Abstract
A BP neural network approach based on differential evolution and particle swarm optimization is developed for fault diagnosis of gear-box. The approach takes a novel kind of optimization algorithm, i.e., differential evolution and particle swarm optimization algorithm, to train BP neural work. The feasibility and effectiveness of this new approach is validated and illustrated by the study cases of fault diagnosis on gear-box. The diagnosis results show that the BP neural network based on differential evolution and particle swarm optimization has a better training performance, better convergence behavior, as well as better diagnosis ability than BP neural network. Owing to the flexibility of this approach, it can be adapted to other problems very easily.
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