Multilayer perceptron neural networks and radial-basis function networks as tools to forecast accumulation of deoxynivalenol in barley seeds contaminated with Fusarium culmorum

Food Control ◽  
2011 ◽  
Vol 22 (1) ◽  
pp. 88-95 ◽  
Author(s):  
Fernando Mateo ◽  
Rafael Gadea ◽  
Eva M. Mateo ◽  
Misericordia Jiménez
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Multilayer Perceptron ◽  
Fusarium Culmorum ◽  
Radial Basis Function Networks ◽  
Radial Basis

Classification Using Radial Basis Function Networks with Uncertain Weights

2005 ◽  
Vol 293-294 ◽  
pp. 135-142
Author(s):  
Graeme Manson ◽  
Gareth Pierce ◽  
Keith Worden ◽  
Daley Chetwynd
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Performance ◽  
Threshold Value ◽  
Radial Basis Function Networks ◽  
Training Techniques ◽  
Network Training ◽  
Training Approach ◽  
Radial Basis

This paper considers the performance of radial basis function neural networks for the purpose of data classification. The methods are illustrated using a simple two class problem. Two techniques for reducing the rate of misclassifications, via the introduction of an “unable to classify” label, are presented. The first of these considers the imposition of a threshold value on the classifier outputs whilst the second considers the replacement of the crisp network weights with interval ranges. Two network training techniques are investigated and it is found that, although thresholding and uncertain weights give similar results, the level of variability of network performance is dependent upon the training approach

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