Preisach迟滞模型分类排序法的神经网络实现

The Neural Network Realization of Preisach Hysteresis Model Using Sorting &Taxis Method

  • 摘要: 针对非线性系统的迟滞特性开展建模研究,提出了一种Preisach模型分类排序法的神经网络实现方法,据此对压电陶瓷执行器纳米定位系统的迟滞非线性进行建模. 兼顾到迟滞的擦除特性和建模的精确度,建立BP神经网络求取收缩函数,避免了插值法求收缩函数值带来的插值误差. 实验结果表明,神经网络分类排序实现方法有效提高了Preisach模型的精度,减小了模型的误差.

     

    Abstract: The hysteresis modeling of nonlinear system is studied in this paper. A new sorting & taxis model of hysteresis is realized using neural network to describe the hysteresis of the piezoceramic actuator. Taking both the wiping-out property and the precision into consideration, a BP neural networks is proposed to solve function F. In this way the error resulting from interpolation is avoided. The modeling experiment of the piezoelectric ceramic is realized by the network. The results of experiments prove that the sorting & taxis scheme using neural network effectively improves the precision and reduces the error of the model.

     

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