Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 fuzzy set (T1FS) does not capture uncertainty in its manifestations when it arises from vagueness in the shape of the membership function. Such unc...

This paper reports the use of simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of interval and general type-2 fuzzy logic systems to maximize their modeling ability. The combination of simulated annealing with these models is presented in...

There are two main approaches to design a neural fuzzy system; namely, through expert knowledge, and through numerical data. While the computational structure of a system is manually crafted by human experts in the former case, self-organizing neural fuzzy systems that are able to automatically extract generalized knowledge from batches of numerical training data are proposed for the latter. Ne...

One of the important problems of robotics is the navigation of mobile robots in uncertain environments that are densely cluttered with obstacles. The control of robots using the traditional control algorithms is not satisfactory as far as the navigational accuracy and the distance and time to reach the goal are concerned, when the robot is in a complicated surrounding. One of alternative and ef...

This paper presents an automatic method to design interval type-2 fuzzy systems for load forecasting applications using a memetic algorithm. This hybridisation of a variable-length genetic algorithm and a gradient descent method allows for concurrent learning of the system’s parameters and structure in a versatile fashion. Results are presented addressing chaotic system and market-level one-day...

In this paper, a micromilling type-2 fuzzy tool condition monitoring system based on multiple AE acoustic emission signal features is proposed. The type-2 fuzzy logic system is used as not only a powerful tool to model acoustic emission signal, but also a great estimator for the ambiguities and uncertainties associated with the signal itself. Using the results of root-mean-square error estimati...

prompt detection and diagnosis of faults in industrial systems areessential to minimize the production losses, increase the safety of the operatorand the equipment. several techniques are available in the literature to achievethese objectives. this paper presents fuzzy based control and fault detection for a6/4 switched reluctance motor. the fuzzy logic control performs like a classicalproporti...

A fuzzy logic controller equipped with a training algorithm is developed such that the H∞ tracking performance should be satisfied for a model free nonlinear multipleinput multiple-output (MIMO) system with external disturbances. In this paper, a new indirect adaptive interval type-2 fuzzy controller by using fuzzy descriptions to model the plant is proposed to handle the training data corrupte...

A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the o...

Abstract: In this paper, a type-2 fuzzy adaptive controller of a Lotak-Volterra system is considered. For bettering the active system properly, the approximated error is introduced into the design of the controller. By using Lyapunov synthesis approach, we attain an adaptive law adjusting the free parameters in the controller such that the closed-loop system follows a desired trajectory. The no...