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Affiliation of Author(s):民航学院

Journal:Hangkong Dongli Xuebao

Abstract:In the process of immune algorithm training, the affinity propagation(AP) clustering and entropy weight method were introduced, the training samples were clustered and weighted, and the weights were introduced into the calculation of the sample selection threshold in the immune algorithm to solve the problem of a fixed selection threshold in the training process, which led to over fitting of the detector in a partial area, and under-fitting of the partial area. Result showed that, when the improved immune algorithm was used for the optimization of typical nonlinear functions, the iterative performance was better than the traditional immune algorithm. In most cases it was better than the particle swarm optimization algorithm and the quantum genetic algorithm, in the case of a certain type of engine fault diagnosis. The improved algorithm had a diagnostic accuracy of 98.06%, which was higher than 92.60% of the traditional immune algorithm. © 2019, Editorial Department of Journal of Aerospace Power. All right reserved.

ISSN No.:1000-8055

Translation or Not:no

Date of Publication:2019-08-01

Co-author:Zhang, Bowen,Li Yanjun

Correspondence Author:cyy