Fuzzy set theory, originally developed by Lotfi Zadeh in the 1960’s, has become a popular tool for control applications in recent years (Zadeh, 1965). Fuzzy control has been used extensively in applications such as servomotor and process control. One of its main benefits is that it can incorporate a human being’s expert knowledge about how to control a system, without that a person need to have...

In this paper, we use a method based on a multi-objective genetic algorithm, namely the Pareto Archived Evolutionary Strategy (PAES), to generate a set of Mamdani fuzzy systems from numerical data. PAES determines an approximation of the optimal Pareto front by concurrently maximizing the accuracy and minimizing the complexity. Unlike other approaches, we measure the complexity as sum of the va...

In this paper we present a new approach for reliable fall detection. The fuzzy system consists of two input Mamdani engines and a triggering alert Sugeno engine. The output of the first Mamdani engine is a fuzzy set, which assigns grades of membership to the possible values of dynamic transitions, whereas the output of the second one is another fuzzy set assigning membership grades to possible ...

Protection is the most significant area of power system indeed. For single phase LT consumers, electromechanical B – Type MCB is available nowadays. Many significant workouts on various breakers have been found with excellent features like VCB, SF6 and so on. In the present paper a brief discussion is made on protection against over current scenario due to low fault impedance. The purpose of th...

An explainable artificial intelligence (XAI) agent is an autonomous that uses a fundamental XAI model at its core to perceive environment and suggests actions be performed. One of the significant challenges for these agents performing their operation efficiently, which governed by underlying inference optimization system. Along similar lines, Explainable Fuzzy AI Challenge (XFC 2022) competitio...

This study presents a novel controller of magnetic levitation system by using new neuro-fuzzy structures which called flexible neuro-fuzzy systems. In this type of controller we use sliding mode control with neuro-fuzzy to eliminate the Jacobian of plant. At first, we control magnetic levitation system with Mamdanitype neuro-fuzzy systems and logical-type neuro-fuzzy systems separately and then...

In this paper, analytical structures of TITO (two-input two-output) Mamdani fuzzy PI/PD controllers are investigated with respect to conventional PI/PD control and variable gain control. Components of the fuzzy controllers include two input fuzzy sets for each input variable, five singleton output fuzzy sets for each output variable, 16 fuzzy rules, product AND fuzzy logic operator, the Mamdani...

Thanks to their ability to model natural language, fuzzy rules are very popular in expert knowledge representation. Mamdani fuzzy systems are widely used for process simulation or control. Nevertheless, fuzzy implicative rules, and especially gradual rules, provide another kind of knowledge representation, which can be very useful in approximate reasoning. In this paper, the two types of rules ...

There is the specific situation in secondary schools regarding students' understanding of the importance of acquired knowledge which they need for the continuation of their education. Therefore, nowadays it is necessary to introduce innovative forms of knowledge acquiring such as blended learning. The intention is to build an adequate model using Mamdani Fuzzy Logic in terms of better motivatio...

In this paper, we derive new neuro-fuzzy structures called flexible neuro-fuzzy inference systems or FLEXNFIS. Based on the input-output data, we learn not only the parameters of the membership functions but also the type of the systems (Mamdani or logical). Moreover, we introduce: 1) softness to fuzzy implication operators, to aggregation of rules and to connectives of antecedents; 2) certaint...