background : drivers are vulnerable to musculoskeletal and psychological disorders because of substantially harmful agents in this stressful occupation. this study aims to investigate the influence of driver’s physical and psychological health on the risk of road accidents using fuzzy logic approach. methods : two input variables including musculoskeletal disorders (msds) and mental health, alo...

Noise is source of ambiguity for fuzzy systems. Although being an important aspect, the effects of noise in fuzzy modeling have been little investigated. This paper presents a set of tests using three well-known fuzzy modeling algorithms. These evaluate perturbations in the extracted rule-bases caused by noise polluting the learning data, and the corresponding deformations in each learned funct...

allows the user to define rules on contraindication alerts, drug interaction warnings, diagnostic and In the near future, every medical intreatment recommendations, etc. formation system wiil be equipped with a function that provides knowledge-based decision support. Data-driven knowledgebased decision support consists, in the first place, of a medical data-to-concept conversion step and, secon...

Fuzzy modeling has many advantages over the non-fuzzy methods, such as robustness against uncertainties and less sensitivity to the varying dynamics of nonlinear systems. Data-driven fuzzy modeling needs to extract fuzzy rules from the input/output data, and train the fuzzy parameters. This paper takes advantages from deep learning, probability theory, fuzzy modeling, and extreme learning machi...

The TSK model introduced by Takagi Sugeno and Kang TSK fuzzy reasoning is associated with fuzzy rules that have a special format with a func tional type consequent instead of the fuzzy consequent that normally appears in the MamdamiModel In this way the TSK approach tries to decompose the input space into subspaces and then approximate the system in each subspace by a simple linear regression m...

A special method for fuzzy-logic-based system modeling is presented to develop multi-variable fuzzy models on the basis of the system's measured input and output data. The global modeling problem is proposed to be solved in two steps: by fuzzy model connguration and fuzzy model identiication. The fuzzy model connguration procedure is characterized by preliminary considerations leading to the de...

The dynamic behavior of an industrial tunneltype furnace is of the fundamental importance for the quality of the processed products. The knowledge of the furnace dynamics can make the control of the system easier as well as it can eliminate certain operational problems. The various subsystems existent in a tunnel-type furnace are usually controlled by conventional techniques and independent con...

A technique for the modeling of nonlinear control processes using fuzzy modeling approach based on the Takagi–Sugeno fuzzy model with a combination of genetic algorithm and recursive least square is proposed. This paper discusses the identification of the parameters at the antecedent and consequent parts of the fuzzy model. For the antecedent fuzzy parameters, genetic algorithm is used to tune ...

In this paper, we used the fuzzy set theory for modeling flexible constraints and uncertain data in nurse scheduling problems and proposed a fuzzy linear model for nurse rostering problems. The developed model can produce rosters that satisfy hospital objectives, ward requirements and staff preferences by satisfying their requests as much as possible. Fuzzy sets are used for modeling demands of...

the main purpose of this paper is to present a fuzzy multi-criteria decision making (fmadm) model for appropriate location selection of a health center. therefore, we identify sixteen criteria and sub-criteria for selecting a health center location. these criteria and sub-criteria have been obtained from literature reviews and practical interviews. this paper proposes a method which combines th...