Generalized inverse matrix - long short-term memory neural network data processing algorithm for multi-wavelength pyrometry

Abstract

The data processing of multi-wavelength pyrometry (MWP) is faced with the problem of solving N equations and N+1 unknown underdetermined equations. The traditional iterative optimization methods are difficult to meet the actual measurement requirements in terms of accuracy and efficiency. With the development of artificial intelligence technology in the field of data processing, it is expected to solve this problem. A generalized inverse matrix (GIM) is combined with a long short-term memory (LSTM) neural network algorithm for data processing of MWP is proposed, which emissivity influence is dispensed completely. Firstly, GIM is used for classification of the emissivity. Furthermore, inputting to the LSTM network not only ensures the accuracy of temperature measurement but also greatly improves the efficiency. The simulation results demonstrated that the accuracy of the GIM-LSTM algorithm was superior to that of the GIM-EPF and BP methods. After random noise was added, the relative error was still less than that for the GIM-EPF and BP methods, and the algorithm exhibited excellent anti-noise performance. Publicly available temperature data for the exhaust plume of a rocket engine were processed by the GIM-LSTM method, and the average relative error was less than the traditional method. Especially, in terms of inversion speed, the operational time of the GIM-LSTM algorithm was at the millisecond level, which is of great significance for the real-time monitoring of rocket exhaust plumes. The proposed GIM-LSTM data processing algorithm affords high accuracy and speed and is suitable for practical measurement of high-emissivity objects in real-time via MWP.