Combined with the current problems in the maintenance of civil air conditioning system, in order to improve the efficiency of civil fault diagnosis and reduce the troubleshooting time, a fault diagnosis method based on Wolf pack algorithm was proposed to optimize BP neural network. The experimental results show that compared with the standard BP neural network, WPA-BP algorithm can effectively shorten the training time, improve the accuracy of fault diagnosis, and has practical application value.
The ICAO document clearly states the need for a fuel-saving effectiveness review and evaluation of the continuous and step-by-step flight modes of the climb and descent phases. In order to identify the flight mode efficiently, a flight mode recognition method based on DE-RFE-RF is proposed according to the movement characteristics and flight characteristics of the vertical section of the track. Firstly, the random forest recursive feature elimination (RFE-RF) algorithm was used to search for optimal feature combination packages and classifier training simultaneously. Then, differential evolution (DE) algorithm was used to automatically optimize RFE-RF parameter settings. The method guarantees both the accuracy of feature combination selection and recognition results. Experimental results showed that the method can realize the automatic recognition of climbing/descending mode.
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