Design of fault detection observer based on new-type neural networks

摘要：

　　In this paper,the convex combination algorithm (CCA) is proposed to optimize new-type neural networks.This method updates the weights by iterating to massage the information in the hidden layer.And a new error function is set up to measure the performance of the neural networks.The optimized parameters can be obtained by decoupling the weights,which improves the calculating speed of the parameters.On the basis,a fault detection and diagnosis method is proposed based on the observer for the nonlinear modeling ability of neural network.Finally,this method is applied to the nonlinear systems,and the sensitivity of the neural networks fault detection observer to nonlinear systems failure is proved by simulation.

关键词： Observer Fault detection Feedforward neural networks Convex combination algorithm

作者: WEN Xin ZHANG Xingwang ZHANG Wenhao

作者单位: Faculty of Aerospace Engineering Shenyang Aerospace University Shenyang, China

会议类型: 国际会议

会议名称: 2015 Fifth International Conference on Instrumentation and Measurement,Computer,Communication and Control (IMCCC2015)(第五届仪器测量、计算机通信与控制国际会议)

会议地点: 秦皇岛

会议语种:英文

页码: 326-329

在线出版日期: 2015-09-18（万方平台首次上网日期，不代表论文的发表时间）