This paper discusses a method for improving accuracy of fuzzy-rule-based classifiers using particle swarm optimization (PSO). Two different fuzzy classifiers are considered and optimized. The first classifier is based on Mamdani fuzzy inference system (M_PSO fuzzy classifier). The second classifier is based on TakagiSugeno fuzzy inference system (TS_PSO fuzzy classifier). The parameters of the ...

The Mamdani model [1] of Fuzzy Systems is the earliest and the most widely studied type of Fuzzy Systems. In this work, Residuated Implication (R-implication) operators have been explored for rule reduction in Mamdani-Type Fuzzy Systems with lossless inferencing.

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 paper concerns the use of fuzzy structures to model linear dynamic systems. A systematic method is proposed to generate the rules and also select the antecedent and consequent membership functions directly from the mathematical expression. The procedure is applied to the Takagi-Sugeno-Kang fuzzy structures and later adapted to the Mamdani fuzzy structures. It is shown that the Mamdani stru...

In this paper the development of a model for Mamdani type fuzzy rule-based systems using the new concept of granular computing (GrC) is presented. In this study a GrC algorithm is used to capture the required information in the form of data granules within a high dimensional complex database. The initial collection of information granules is used as a rule-base for a fuzzy inference system (FIS...

|A novel technique of designing application speci c defuzzi cation strategies with neural learning is presented. The proposed neural architecture considered as a universal defuzzi cation approximator is validated by showing the convergence when approximating several existing defuzzi cation strategies. The method is successfully tested with fuzzy controlled reverse driving of a model truck. The ...

This paper presents a study on inference in a Fuzzy Logic Controller (FLC). Inference is made up of interpretation, implication, combination and defuzzification. These last two steps can be performed in two sequences. An explanation, through approximate reasoning, on how to implement other than the welìknown Mamdani and Larsen implications is given. (Dis)advantages of several combinations of im...

Unmanned Aerial Vehicle (UAV) is inherently an unstable system and this necessitated the need of an external controller to stabilize and to control it. In this paper presents the exploration into the simulation, design and analysis of two autopilot schemes: (1) a fuzzy-PID controller and, (2) hybrid PID-Fuzzy controller for controlling pitch plane dynamics of an aircraft. The modeled fuzzy logi...

Seismic hazard assessment like many other issues in seismology is a complicated problem, which is due to a variety of parameters affecting the occurrence of an earthquake. Uncertainty, which is a result of vagueness and incompleteness of the data, should be considered in a rational way. Using fuzzy method makes it possible to allow for uncertainties to be considered. Fuzzy inference system,...