The rough sets theory and evidence theory
Fundamenta Informaticae
A practical Bayesian framework for backpropagation networks
Neural Computation
Detecting the symmetry of attractors
Physica D
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Forecasting Global Temperature Variations by Neural Networks
Forecasting Global Temperature Variations by Neural Networks
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Recurrent neural networks and robust time series prediction
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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In this paper, we describe three methods based on the use of a dynamical system, the use of neural networks with rough sets, and the use of a fuzzy-logic method in order to predict the behavior of data processing extracted from a highly automated factory. The work presented here is a preliminary study done in order to develop a decision aid system for a highly automated factory in southern France (Merlin-Gerin). This factory produces low cost electrical circuit breakers in high volumes with short order delays. The aim is to predict the occurrence of ruptures in the production system where a rupture corresponds to a missed delivery date. The aim of our research is to model the global behavior of manufacturing systems, and then to correlate production tactical choices with their effects. We would like then to clarify the existing relations between some sectors of the enterprise. More precisely, the work carried out at Merlin-Gerin consists in a prediction of their nondelivery ratio, which is evaluated according to an analysis of historical data provided by production processing.