Abstract
In this chapter three applications of interval type-2 fuzzy logic are considered. First, we consider the use of interval type-2 fuzzy systems in conjunction with modular neural networks for image recognition. A type-2 fuzzy system is used for feature extraction in the training data, and another type-2 fuzzy system is used to find the optimal parameters for the integration method of the modular neural network. Type-2 Fuzzy Logic is shown to be a tool to help improve the results of a neural system by facilitating the representation of the human perception. The second application involves edge detection in digital images, which is a problem that has been solved by means of the application of different techniques from digital signal processing, and also the combination of some of these techniques with type-1 fuzzy systems have been proposed. In this chapter a new interval type-2 fuzzy method is implemented for the detection of edges and the results of three different techniques for the same goal are compared. The third application, concerns the problem of stability, which is one of the more important aspects in the traditional knowledge of Automatic Control. Interval type-2 fuzzy logic is an emerging and promising area for achieving intelligent control (in this case, Fuzzy Control). In this chapter we use the Fuzzy Lyapunov Synthesis, as proposed by Margaliot, to build a Lyapunov stable type-1 fuzzy logic control system, and then we make an extension from a type-1 to a type-2 fuzzy controller, ensuring the stability on the control system and proving the robustness of the corresponding fuzzy controller.
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Castillo, O., Melin, P. (2009). Interval Type-2 Fuzzy Logic Applications. In: Bargiela, A., Pedrycz, W. (eds) Human-Centric Information Processing Through Granular Modelling. Studies in Computational Intelligence, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92916-1_9
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DOI: https://doi.org/10.1007/978-3-540-92916-1_9
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