The fundamental focus of the work is based on the basic reasons for the need for optimization of type-2 fuzzy systems for different areas of application.

May 25, 2019 · In this paper we perform a comparison of using type-2 fuzzy logic in two different bio-inspired methods: Ant Colony Optimization (ACO) and ...

In this review, we consider the application of genetic algorithms, particle swarm optimization and ant colony optimization as three different paradigms that ...

This chapter presents a general framework for designing interval type-2 fuzzy controllers (FCs) based on bio-inspired optimization techniques.

In this paper we perform a comparison of using type-2 fuzzy logic in two different bio-inspired methods: Ant Colony Optimization (ACO) and Gravitational ...

IT2FLC based on bio-inspired optimization methods always outperforms T1FLC under different scenarios where it shows high stability, and it can dampen out the ...

The fundamental focus of the work is based on the basic reasons for the need for optimization of type-2 fuzzy systems for different areas of application.

The fundamental focus of the work has been based on the basic reasons of the need for optimizing type-2 fuzzy systems for different areas of application.

This chapter presents a general framework for designing interval type-2 fuzzy controllers based on bio-inspired optimization techniques.

Abstract: A new approach for the Bee Colony Optimization algorithm (BCO) for Type-1 and Type-2 Fuzzy Controller design is presented in this paper.