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 design and optimization process of fuzzy controllers can be supported by learning techniques derived from neural networks. Such approaches are usually called neuro-fuzzy systems. In this paper, we describe the application of an updated version of the neuro-fuzzy model NEFCON to a real plant. The NEFCON model is able to learn and optimize the rulebase of a Mamdani-type fuzzy controller onlin...

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

A dynamic positioning (DP) system is a computer-controlled which maintains the and heading of ship by means active thrust. DP consist sensors, observer, controller thrust allocation algorithm. The purpose this paper to investigate performance proportional derivative type fuzzy with Mamdani interface scheme for an oceanographic research vessel (ORV) numerical simulation. Nonlinear passive observ...

defuzzifier circuit is one of the most important parts of fuzzy logic controllers that determine the output accuracy. the center of gravity method (cog) is one of the most accurate methods that so far been presented for defuzzification. in this paper, a simple algorithm is presented to generate triangular output membership functions in the mamdani method using the multiplier/divider circuit and...

This work outlines a new approach for online learning from imprecise data, namely, fuzzy set based evolving modeling (FBeM) approach. FBeM is an adaptive modeling framework that uses fuzzy granular objects to enclose uncertainty in the data. The FBeM algorithm is data flow driven and supports learning on an instance-per-instance recursive basis by developing and refining fuzzy models on-demand....

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 ...

A control law based on fuzzy logic features was developed and validated for an anaerobic wastewater treatment (AWT) process. The controlled variable was the concentration of Volatile Fatty Acids (VFA) in the anaerobic reactor and the manipulated variable was the input flow rate. In order to use it as the input in a Mamdani-inference fuzzy set, the controlled variable was treated using an algori...

In this paper Genetic Algorithm based Fuzzy Logic Controller for temperature control in a plastic extrusion is developed and tested through a simulation study. A novel GA based FLC method is implemented to design a practicable advanced controller. Manifest feature of the proposed method is smoothing of undesired control signal of mamdani type FLC controller. Plastic extrusion system is generall...