In a large fuzzy rule-based system, a great dealof computation time is required for a fuzzy inference engine. A given fuzzy rule-based system is modeled as a fuzzy inference graph where each node in the graph corresponds to a relation representing a rule in the rule-based system. This paper presents algorithms to minimize the number of nodes in the graph using fuzzy operations as well as their ...

This paper presents the development and design of a graphical user interface and a command line programming toolbox for construction, edition and observation of Interval Type-2 Fuzzy Inference Systems. The Interval Type-2 Fuzzy Logic System Toolbox (IT2FLS), is an environment for interval type-2 fuzzy logic inference system development. Tools that cover the different phases of the fuzzy system ...

Fuzzy expert systems are one of the most practical intelligent models with the high potential for managing uncertainty associated to the medical diagnosis. In this paper, a fuzzy inference system (FIS) for diagnosing of acute lymphocytic leukemia in children has been introduced. The fuzzy expert system applies Mamdani reasoning model that has high interpretability to explain system results to e...

Prompt detection and diagnosis of faults in industrial systems areessential to minimize the production losses, increase the safety of the operatorand the equipment. Several techniques are available in the literature to achievethese objectives. This paper presents fuzzy based control and fault detection for a6/4 switched reluctance motor. The fuzzy logic control performs like a classicalproporti...

Introduction: The adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study we used this model in breast cancer detection. Methodology: A set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used.  First,...

Qualitative modeling may be applied when knowledge about a system is only available in linguistic form. The knowledge might be processed by a dynamic fuzzy system consisting of a rule base and an inference method modeling human reasoning. Conventional fuzzy inference methods do not consider this association to human reasoning and therefore are not suitable for the dynamic processing of linguist...

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 ...

This paper describes an implementation of a fuzzy inference engine that is part of a Hybrid Fuzzy Logic Intrusion Detection System. A data-mining algorithm is used offline to capture features of interest in network traffic and produce fuzzy-logic rules. Using an inference engine, the intrusion detection system evaluates these rules and gives network administrators indications of the firing stre...

this paper describes the development of a fuzzy decision support system (fdss) for the assessment of risk in e-commerce (ec) development. a web-based prototype fdss is suggested to assist ec project managers in identifying potential ec risk factors and the corresponding project risks. a risk analysis model for ec development using a fuzzy set approach is proposed and incorporated into the fdss....

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...