极限学习机(ELM)作为一种新型神经网络, 具有学习速度快、泛化能力好等优点,但其输入权值矩阵和隐含层偏置是随机生成的, 易导致网络不稳定和偏差较大等问题。引入改进的差分进化多目标寻优算法, 获取训练误差最小时的极限学习机输入权值矩阵和隐含层偏置, 从而改进极限学习机。最后, 将优化后的极限学习机应用于方向舵系统故障诊断, 结果表明, 改进后的极限学习机具有较高的学习速度和诊断准确性。

As a new type of neural network, Extreme Learning Machine (ELM) has the advantages of fast learning speed and good generalization ability.However, due to the random generation of its input weight matrix and hidden layer bias, it is easy to cause network instability and large deviation.An Improved Differential Evolution (IDE) multi-objective optimization algorithm is introduced to obtain the input weight matrix and hidden layer bias of the ELM with the smallest training error, so as to improve the performance of it.Finally, the optimized extreme learning machine is applied to fault diagnosis of a rudder system.The results show that the improved ELM has high learning speed and diagnostic accuracy.