• 人工智能 •

粗糙集及PSO优化BP网络的故障诊断研究

1. (重庆大学自动化学院　重庆400030)
• 出版日期:2018-12-01 发布日期:2018-12-01

Fault Diagnosis Research by Rough Set Theory and the PSo-BP Neural Network

• Online:2018-12-01 Published:2018-12-01

Abstract: For the imperfections of BP network fault diagnosis model, including the complexity of the network structure, the long time of training, and the low precision, this article introduced rough set(RS) , particle swarm optimization(PSO) and genetic algorithm(GA) into the diesel engine fault diagnosis, then proposed a new algorithm that is based on rough set theory and the improved 13P neural network. hhe algorithm uses self-organization mapping net(SOM) to discretize the continuous attributes, rough set theory to make a reduction on the properties for characteristic parameters,and the particle swarm optimization(PSO) to optimize the BP network structure,so that it can shorten training time and improve the accuracy of fault diagnosis effectively. Finally, the result of the diesel engine's diagnosis proves the fcasibilily, rapidity, veracity of the algorithm.

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