Abstract: To obtain a better accuracy,the time-varying fuzzy inference system theory was established,and a time-varying fuzzy neural network was created to solve the problem that the application was hard realized by too much parameters 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 better variation trend was predicted by proposed network,which could provide support for pre-diagnosis of aero-engine.

Key words: time-varying fuzzy inference system, time-varying fuzzy neural network, aero-engine, exhaust gas temperature prediction

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。

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