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Calibration of online ash analyzers using neural networks

机译：使用神经网络校准在线灰分仪

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摘要

A novel form of calibration of online analyzers was implemented, Rather than simple equations, a neural network was used to model the relationship between the scintillation counts (Am and Cs) of an analyzer and the measured ash for improved online analysis of run-of-mine coal. Also, a new approach was followed to better evaluate neural network performance. Samples were first divided into various statistically different groups using a Kohonen network. Data were then selected for the training, calibration and prediction subsets using criteria developed in this paper for sparse data, with representation from each group. Back propagation-based neural network architecture was used in conjunction with quick-stop training. The predictions were very good on average, but due to noise in the data, the predictions were not good individually.
机译：实现了一种新颖的在线分析仪校准形式，而不是简单的方程式，而是使用神经网络对分析仪的闪烁计数（Am和Cs）与测得的灰分之间的关​​系进行建模，以改进运行时的在线分析。煤矿。此外，还采用了一种新方法来更好地评估神经网络性能。首先使用Kohonen网络将样本分为统计上不同的不同组。然后使用本文针对稀疏数据制定的标准为训练，校准和预测子集选择数据，每个组都有代表。基于反向传播的神经网络体系结构与快速停止训练结合使用。平均而言，预测效果非常好，但是由于数据中的噪声，单个的预测效果并不理想。

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