A first attempt is made to use recently developed, non-conventional Artificial Neural Network (ANN) models with Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Adaptive Neuro-Fuzzy Interference System (ANFIS) architectures to predict the fuel flow rate of a commercial aircraft using real data obtained from Flight Data Records (FDRs) of the cruise, climb and descent phases. The training of the architectures with a single hidden layer is performed by utilising the Delta-Bar-Delta (DBD), Conjugate Gradient (CG) and Quickprop (QP) algorithms. The optimum network topologies are sought by varying the number of processing elements in the hidden layer of the networks using a trial-and-error method. An evaluation of the approximate fuel intake values against the ideal fuel intake data from the FDRs indicates a good fit for all three ANN models. Thus, more accurate fuel intake estimations can be obtained by applying the RBF-ANN model during the climb and descent flight stages, whereas the MLP-ANN model is more effective for the cruise phase. The best accuracy obtained in terms of the linear correlation coefficient is 0.99988, 0.91946 and 0.95252 for the climb, cruise and descent phase, respectively.

中文翻译：

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通过多层感知器、径向基函数和 ANFIS 人工神经网络预测商用飞机的燃油流量

首次尝试使用最近开发的具有多层感知器 (MLP)、径向基函数 (RBF) 和自适应神经模糊干扰系统 (ANFIS) 架构的非常规人工神经网络 (ANN) 模型来预测燃料流量使用从巡航、爬升和下降阶段的飞行数据记录 (FDR) 获得的真实数据对商用飞机进行评估。通过利用 Delta-Bar-Delta (DBD)、共轭梯度 (CG) 和 Quickprop (QP) 算法对具有单个隐藏层的架构进行训练。通过使用试错法改变网络隐藏层中处理元素的数量来寻求最佳网络拓扑。对来自 FDR 的理想燃料摄入数据的近似燃料摄入值的评估表明，所有三个 ANN 模型都非常适合。因此，通过在爬升和下降飞行阶段应用 RBF-ANN 模型可以获得更准确的燃料摄入量估计，而 MLP-ANN 模型在巡航阶段更有效。在爬升、巡航和下降阶段，线性相关系数获得的最佳精度分别为 0.99988、0.91946 和 0.95252。