Identification for nonlinear systems modelled by deep long short-term memory networks based Wiener model

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.