An adaptive predictor for dynamic system forecasting

被引：53

作者：

Wang, Wilson ^{[1
]}

机构：

[1] Lakehead Univ, Dept Engn Mech, Thunder Bay, ON P7B 5E1, Canada

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2007年 / 21卷 / 02期

基金：

加拿大自然科学与工程研究理事会;

关键词：

neuro-fuzzy forecasting scheme; adaptive training; machinery condition monitoring; model uncertainty; fatigue testing;

D O I：

10.1016/j.ymssp.2005.12.008

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

A reliable and real-time predictor is very useful to a wide array of industries to forecast the behaviour of dynamic systems. In this paper, an adaptive predictor is developed based on the neuro-fuzzy approach to dynamic system forecasting. An adaptive training technique is proposed to improve forecasting performance, accommodate different operation conditions, and prevent possible trapping due to local minima. The viability of the developed predictor is evaluated by using both gear system condition monitoring and material fatigue testing. The investigation results show that the developed adaptive predictor is a reliable and robust forecasting tool. It can capture the system's dynamic behaviour quickly and track the system's characteristics accurately. Its performance is superior to other classical forecasting schemes. (c) 2006 Elsevier Ltd. All rights reserved.

引用

页码：809 / 823

页数：15

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