Hysteretic recurrent neural networks: a tool for modeling hysteretic materials and systems
Hysteretic recurrent neural networks: a tool for modeling hysteretic materials and systems
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DOI：
10.1088/0964-1726/18/7/075004
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发表时间：
2009-07-01
影响因子：
4.1
通讯作者：
Buckner, Gregory D.
中科院分区：
文献类型：
--
作者：
Veeramani, Arun S.;Crews, John H.;Buckner, Gregory D.
本文介绍了一种新的递归神经网络，滞后递归神经网络（HRNN），这是理想的滞后材料和系统的建模。该网络采用了一个滞后神经元组成的联合S形激活功能。虽然类似的滞后神经元已被探索以前，HRNN是独特的，在其利用简单的递归“自我选择”相关的激活功能。此外，通过将网络权重放置在输出侧来促进训练，从而允许使用误差训练算法的标准反向传播。我们提出了两个和三相版本的HRNN建模滞后材料具有不同的阶段。这些模型进行了实验验证，使用从形状记忆合金和铁磁材料收集的数据。结果表明，HRNN的能力，准确地概括滞后行为与相对较少数量的神经元。另外的好处在于网络能够通过分析单个神经元的权重来识别关于宏观材料的统计信息。
This paper introduces a novel recurrent neural network, the hysteretic recurrent neural network (HRNN), that is ideally suited to modeling hysteretic materials and systems. This network incorporates a hysteretic neuron consisting of conjoined sigmoid activation functions. Although similar hysteretic neurons have been explored previously, the HRNN is unique in its utilization of simple recurrence to 'self-select' relevant activation functions. Furthermore, training is facilitated by placing the network weights on the output side, allowing standard backpropagation of error training algorithms to be used. We present two- and three-phase versions of the HRNN for modeling hysteretic materials with distinct phases. These models are experimentally validated using data collected from shape memory alloys and ferromagnetic materials. The results demonstrate the HRNN's ability to accurately generalize hysteretic behavior with a relatively small number of neurons. Additional benefits lie in the network's ability to identify statistical information concerning the macroscopic material by analyzing the weights of the individual neurons.