title: Gaussian neural networks for glass bottles inspection: a learning procedure.
abstract: In glass bottle inspection, the defects detection is of first importance. For online system detection, high speed and robust detection of faults are highly required. Neural networks have recently, and successfully, been applied to fault detection in many manufacturing processes. In this study, a Gaussian neural network, an extension of the RBF network, trained through a competitive algorithm, has been chosen for fault detection. Four parameters extracted from images of the bottles are used as inputs of the network. The number of Gaussian units is adjusted by an informational criterion. Experimental results show that the performance of this network are better than classical parametric and non parametric classifiers.
authors.affiliation: Centre d'Automatique de Lille, Université des Sciences et Technologies de Lille, Villeneuve d'Ascq, France.
authors.forename: C
authors.initials: C
authors.lastname: Firmin
authors.forename: D
authors.initials: D
authors.lastname: Hamad
authors.forename: J G
authors.initials: JG
authors.lastname: Postaire
authors.forename: R D
authors.initials: RD
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D003658:Decision Making, Computer-Assisted; D005898:Glass; D007091:Image Processing, Computer-Assisted; D007858:Learning; D016571:Neural Networks, Computer; D016011:Normal Distribution; D011786:Quality Control