The improved quantum genetic algorithm applied in the intelligent fault diagnosis of neural network

Aiming at the defects of BP neural network, analyzed the disadvantages of the genetic algorithm and the common quantum genetic algorithm. Used the diversity of population and the rapidity of convergence of the real-number coded double chain quantum genetic algorithm which was combined with BP neural network to modify the weights and thresholds of the neural network, and the modified neural network was applied in the intelligent fault diagnosis of bearing. The results of simulation and prediction of the example showed that this method is good effect.