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BLACK-BOX MODELLINGOF HVAC SYSTEM: IMPROVING THE PERFORMANCES OF NEURAL NETWORKS

机译:黑箱式型HVAC系统:提高神经网络的性能

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This paper deals with neural networks modelling of HVAC systems. In order to increase the neural networks performances, a method based on sensitivity analysis is applied. The same technique is also used to compute the relevance of each input. To avoid the prediction errors in dry coil conditions, a metamodel for each capacity is derived from the neural networks. The regression coefficients of the polynomial forms are identified through the use of spectral analysis. These methods based on sensitivity and spectral analysis lead to an optimized neural network model, as regard to its architecture and predictions.
机译:本文涉及HVAC系统的神经网络建模。为了增加神经网络性能,应用了一种基于灵敏度分析的方法。相同的技术也用于计算每个输入的相关性。为了避免干燥线圈条件中的预测误差,每个容量的元模型来自神经网络。通过使用光谱分析来识别多项式形式的回归系数。基于灵敏度和光谱分析的这些方法导致了优化的神经网络模型,如其架构和预测。

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