Modeling hysteresis using hybrid method of continuous transformation and neural networks
Modeling hysteresis using hybrid method of continuous transformation and neural networks
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DOI：
10.1016/j.sna.2004.09.019
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发表时间：
2005-03
影响因子：
4.6
通讯作者：
Zhao Tong;Yonghong Tan;Xianwen Zeng
中科院分区：
文献类型：
--
作者：
Zhao Tong;Yonghong Tan;Xianwen Zeng
提出了一种新的、简单的迟滞非线性建模方法。利用连续变换技术构造了一个基本迟滞模型（Elementary Hysteresis Model，EHM），该模型在迟滞非线性的输入空间和输出空间之间形成了一对一的关系。从理论上讲，我们可以将EHM的输出作为一个普通的神经网络（NN）的输入信号之一，以逼近任何类型的磁滞非线性，它满足任何输入信号满足一个假设。为了验证所提出的方法的有效性，我们使用几组数据，从建议的基于反冲的迟滞仿真模型（BHSM）分别进行仿真测试。然后，一组真实的数据测量被用来评估所提出的方法。仿真测试结果表明，该方法简单、有效。
A novel and simple approach to modeling hysteresis nonlinearities is proposed. The continuous transformation technique is used to construct an elementary hysteresis model (EHM), which forms a one-to-one relation between the input space and the output space of hysteresis nonlinearities. In theory, we can apply the output of the EHM as one of the input signals of a common neural network (NN) to approximate any kind of hysteresis nonlinearities, which meet any input signals satisfying an assumption. In order to validate the effectiveness of the proposed approach we use several sets of data from the proposed backlash-based hysteresis simulation models (BHSMs) for respective simulation testing. Then a set of real data measurements is used to evaluate the proposed approach. These results of simulation testing indicate that the proposed approach is simple and successful.