基于输入空间扩张的动态迟滞神经网络模型  

Neural Network Model for the Dynamic Hysteresis Based on the Expanded Input Space

作　　者：张新良[1] 谭永红[2] 

机构地区：[1]上海交通大学电子信息学院自动化系,上海200030 [2]上海师范大学机电学院,上海2018141

出　　处：《自动化学报》2009年第3期319-323,共5页Acta Automatica Sinica

基　　金：国家自然科学基金(60572055);上海师范大学重点学科项目(DZL811);上海市教育委员会科研创新项目(09ZZ141)资助~~

摘　　要：针对神经网络不能直接用于辨识具有多值映射特征的迟滞非线性的不足,利用输入空间扩张的方法,引入动态迟滞算子来反映动态迟滞的速率依赖性,由迟滞的输入、输入变化率和算子输出构造神经网络的扩张输入空间,将输出空间的迟滞多值映射转换为在新的扩张输入空间上的一一映射,从而将神经网络应用到动态迟滞非线性的辨识中.所建立模型结构简单,易于实现在线调整.最后,使用该方法对压电陶瓷执行器中的动态迟滞进行了辨识.To solve the problem that the neural networks cannot directly approximate the nonlinear hysteresis which is characterized by multi-valued mapping, a new expanded input space is proposed in this paper. A dynamic hysteretic operator is constructed to represent the rate-dependent characteristic of the dynamic hysteresis. Then with the introduction of the input variable, input change-rate and dynamic hysteretic operator, a new expanded space is constructed. Hence, the multi-valued mapping of the dynamic hysteresis can be transformed into a one-to-one mapping on this expanded input space. Then, the neural networks are implemented for the identification of the dynamic hysteresis based on the proposed expanded input space. The proposed model is simple in structure and available for online updating to adapt to the environmental changes. Finally, the application of the proposed approach to the identification of a piezoelectric actuator is presented.

关 键 词：迟滞 多值映射 速率依赖性 动态迟滞算子 神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]