中文摘要：

针对传统BP算法存在的收敛速度缓慢和易陷入局部极小值的固有缺陷,提出用具有全局搜索能力的模拟退火算法优化BP神经网络,避免陷入局部极小值,提高网络的稳定性;引入Powell算法优化模拟退火算法,加快网络的收敛速度。最后,以齿轮箱故障诊断为例进行仿真试验,结果表明改进后的BP神经网络比传统BP神经网络的训练收敛速度快、精度高。

英文摘要：

The traditional BP algorithm has the inherent shortcomings such as low convergent speed and local minimum.So this paper proposes a simulated annealing algorithm with globalsearch property to optimize the BP neural network for avoiding the local minimum and improving its stability.In order to improve the convergent speed of neural network,the Powell algorithm is proposed to optimize the simulated annealing algorithm.Finally,the simulated results of gearbox fault diagnosis are presented to show that the improved BP neural network has higher convergent speed and precision than those of the traditional BP neural network.