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A Neuro-fuzzy Coding for Processing Incomplete Data: Application to the Classification of Seismic Events |
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| Authors: | Muller Stéphanie Garda Patrick Muller Jean-Denis Crusem René Cansi Yves |
| Affiliation: | (1) Commissariat à l'Energie Atomique, Direction des Applications Militaires, B.P. 12, F–91680 Bruyères-le-Châtel;(2) Laboratoires d'Electronique Philips, 22, avenue Descartes, B.P. 15, F–94453 Limeil-Brévannes Cedex;(3) Laboratoire des Instruments et Systèmes, Université Pierre et Marie Curie, case 252, 4, place Jussieu, F–75252, Paris, Cedex 05 E-mail |
| Abstract: | This letter presents a method for modelling and processing incomplete data in connectionist systems. The approach consists in applying a neuro-fuzzy coding to the input data of a neural network. After an introduction to the different kinds of imperfections, we propose a neuro-fuzzy coding in order to take incomplete data into account. We show the efficiency of this coding on the problem of the classification of seismic events. The results show that a neuro-fuzzy coding of the inputs of a neural network increases the performance and classifies incomplete data with little affect on the results. |
| Keywords: | classification of seismic events fuzzy logic half-distributed coding incomplete data learning multi-layer perceptron |
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