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...

Materials and methods Retrospective analysis of 195 patients. The fuzzy model determined a DPN degree score (0-10) by the combination of fuzzy sets derived from clinical variables (sensorial modalities and a set of DPN-related symptoms), using if-then rules to combine the inputs with the output sets (Mamdani process), with membership functions determined by a team of 4 DPN specialists. The MCA ...

The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain different genetic fuzzy rule-based systems, i.e., evolutionary algorithm-based processes to automatically design fuzzy rulebased systems by learning andror tuning the fuzzy rule b...

The charging method has a significant influence on the performance and lifetime of Rechargeable batteries. Therefore intelligent charging algorithm which can properly determine the charging current is essential. In this study, a fuzzy-logic-control-based (FLC-based) battery charger is proposed. The proposed charger takes the voltage and current of battery into account, and adjusts the pwm duty ...

The requirement to improve software productivity has promoted the research on software metrics technology. Object Oriented paradigm is the technology being used to build fault free and stupendous softwares; and to make them fault free object oriented metrics are being used. These metrics are used to identify high risk components early in the design phase and hence help us to reduce the rework a...

This paper assesses the operator functional state (OFS) based on a collection of psychophysiological (i.e., cardiovascular and EEG) and performance measures. Two types of adaptive fuzzy model, namely ANFIS (adaptivenetwork-based fuzzy inference system) and GA (genetic algorithm) based Mamdani fuzzy model, are employed to estimate the OFSs under a set of simulated process control tasks involved ...

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...

Mamdani fuzzy models have always been used as black-box models. Their structures in relation to the conventional model structures are unknown. Moreover, there exist no theoretical methods for rigorously judging model stability and validity. I attempt to provide solutions to these issues for a general class of fuzzy models. They use arbitrary continuous input fuzzy sets, arbitrary fuzzy rules, a...

This chapter discusses the foundation of neuro-fuzzy systems. First, we introduce Takagi, Sugeno, and Kang (TSK) fuzzy model [l,2] and its difference from the Mamdani model. Under the idea of TSK fuzzy model, we discuss a neuro-fuzzy system architecture: Adaptive Network-based Fuzzy Inference System (ANFIS) that is developed by Jang [3]. This model allows the fuzzy systems to learn the paramete...