nowadays, the problem of the flexibility in any organization has essential importance and the associated investigation with the organizational efficiency and performance is considered. in real-world situation, the process of decision making is often based on linguistic and mental data, so applying fuzzy logic in modeling at this point of view is convenient. in this paper, a fuzzy approach is ap...

The railway transportation planning under the fuzzy environment is investigated in this paper. As a main result, a new modeling method, called minimum risk chance-constrained model, is presented based on the credibility measure. For the convenience ofs olving the mathematical model, the crisp equivalents ofc hance functions are analyzed under the condition that the involved fuzzy parameter...

air conditioning is widely applied for the improvement of standard of living in human life. this present investigation focused on the prediction of indoor air quality characteristics of air conditioned car using fuzzy logic algorithm. the conditioned space was selected and the experiments were planned as per design of experiments to study the effect of human load, fresh air supply and air veloc...

a new approach for pole placement of nonlinear systems using state feedback and fuzzy system is proposed. we use a new online fuzzy training method to identify and to obtain a fuzzy model for the unknown nonlinear system using only the system input and output. then, we linearized this identified model at each sampling time to have an approximate linear time varying system. in order to stabilize...

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, the risk fact...

In this paper, we present a fuzzy logic integration to the Hidden Markov Models (HMM). We have replaced the basic arithmetic operators by some adequate k z y operators. Using fuzzy operators permits us to relax the additivity constraint of probability measures. So we will need only the monotonicity with respect to set inclusion. Some results show the interest of our approach.

This paper presents a new method for fuzzy system reliability analysis based on fuzzy semi-markov model with fuzzy transitions. The definition and the basic equation for interval transitions of a semi-markov model with fuzzy transitions are provided. The transitions for the above model with fuzzy states is provided for the non-homogeneous case. We apply this fuzzy technique to ATM transactions ...

This study is an investigation of fuzzy linear regression model for crisp/fuzzy input and fuzzy output data. A least absolutes deviations approach to construct such a model is developed by introducing and applying a new metric on the space of fuzzy numbers. The proposed approach, which can deal with both symmetric and non-symmetric fuzzy observations, is compared with several existing models by...

Fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. In this research, FCM is chosen for fuzzy clustering. Parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. These two parameters require tuning to reduce the overfitting in the...

This paper aims at applying a fuzzy arithmetic of intervals calculus and fuzzy quantities to automatic control. This one called φ–calculus fuzzy arithmetic is more practical than the extension principle one and -cut based methods. It comes from a different representation of fuzzy numbers. The present paper follows up work in introducing. The present paper is interested in its use for a fuzzy in...