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- Title
基于 PSO 的发动机故障诊断算法与应用.
- Authors
谢春丽; 凌斌; 王宇超
- Abstract
In order to improve the safety of the engine, this paper uses BP neural network for engine fault diagnosis. BP neural network hidden layer nodes are chosen according to the empirical formula, and the effects of training methods are considered. By comparing the error of each scheme, the best BP neural network is determined. Because there are many problems to be optimized in BP neural network, particle swarm optimization is used to optimize BP neural network. First, the corresponding parameters in PSO as weights and thresholds of the network are extracted. Then the parameters of network evolution are set. Finally, the numerical value extracted from the particle swarm is assigned to the neural network for network training. The fault diagnosis test of two algorithms is carried out by the engine fault diagnosis platform. Comparison results showed that the learning speed and correct rate of optimized BP network using particle swarm optimization algorithm are better than no optimization BP neural network.
- Subjects
PARTICLE swarm optimization; BACK propagation; PERFORMANCE of diesel motors; ALGORITHMS; ENGINE equipment
- Publication
Forest Engineering, 2018, Vol 34, Issue 4, p96
- ISSN
1006-8023
- Publication type
Article