The magnetic shape memory alloy (MSMA) is a new family of smart materials, which exhibits great strain deformation and high energy density. Based on these properties, the MSMA has excellent potential to represent an available means for developing a novel generation of actuators in the micro-positioning application. However, the MSMA-based actuator suffers from the inherent hysteresis and it has become a bottleneck in the industrial application. A hybrid hysteresis model, which consists of a simple dynamic hysteresis operator (SDHO) and chaotic neural network (CNN), is proposed in this article. This developed model possesses a concise construction and distinguished generalization capability. By conducting comparative experiments, the proposed approach has a superior ability to predict the hysteresis behaviors under various input signals.

中文翻译：

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磁性形状记忆合金驱动器的基于混沌神经网络的动态算子滞后建模

磁性形状记忆合金（MSMA）是一种新型智能材料，具有很大的应变变形和高能量密度。基于这些特性，MSMA具有极好的潜力，可以代表在微定位应用中开发新一代致动器的可用方法。但是，基于MSMA的执行器具有固有的磁滞现象，并且已成为工业应用中的瓶颈。本文提出了一种混合滞后模型，该模型由一个简单的动态滞后算子（SDHO）和混沌神经网络（CNN）组成。这种开发的模型具有简洁的构造和出色的泛化能力。通过进行比较实验，所提出的方法具有在各种输入信号下预测磁滞行为的出色能力。