Abstract
In this paper we show the performance of an Interval Type-2 Fuzzy Inference System as a method to estimate the relevance of each module in a Modular Neural Network for images recognition. The aggregation operator used to make the integration of the simulation matrices is Sugeno Integral, and the output of the inference system are the fuzzy densities to calculate the fuzzy λ measures. Although this integration method was tested for image recognition, is possible to adapt it for distinct applications, which need information fusion of sources with uncertain relevance.
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Mendoza, O., Melín, P., Licea, G. (2008). Interval Type-2 Fuzzy Logic for Module Relevance Estimation in Sugeno Integration of Modular Neural Networks. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70812-4_7
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DOI: https://doi.org/10.1007/978-3-540-70812-4_7
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