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In this article, a constraint factor is introduced into the hysteretic operator so that the hysteretic operator can pass through the origin in every minor coordinate system. Based on the hysteretic operator, an expanded input space is constructed. And then, it is proved that the mapping between the expanded input space and the output space contains only one-to-one mapping and multiple-to-one mapping, which can be identified using the traditional methods of neural networks. Finally, a neural network is employed to model hysteresis for the magnetostrictive sensors and actuators. Two experiments are implemented to validate the neural hysteresis model. The experimental results demonstrate that the proposed approach is effective.