粗糙集及PSO优化BP网络的故障诊断研究

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 proposes a new diesel engine fault diagnosis model that based on rough set theory and the improved BP neural network., and introduces rough set (RS), particle swarm optimization (PSO) and genetic algorithm (GA) into the diesel engine fault diagnosis. The algorithm uses self-organization mapping net (SOM) to discretize the continuous attributes, and the 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 feasibility, rapidity, veracity of the algorithm.