PSO-RBF神经网络在舵机系统故障诊断中的应用

Application of RBF Neural Network Based on PSO Algorithm in Fault Diagnosis of Actuation System

文章建立了基于RBF神经网络的故障观测器模型,提出了一种将粒子群优化算法（PSO）与正则化正交最小二乘法（ROLS）相结合的2级RBF学习方法,并将该RBF网络观测器应用于导弹舵机系统的故障诊断。实验结果表明,基于该RBF神经网络的故障观测器能够有效地实现导弹舵机系统的故障检测。

In this paper,the failure observer based on RBF neural network was developed,and a two-level learning method for designing radial basis function（RBF） network based on particle swarm optimization（PSO） and regularized orthogonal least squares（ROLS） was proposed.Finally,the RBF observer was applied to fault diagnosis of the missile＇s actuation system.The experimental results showed that the failure observer based on the RBF neural network was effective in detecting the failure of the missile＇s actuation system.