In this paper, a learning algorithm based on soft consistency and completeness conditions is proposed. This learning algorithm combines in a single process rule ...
In this paper, a learning algorithm based on soft consistency and completeness conditions is proposed. This learning algorithm combines in a single process rule ...
May 16, 1998 · The completeness and consistency conditions were introduced in order to achieve acceptable concept recognition rules.
An iterative approach for developing fuzzy classifiers is proposed. The initial model is derived from the data and subsequently, feature selection and rule base ...
This paper focuses on the design of a multiobjective genetic algorithm that properly considers all these properties thus ensuring an effective search space ...
Jul 30, 2024 · Maintaining Consistency in Fuzzy Rule Bases · Consistency ensures that there are no contradictory or conflicting rules in the rule base.
Abstract-A general method is developed to generate fuzzy rules from numerical data. This new method consists of five steps: Step 1 divides the input and output ...
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Flexibility, completeness and consistency are essential for fuzzy systems to exhibit an excellent performance and to have a clear physical.
Oct 22, 2024 · Inconsistencies between the fuzzy rules must be dealt with in order to generate accurate results (Herrera and Martínez, 2000). ... ... The ...
Abstract— This paper proposes Pitts-DNF-C, a multi- objective Pittsburgh-style Learning Classifier System that evolves a set of DNF-type fuzzy rules for ...