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首页> 外文会议>Conference on Applied Mechanis, Materials and Manufacturing >Aeroengine Exhaust Gas Temperature Prediction Using Process Neural Network with Time-varying Threshold Functions
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Aeroengine Exhaust Gas Temperature Prediction Using Process Neural Network with Time-varying Threshold Functions

机译：使用过程神经网络具有时变阈值的航空发动机排气温度预测

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摘要

To predict the aeroengine exhaust gas temperature (EGT) more precisely, a process neuron with time-varying threshold function is proposed in this paper, and then the time-varying threshold process neural network model comprised of the presented process neurons is used for EGT prediction. By introducing a group of appropriate orthogonal basis functions, the input functions, the weight functions and the threshold functions of the time-varying threshold process neural network can be expanded as linear combinations of the given orthogonal basis functions, thus to eliminate the integration operation, then to simplify the time aggregation operation. The corresponding learning algorithm is also presented, and the effectiveness of the time-varying threshold process neural network model is evaluated through the prediction of EGT series from practical aeroengine condition monitoring.
机译：为了更准确地预测空气发动机排气温度（EGT），本文提出了具有时变阈值函数的过程神经元，然后将由所呈现的过程神经元组成的时变阈值处理神经网络模型用于EGT预测。通过引入一组适当的正交基函数，输入函数，重量函数和阈值函数的时变阈值过程神经网络可以被扩展为给定的正交基数的线性组合，从而消除集成操作，然后简化时间聚合操作。还提出了相应的学习算法，并且通过从实际航空发动机状态监测预测EGT系列的预测来评估时变阈值处理神经网络模型的有效性。

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