Fuzzy modeling has great adaptability to the variations of system configuration and operation conditions. This paper investigates the fuzzy modeling of a laboratory scale system of anaerobic tapered fluidized bed reactor (ATFBR). The studied system is the anaerobic digestion of synthetic wastewater derived from the starch processing industries. The experiment was carried out in a mesophilic ATF...

FLAS (Fuzzy Linear Adaptive System) is a self-organizing fuzzy system for non-linear function identification, that uses a learning method based on clustering to generate fuzzy rules and tune their parameters. This method reduces the influence of pattern presentation order permits building prototypes with physical meaning, allows measuring the importance of each variable, and therefore reduces t...

Melt volume-flow rate (MVR) is one of the most important quality indicators of composite materials, which depends on the proportion of the component materials. This paper reports the development of a low complexity fuzzy model that describes the relation between percentage amount of multiwall carbon nanotube (MWCNT), acrylonitrile-butadiene-styrene (ABS), polycarbonate (PC) and MVR of the resul...

This paper reviews some important points of sparse rule-bases, the reason of their generation and after that three methods are presented, which allow the approximation of the missing rules with reasonable demand on computing. The delimitations and advantages of these methods are presented, too. Fuzzy systems based on a sparse rule-base do not have rules for all the possible combinations of obse...

The selection of the applied inference technique is determined primarily by the availability of the whole bulk of the rules necessary for the coverage i.e. dense character of the rule base. Having a sparse rule base (see fig. 1) in lack of the coverage in case of some observations (e.g. A, B) there are no rules whose antecedent part would overlap the observation at least partially. Therefore ap...

This paper presents an alternative inference-defuzzification algorithm for Takagi-Sugeno fuzzy systems that preserves local-model interpretation and convexity properties. The linear model in the rule consequent is saturated outside the core set of the antecedent membership functions. This allows the interpretation of the consequents of fuzzy rules as a local linearization of the model restricte...

This paper focuses on the acquisition of abstract information, i.e. information which are not analytically related to conventional physical quantities as for example the comfort. In these complex cases, we propose to use fuzzy sensors which compute and report linguistic assessment of numerical acquired values. Two methods are proposed to realize the aggregation from basic measurements. The firs...

In this paper, a Fuzzy PI Adaptive Learning controller is proposed for a flight control system to control the angle of attack of an aircraft. The proposed controller tracks the reference angle as desired by the pilot of the aircraft. The performance indices are evaluated and the corresponding value is compared with that for the conventional controllers obtained from Zigler Nichols (ZN), Tyreus ...

This paper is an extended research for a novel technique used in the pose error compensations of the robot and manipulator calibration process based on an Interval Type-2 Fuzzy error interpolation (IT2FEI) method. Robot calibrations can be classified into model-based and modeless methods. A model-based calibration method normally requires that the practitioners understand the kinematics of the ...

This paper is an extended research for a novel technique used in the pose error compensations of the robot and manipulator calibration process based on an IT2FEI (interval type-2 fuzzy error interpolation) method. Robot calibrations can be classified into model-based and modeless methods. A model-based calibration method normally requires that the practitioners understand the kinematics of the ...