to model inverse hysteresis. A set of real data is also used to validate the effectiveness of the proposed approach. Finally, simulation results indicate that the proposed approach is successful.