基于小波神经网络的一类非线性系统的故障检测

摘要

In this paper, an adaptive fault detection method based on wavelet neural network (WNN) is proposed for a class of nonlinear systems .Compared with BP ( Propagation Back ) neural networks , the WNN has a more generalization ability with dif-ferent activation functions .By designing adaptive state observer technique , the current state of the system is observed by using wavelet neural network observer , and it is applied to a class of nonlinear system fault detection and diagnosis .The stability of the fault detection observer for the wavelet neural network is proved by using the Lyapunov ’ s direct method.Finally, the reliability and stability of the fault detection observer for the nonlinear system are demonstrated by simulation.%针对一类非线性系统，提出一种小波神经网络（ wavelet neural network ， WNN）自适应故障检测方法。 WNN具有较强的泛化能力及不同的激活函数。通过设计自适应状态观测器技术，利用小波神经网络观测器良好的观测性能来观测系统的当前状态，并将其应用于一类非线性系统中，实现故障检测与诊断。利用Lyapunov直接方法从理论上证明了小波神经网络故障检测观测器的稳定性，仿真结果亦表明了该非线性系统故障检测观测器的可靠性和稳定性。