Abstract
This article discusses the natural evolution that took place from type-1 to type-2, and now recently is occurring from type-2 to type-3 fuzzy systems. Prof. Zadeh originally proposed the idea of type-1 fuzzy sets in 1965 and later of type-2 fuzzy in 1975. The goal was to model the uncertainty existing in the real world, and it is well known now that type-2 fuzzy models are better to handle the levels of uncertainty in the real-world, coming from noisy, dynamic, non-linear environments and systems. In addition, subjectivity that is handled by humans is also better represented by type-2 fuzzy sets. As a consequence, type-2 systems have been able to overcome type-1 fuzzy systems in many application areas, such as, intelligent control, pattern recognition, and diagnosis. More recently, we have witnessed the rise of the interval type-3 fuzzy sets and their utilization in control and identification of non-linear systems, showing better results than type-2 and type-1, so we expect that also this pattern will continue to other areas of application. In this article, the main differences among the concepts of type-3, type-2 and type-1 will be discussed and then an account of the existing applications of type-2 will be highlighted and finally, future areas of research will be outlined.
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Castillo, O., Castro, J.R., Melin, P. (2023). Interval Type-3 Fuzzy Systems: A Natural Evolution from Type-1 and Type-2 Fuzzy Systems. In: Castillo, O., Melin, P. (eds) Fuzzy Logic and Neural Networks for Hybrid Intelligent System Design. Studies in Computational Intelligence, vol 1061. Springer, Cham. https://doi.org/10.1007/978-3-031-22042-5_12
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