Clustering is an important tool in data mining and knowledge discovery. Fuzzy Adaptive Resonance Theory, a member of unsupervised neural networks, clusters data effectively because of applying operator AND of Fuzzy Logic. In previous studies, learning from data was ineffective when the surface of data is higher than the surface of weight vector of categories. In this paper, we propose an improv...

Abstract: Although the general sliding mode control has the robust property, bounds on the disturbances and parameter variations should be known a priori to the designer of the control system. Fuzzy logic provides an effective way to design a controller of the system with disturbances and parameter variations. Therefore, combination of the best feature of the fuzzy logic control and the sliding...

Fuzzy linear regression (FLR) model can be thought of as a fuzzy variation of classical linear regression model. It has been widely studied and applied in diverse fields. When noise exists in data, it is a very meaningful topic to reveal the dependency between the parameter h (i.e. the threshold value used to measure degree of fit) in FLR model and the input noise. In this paper, the FLR model ...

Some work has been done in the past on the estimation of reliability characteristics of Rayleigh distribution based on complete and censored samples. But, traditionally it is assumed that the available data are performed in exact numbers. However, in real world situations, some collected lifetime data might be imprecise and are represented in the form of fuzzy numbers. Thus, it is necessary to ...

Conventional control depends on the mathematical model of the plant being controlled. When this model is uncertain, intelligent controllers promise better performance. This paper presents a theoretical study on model reference adaptive fuzzy logic controller for vector controlled Permanent Magnet Synchronous Motor (PMSM) drive. In the proposed system, Fuzzy Logic Control (FLC) is used to implem...

This study presents a recurrent compensatory fuzzy neural network (RCFNN) for dynamic system identification. The proposed RCFNN uses a compensatory fuzzy reasoning method, and has feedback connections added to the rule layer of the RCFNN. The compensatory fuzzy reasoning method can make the fuzzy logic system more effective, and the additional feedback connections can solve temporal problems as...

The classical analysis of realtime systems tries to ensure that the instance of every task finishes before its absolute deadline (strict guarantee). The probabilistic approach tends to estimate the probability that it will happen. The deterministic timed behavior is an important parameter for analyzing the robustness of the system. Most of related works are mainly based on the determinism of ti...

Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm...

Most researches concerning uniform caching base their replacement decision on just one parameter. This parameter in some cases may not do well because of the workload characteristics. Some others use more than one parameter. In this case, finding the relation between these parameters and how to combine them is another problem. A number of algorithms try to combine their decision parameter with ...

A fuzzy regression model is used in evaluating the functional relationship between the dependent and independent variables in a fuzzy environment. Most fuzzy regression models are considered to be fuzzy outputs and parameters but non-fuzzy (crisp) inputs. In general, there are two approaches in the analysis of fuzzy regression models: linear-programmingbased methods and fuzzy least-squares meth...