Abstract
We describe in this paper an overview of new methods that we have been working on for building intelligent systems for pattern recognition using type-2 fuzzy logic and soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including type-1 fuzzy logic, neural networks, and genetic algorithms, which can be used to create powerful hybrid intelligent systems. In this paper, we are reviewing the use of a higher order fuzzy logic, which is called type-2 fuzzy logic. Combining type-2 fuzzy logic with traditional SC techniques, we are able to build powerful hybrid intelligent systems that can use the advantages that each technique offers in solving pattern recognition problems.
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Melin, P. (2013). Interval Type-2 Fuzzy Logic in Hybrid Neural Pattern Recognition Systems. In: Seising, R., Trillas, E., Moraga, C., Termini, S. (eds) On Fuzziness. Studies in Fuzziness and Soft Computing, vol 299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35644-5_1
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DOI: https://doi.org/10.1007/978-3-642-35644-5_1
Publisher Name: Springer, Berlin, Heidelberg
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