BP神经网络用于传感器故障诊断的仿真研究

在标准逆传播神经网络的基础上 ,提出了一种全新的改进逆传播神经网络 ,用来消除标准逆传播神经网络收敛速度慢和易陷入局部最小点的缺陷 ;并且设计了一个主神经网络和三个分布神经网络的结构 ,通过神经网络的在线学习 ,得到需要的参数估计。用它与传感器测得的实际值比较 ,可判断出故障 ,并且给出了仿真实例。

Simulation of Sensor Failure Detection Using BP Neural Network

This paper put forward a new extended Back-Propagation neural network based on the standard Back-Propagation neural network, which can get rid of the shortcoming of slow convergence speed and is easy to enter local maximum. A structure of a main neural network (MNN) and three decentralized neural networks (DNN) are designed. The parameter estimations are given by on-line learning of neural network. The fault can be fount out by comparing the estimators to the actual data acquired by sensors, and the examples of simulation are given.