Industrial process multi-order inertial system open loop identification method based on deep learning

Abstract

The invention discloses an industrial process multi-order inertial system open loop identification method based on deep learning. Based on a deep learning random inactivation neural network, after a first-order inertia filtering link with inertia time constants of 30 seconds and 60 seconds and a second-order inertia filtering link with inertia time constants of 30 seconds, 60 seconds and 160 seconds are arranged at the input end of the network, an open-loop object identifier based on deep learning is formed; when identifying an open-loop object model, forward and reverse step inputs are addedto the input end of the open-loop object model, the open-loop object model outputs corresponding data, then the input data and the output data are input into the open-loop object identifier at the same time, and after the deep learning random inactivation neural network is subjected to offline training, the characteristics of the open-loop object model are effectively identified. According to themethod, the open-loop object model of the multi-order inertial system can be identified simply, conveniently and accurately, and the control quality of the system is effectively improved.

Description

technical field [0001] The invention relates to the field of industrial process control, in particular to an open-loop identification method for industrial process multi-order inertial systems based on deep learning. Background technique [0002] A common type of controlled object in industrial processes is the multi-order inertial system, which has the characteristics of large delay and large inertia. In order to obtain good control performance, it is often necessary to identify the characteristics of the object model. [0003] At present, the identification of the object model characteristics of multi-order inertial systems usually adopts the conventional least squares identification algorithm, but the conventional least squares identification algorithm needs to add more complex excitation signals, and the accuracy of the identified object model is not high. Contents of the invention [0004] The technical problem to be solved by the present invention is to overcome the ...

AI Technical Summary

Problems solved by technology

[0003] At present, the identification of the object model characteristics of the multi-order inertial system usually adopts the conventional least squares identification algorithm, but the conventional least squares identification algorithm needs to add more complex excitation signals, and the accuracy of the identified object model is not high.

Images