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.
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Muller, S., Garda, P., Muller, JD. et al. A Neuro-fuzzy Coding for Processing Incomplete Data: Application to the Classification of Seismic Events. Neural Processing Letters 8, 83–91 (1998). https://doi.org/10.1023/A:1009621214099
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DOI: https://doi.org/10.1023/A:1009621214099