基于扩展Preisach神经网络的通用磁滞辨识
Universal Hysteresis Identification Using Extended Preisach Neural Network
摘要：
在物理学和工程科学的不同分支中都观察到了滞后现象。因此，在不同的领域，已经提出了几种模型来进行磁滞模拟，但几乎没有一种模型可以通用。本文从基于Madelungs规则的Preisach模型出发，借鉴Preisach神经网络的思想，利用神经网络的学习能力，提出了一种自适应的通用迟滞模型--扩展Preisach神经网络模型。它由输入、输出和两个隐藏层组成。输入和输出层包含线性神经元，而第一隐藏层包含称为恶化停止神经元的神经元，其激活函数遵循恶化停止算子。恶化停止操作符可以生成非全等滞后回线。第二隐藏层包括S形神经元。添加第二个隐藏层，有助于神经网络非常平滑地学习非Masing和非对称磁滞回线。在输入层，除了输入数据之外，还包括输入数据改变的速率，以便给予模型依赖于学习率的磁滞回线的能力。因此，所提出的方法具有模拟速率无关和速率相关的滞后与全等或非全等的循环，以及对称和非对称的循环的能力。采用一种新的混合算法对模型进行训练，该算法是基于遗传算法和空间扩张次梯度优化方法相结合的。所提出的模型的通用性进行了评估，将其应用到各种滞后从不同领域的工程具有不同的特点。结果表明，该模型是成功的识别所考虑的故障。
英文摘要：
Hysteresis phenomena have been observed in different branches of physics and engineering sciences. Therefore, several models have been proposed for hysteresis simulation in different fields; however, almost neither of them can be utilized universally. In this paper by inspiring of Preisach Neural Network which was inspired by the Preisach model that basically stemmed from Madelungs rules and using the learning capability of the neural networks, an adaptive universal model for hysteresis is introduced and called Extended Preisach Neural Network Model. It is comprised of input, output and, two hidden layers. The input and output layers contain linear neurons while the first hidden layer incorporates neurons called Deteriorating Stop neurons, which their activation function follows Deteriorating Stop operator. Deteriorating Stop operators can generate non-congruent hysteresis loops. The second hidden layer includes Sigmoidal neurons. Adding the second hidden layer, helps the neural network learn non-Masing and asymmetric hysteresis loops very smoothly. At the input layer, besides input data the rate at which input data changes, is included as well in order to give the model the capability of learning rate-dependent hysteresis loops. Hence, the proposed approach has the capability of the simulation of both rate-independent and rate-dependent hysteresis with either congruent or non-congruent loops as well as symmetric and asymmetric loops. A new hybridized algorithm has been adopted for training the model which is based on a combination of the Genetic Algorithm and the optimization method of sub-gradient with space dilatation. The generality of the proposed model has been evaluated by applying it to various hysteresis from different areas of engineering with different characteristics. The results show that the model is successful in the identification of the considered hystereses.
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