In this paper, we study how changes in the coefficients of objective function, the coefficients matrix and the right-hand-side vector of constraints of the fuzzy linear programming problems with the fuzzy order relation in the objective function and the constraints under ranking function affect the fuzzy optimal solution. We consider separate cases when changes occur in the data of the problem ...

In this paper, a new structure possessing the advantages of low-power consumption, less hardware and high-speed is proposed for fuzzy controller. The maximum output delay for general fuzzy logic controllers (FLC) is about 86 ns corresponding to 11.63 MFLIPS (fuzzy logic inference per second) while this amount of the delay in the designed fuzzy controller becomes 52ns that corresponds to 19.23 M...

While focusing on document clustering, this work presents a fuzzy semi-supervised clustering algorithm called fuzzy semi-Kmeans. The fuzzy semi-Kmeans is an extension of K-means clustering model, and it is inspired by an EM algorithm and a Gaussian mixture model. Additionally, the fuzzy semi-Kmeans provides the flexibility to employ different fuzzy membership functions to measure the distance b...

Fuzzy covering-based rough set models are hybrid models using both rough set and fuzzy set theory. The former is often used to deal with uncertain and incomplete information, while the latter is used to describe vague concepts. The study of fuzzy rough set models has provided very good tools for machine learning algorithms such as feature and instance selection. In this article, we discuss diff...

Recent research has led to proposals for the integration of fuzzy based information and decision rules in business process models with use of concepts based on the fuzzy set theory. While the proposed Fuzzy EPCs provide an adequate method for conceptual representation of fuzzy business process models, the issue of exchanging and transforming such models, together with its enclosed executable co...

In this paper, a neuro-fuzzy system identification using measured input and output data are carried out. A model-free learning from “examples” methodology is developed to train a neuro-fuzzy model of a smallsize helicopter. The helicopter model is obtained and tuned using training data gathered while a teacher operates the helicopter. Behavior-based model architecture is used, with each behavio...

Fuzzy analytic hierarchy process (AHP) has been widely used for a variety of applications such as supplier selection, customer requirements assessment and the like. The vast majority of the applications, however, were found avoiding the use of sophisticated approaches for fuzzy AHP such as fuzzy least squares method while using a simple extent analysis for the sake of simplicity. The extent ana...

Edges characterize boundaries and are therefore considered for prime importance in image processing. Edge detection filters out useless data, noise and frequencies while preserving the important structural properties in an image. The proposed method adopts fuzzy reasoning in order to extract edges. Here the scanning of an image using the windowing technique takes place which is subjected to a s...

Multivalued fuzzy behavior control systems for robot navigation have demonstrated more system robustness against sensor and environmental uncertainty and changes in behavioral priorities than univalued fuzzy behavior control systems. However, a major concern for the implementation of multivalued systems is the computational burden associated with the fuzzy inference and deffuzification process....

in this paper, a new approach of modeling for artificial neural networks (ann) models based on the concepts of ann and fuzzy regression is proposed. for this purpose, we reformulated ann model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ann models. hence, it can be applied to uncertain, ambiguous, or complex environments due to its flexibility. in ...