During the past decades, fuzzy logic control (FLC) has been one of the most active and fruitful areas for research in the application of fuzzy set theory. It has has been an active research topic in automation and control theory, since the work of Mamdani proposed in 1974 based on the fuzzy sets theory of Zadeh (1965), to deal with the system control problems which is not easy to be modeled [Ma...

A method for selecting the best service for the storage of information by Mamdani.

Linear and Non-linear distortion influenced data transmission rate in communication system. In the presence of White Gaussian Noise linear distortion occurs in form of intersymbol interference (ISI) and co-channel interference (CCI). Amplifiers, modulator and demodulator subsystems are caused for Non-linear distortions along with nature of the medium. Different techniques are used to equalized ...

It is well-known that the choice of membership functions is a key problem in the design of a fuzzy controller. The aim of this paper is thus to give a further contribution in this direction. In particular, the performance of the Mamdani-type fuzzy controller with piece wise linear membership functions is discussed in details, taking into account features such as overlapping, completeness level,...

In this article, the specification of a histopathology decision making support system, based on Pawlak’s information system concept and Mamdani type fuzzy control is presented. The proposed system supports the recognition process of HER-2/neu histopathology preparations through microscopy image information analysis. We used Pawlak’s information system to identify the decisive set of features an...

This paper implements a Neuro-Fuzzy (FNN) approach to autonomously navigate a car-like robot in an unknown environment. The applied technique allows the robot to avoid obstacles and locally search for a path leading to the goal after learning and adaptation. It is based on two Fuzzy Artmap neural networks, a Reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC)...

We introduce a fuzzy controller which uses a fuzzy rule base diierently to the classical Mamdani approach. We argue that it has several desirable and well-motivated properties which often cannot be obtained by a Mamdani controller. Let X and Y denote the input and the output space, respectively, and let F(:) denote the collection of all fuzzy subsets. The support of a fuzzy set A 2 F(X) is Supp...

The database of a rule-based systemmay contain imprecisionswhich appear in the description of the rules given by the expert. Because such an inference can not be made by the methods which use classical two valued logic or many valued logic, Zadeh in (Zadeh, 1975) and Mamdani in (Mamdani, 1977) suggested an inference rule called "compositional rule of inference". Using this inference rule, sever...

compositional rule of inference to a fuzzy input X* E F ( X ) we obtain a fuzzy output Y* E F(Y): We propose an enhancement of MamY * = X * o T R , (1) dani fuzzy controllers. We tested it on l.e., a simple control task and verified that it outperforms the traditional approach. VY E Y : Y*(Y) = SUPT(X*(X),R(X,Y)) , (2) xEX

In this paper, we propose a generalized fuzzy inference system (GFIS) in noise image processing. The GFIS is a multi-layer neuro-fuzzy structure which combines both Mamdani model and TS fuzzy model to form a hybrid fuzzy system. The GFIS can not only preserve the interpretability property of the Mamdani model but also keep the robust local stability criteria of the TS model. Simulation results ...