a new approach to define the similarity measure betweeninterval-valued fuzzy sets is presented. the proposed approach isbased on a weighted measure in which the normalized similaritiesbetween lower functions and also between upper functions arecombined by a weight parameter. the properties of this similaritymeasure are investigated. it is shown that, the proposed measurehas some advantages in c...

background and objectives: evaluating the performance of clinical units is critical for effective managementof health settings. certain assessment of clinical variables for performance analysis is not always possible,calling for use of uncertainty theory. this study aimed to develop and evaluate an integrated independentcomponent analysis-fuzzy-data envelopment analysis approach to accurate the...

In this paper, we will proposed a hybrid method to generate fuzzy membership function automatically. Particle Swarm Optimization (PSO) is used as optimized algorithm, supplement the performance of fuzzy system. The PSO is able to generate an optimal set of parameter for the membership functions automatic adjustment. Fuzzy control system that automatically backs up a truck to a specified point o...

When we deal with the estimator of a parameter, it is important for us to find an estimator with minimum variance. For this reason, this paper discuss on the variance of fuzzy random variable. There are two different approaches to the definition of the variance of a fuzzy random variable. In the first one, the variance of the fuzzy random variable is defined as a crisp number by using defuzzifi...

The advantage of solving the complex nonlinear problems by utilizing fuzzy logic methodologies is that the experience or expert’s knowledge described as a fuzzy rule base can be directly embedded into the systems for dealing with the problems. The current limitation of appropriate and automated designing of fuzzy controllers are focused in this paper. The structure discovery and parameter adjus...

This research proposes a new model for constructing decision trees using intervalvalued fuzzy membership values. Most existing fuzzy decision trees do not consider the uncertainty associated with their membership values, however, precise values of fuzzy membership values are not always possible. In this paper, we represent fuzzy membership values as intervals to model uncertainty and employ the...

A neuro-fuzzy system is the combined the advance feature of fuzzy logic and neural network, it is simply a fuzzy inference system that is trained by the learning concept of neural network. In NFS learning mechanism fine-tunes the underlying fuzzy inference system. This paper presents fundamental concepts and parameterized comparison in the aspects of fuzzy logic, neural network and neuro-fuzzy ...

The main purpose of this paper is to integrate fuzzy logic control and sliding mode control techniques based on backstepping approach to develop a robust fuzzy backstepping sliding mode controller (RFBSMC) for an under-actuated quadrotor UAV system under external disturbances and parameter uncertainties. First, a robust backstepping sliding mode control for quadrotor is introduced briefly. More...

Adaptive Fuzzy Logic Systems trained by genetic evolution of their parameters are presented in this work. This technique is based on the aggregation of parameter perturbations. Neither the evaluation function nor the membership functions, have to be differentiable as required in most optimization techniques. In the classical Genetic Algorithms, the solution space of each parameter should be spe...

The entropy of intuitionistic fuzzy sets is used to indicate the degree of fuzziness of an interval-valued intuitionistic fuzzy set(IvIFS). In this paper, we deal with the entropies of IvIFS. Firstlt, we propose a family of entropies on IvIFS with a parameter λ ∈ [0, 1], which generalize two entropy measures defined independently by Zhang and Wei, for IvIFS, and then we prove that the new entro...