International Journal of Approximate Reasoning
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Similarity relations and fuzzy orderings
Information Sciences: an International Journal
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Generation of a probabilistic fuzzy rule base by learning from examples
Information Sciences: an International Journal
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Learning algorithms have been developed to construct fuzzy rule-based models from training data. The quality of the resulting model is affected by the decomposition of the input and output domains and by the number and precision of the training examples. This paper investigates the robustness of fuzzy models produced from training data. The objective is to analyze the effects of increasing complexity on the off-line performance of the learning algorithm and the on-line performance of the model, where the complexity is measured by the number of variables describing the problem domain and the number of rules in the model. A hierarchical model is proposed to reduce the complexity in high dimensional systems.