摘要
为了提高气路参数偏差值预测精度,首先建立了时变模糊推理系统;同时,为了解决模糊推理系统因参数众多而难以实际应用的问题,建立了时变模糊神经网络,并给出了该网络的学习算法。采用Mackey-Glass时间序列对时变模糊神经网络的预测精度进行验证,并将其应用到发动机排气温度偏差值预测中。应用实例表明,时变模糊神经网络能更好地预测排气温度偏差值的变化趋势,为发动机预诊断提供支持。
To obtain a better accuracy, the time-varying fuzzy inference system theory was established, and a time-va- rying fuzzy neural network was created to solve the problem that the application was hard realized by too much pa- rameters of the fuzzy inference system. The learning algorithm of the network structure was also designed. Mackey- Glass chaotic time series prediction was used to prove the network prediction accuracy, and the time-varying fuzzy inference system theory was used to predict the exhaust gas temperature deviation. The result showed that the bet- ter variation trend was predicted by proposed network, which could provide support for pre-diagnosis of aero-en- gine.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2014年第4期919-925,共7页
Computer Integrated Manufacturing Systems
基金
国家863计划资助项目(2012AA040911)
国家自然科学基金资助项目(51305096)
民航局科技计划资助项目(MHRD201122)~~
关键词
时变模糊推理系统
时变模糊神经网络
航空发动机
排气温度预测
time-varying fuzzy inference system
time-varying fuzzy neural network
aero-engine
exhaust gas tem-perature prediction