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A classification technique based on radial basis function neural networks
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AI
DOI：10.1016/j.advengsoft.2005.07.005.png)
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In this paper, a new classification method is proposed based on the radial basis function (RBF) neural network architecture. The method is particularly useful for manufacturing processes, in cases where on-line sensors for classifying the product quality are not. available. More specifically, the fuzzy means algorithm is employed on a set of training data, where the input data refer to variables that are measured on-line and the output data correspond to quality variables that are classified by human experts. The produced neural network model acts as an artificial sensor that is able to classify the product quality in real time. The proposed method is illustrated through an application to real data collected from a paper machine. The method produces successful results and outperforms a number of classifiers, which are based on the feedforward neural network (FNN) architecture. (c) 2005 Elsevier Ltd. All rights reserved.
Keyword:
neural networks
classification
quality properties
radial basis functions
fuzzy means
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IF:
5.7
论文数:
3.4K
被引数:
1.2W
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