磁滞效应（Effect of magnetic stranded），指磁场可以把铁块变成磁铁，此后即使磁场减弱或消失，铁块的磁力并不会回到原来的起点或零点，部分磁力将永久性地滞留在铁块之中。
本文采用的神经网络预测的方法能有效的改善磁滞效应所产生的问题。
The simulation results also indicate that the neural network predictive can effectively solve the problem induced by hysteresis.
本文对MR阻尼器的固有特性磁滞效应对控制效果的影响进行了研究。
Magnetic hysteresis is the intrinsic characteristic of MR dampers, which will have more serious effect on the performance of MR dampers.
所提出的方法将永磁材料包括局部回线在内的磁滞特性包含到数学模型中，在合理的假设下，考虑了计及磁滞效应的“旋转磁化”。
The rotational limit line was introduced to the rotational hardening law and a parameter b was added to the model to reflect the evolution of anisotropy.
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