This paper proposes an optimal design for interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic system. In this method, the fuzzy c-means clustering algorithm is used to determine structure of fuzzy rule as well as number of rules. A hybrid between chaos firefly algorithm and genetic algorithms (CFGA) is developed, which is used to find the desirable parameters of membership functions and conseq...

Uncertainty is an inherent part in controllers for real world applications. In this paper we compare the performance differences between type-1 and interval type-2 fuzzy logic (IT2FLC) controllers, with five and three term membership functions. The controllers are used to control a PM DC motor in a closed loop real time system. The performance of system with each controller to a step is recorde...

A fuzzy controller for the closed loop control, by insulin infusion of glycaemia in type 1 diabetic patients is proposed. The controller uses type-2 fuzzy sets. The controller was tested in simulation using a complex nonlinear model of the glucose metabolism. Simulation results confirm the effectiveness and the robustness of the type-2 fuzzy logic controller. The design of the controller uses a...

In this paper we developed a Type-2 Fuzzy Logic System (T2FLS) in order to model a batch biotechnological process. Type-2 fuzzy logic systems are suitable to drive uncertainty like that arising from process measurements. The developed model is contrasted with an usual type-1 fuzzy model driven by the same uncertain data. Model development is conducted, mainly, by experimental data which is comp...

A statistical analysis of Type-1 and Interval Type-2 Fuzzy Logic in dynamic parameter adaptation in the Bee Colony Optimization algorithm (BCO) is presented in this paper. The Bee Colony Optimization meta-heuristic belongs to the class of Nature-Inspired Algorithms. The objective of the work is based on the main reasons for the analysis of the approach with Interval Type-2 Fuzzy Logic to find t...

In this paper, we discuss set operations on type-2 fuzzy sets (including join and meet under minimum=product t-norm), algebraic operations, properties of membership grades of type-2 sets, and type-2 relations and their compositions. All this is needed to implement a type-2 fuzzy logic system (FLS). c © 2001 Elsevier Science B.V. All rights reserved.

Intelligent congestion control is vital in encoded video streaming of a clip or film, as network traffic volatility requires constant adjustment of the bit-rate. Equation-based solutions to congestion control are prone to fluctuations in the delivery rate and may respond only when packet loss has already occurred, while both fluctuations and packet loss affect the end-user’s perception of the d...

Type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-base fuzzy logic systems. However, they are difficult to understand for a variety of reasons which we enunciate. In this paper, we strive to overcome the difficulties by: 1) establishing a small set of terms that let us easily communicate about type-2 fuzzy sets and also let us define such sets very precisely, 2) p...

fuzzy logic, 67algebraic semantics, 53approximate reasoning, 6axiomatic extensions of MTL, 56 Bellman, R., 21bipolar possibilistic logic, 93BL logic, 51 canonical chain, 66canonical completeness, 66canonical extension, 83Carnap, R., 3comparative truth, 63compositionality, 29computational complexity, 73core fuzzy logic, 58 De Finetti B., 3degre...