Determining firing strengths through a novel similarity measure to enhance uncertainty handling in non-singleton fuzzy logic systems
College of Computing
Non-singleton Fuzzy Logic Systems have the potential to tackle uncertainty within the design of fuzzy systems. The inference process has a major role in determining results, being partly based on the interaction of input and antecedent fuzzy sets (in generating firing levels). Recent studies have shown that the standard technique for determining firing strengths risks substantial information loss in terms of the interaction of the input and antecedents. To address this issue, alternative approaches, which employ the centroid of intersections and similarity measures, have been developed. More recently, a novel similarity measure for fuzzy sets has been introduced, but as yet this has not been used for non-singleton fuzzy logic systems. This paper focuses on exploring the potential of this new similarity measure in combination with the similarity based inferencing approach to generate a more suitable firing level for non-singleton input. Experiments are presented for fuzzy systems trained using both noisy and noise-free time series. The prediction results of non-singleton fuzzy logic systems for the novel similarity measure and the current approaches are compared. Analysis of the results shows that the novel similarity measure, used within the similarity based inferencing approach, can be a stable and suitable method to be used in real world applications.
IJCCI 2017 - Proceedings of the 9th International Joint Conference on Computational Intelligence
Determining firing strengths through a novel similarity measure to enhance uncertainty handling in non-singleton fuzzy logic systems.
IJCCI 2017 - Proceedings of the 9th International Joint Conference on Computational Intelligence, 83-90.
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