This paper is concerned with modeling and identification methodology for practical nonlinear system via deep long short-term memory (DLSTM) networks-based Wiener model. To determine the unknown parameters and simplify parameters identification procedure for the Wiener model, a two-step identification scheme is implemented applying hybrid signals involving separable signal and random data. First, the separable signal to separate the two blocks in Wiener model is imported, then parameters of the linear block is estimated using correlation function-based least squares technique, which handles the issue that the intermediate variable information in Wiener model cannot be measured. Moreover, integrating the advantages of adaptive stochastic gradient descent algorithm and root mean square propagation, an adaptive momentum estimation technique is created to optimize the DLSTM networks parameters based on available random data, which improves the accuracy of identified Wiener model. Compared with the other existing schemes, the superiority of the proposed method in terms of predictive performance and control results are illustrated by permanent magnet synchronous motors.

中文翻译：

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基于维纳模型的深度长短期记忆网络建模的非线性系统的辨识


本文关注通过基于深度长短期记忆（DLSTM）网络的维纳模型对实际非线性系统进行建模和识别的方法。为了确定未知参数并简化维纳模型的参数识别过程，应用涉及可分离信号和随机数据的混合信号实现了两步识别方案。首先导入维纳模型中分离两个块的可分离信号，然后利用基于相关函数的最小二乘技术估计线性块的参数，解决了维纳模型中中间变量信息无法测量的问题。此外，结合自适应随机梯度下降算法和均方根传播的优点，创建了一种自适应动量估计技术，基于可用的随机数据来优化DLSTM网络参数，提高了维纳模型识别的准确性。与其他现有方案相比，该方法在预测性能和控制结果方面的优越性通过永磁同步电机得到了体现。