Constructing Interval Type-2 Fuzzy Systems (IT2FS) with Memetic Algorithm: Elucidating Performance with Noisy Data

Abstract

AbstractFuzzy modeling is a challenging task and becomes more complex when designing T2FS, which requires identification of more parameters as compared to T1FS. The problem of fuzzy modeling can be expressed as a high-dimensional search and optimization process, and EAs have the ability to search for optimal solutions in high-dimensional search space, so researchers used various EAs for fuzzy modeling. GAs are widely used for finding solutions in large search spaces, and MAs have characteristics of both global and local optimizations. This paper describes how to use MAs and GAs to identify IT2FS, including how to build MFs for both input and output, as well as how to generate a rule base from a data collection. The efficiency of T1FS and IT2FS for noisy data is also compared with GAs and MAs in the paper. For comparison, we consider four different problems: a rapid Ni–Cd battery charger, data from Box and Jenkins’s gas furnace, and the iris and wine classification datasets. In the presence of noise, the results imply that IT2FS is more efficient than T1FS, and MAs are more efficient than GAs.KeywordsFuzzy modelingT1FSIT2FSMemetic algorithm (MAs)Genetic algorithm (GAs)