The method of transformation of a nonlinear mathematical model of an electromechanical object to the form of a modified artificial recurrent neural network has been further developed. The method makes it possible to use knowledge about the object for the synthesis of a recurrent neural network (RNN) structure and the computation of their coefficients. Nonlinearities in the proposed RNN were realized by expanding the input signal space of the network, using the normalized signals of polynomial terms. Mathematical transformations were performed for a model of thyristor-based electric drive with a dc motor of series excitation. In the electric drive model, different nonlinearities were set, namely, the magnetic flux and inductance of the motor winding dependence on the motor current and its derivative, the thyristor converter gain from the reference voltage, and the dependence of the moment of inertia on the speed. An accuracy estimation for the models in the form of an RNN was made.

中文翻译：

---

直流串联驱动非线性数学模型转化为修正循环神经网络形式的方法

将机电对象的非线性数学模型转换为改进的人工递归神经网络形式的方法得到了进一步发展。该方法可以使用有关对象的知识来合成循环神经网络 (RNN) 结构并计算其系数。所提出的 RNN 中的非线性是通过使用多项式项的归一化信号扩展网络的输入信号空间来实现的。对具有串联励磁直流电机的基于晶闸管的电力驱动模型进行了数学变换。在电驱动模型中，设置了不同的非线性，即电机绕组的磁通量和电感对电机电流及其导数的依赖关系，晶闸管转换器从参考电压获得增益，以及转动惯量对速度的依赖性。对 RNN 形式的模型进行了准确度估计。