We introduce a fuzzy controller which uses a fuzzy rule base diierently to the classical Mamdani approach. We argue that it has several desirable and well-motivated properties which often cannot be obtained by a Mamdani controller. Let X and Y denote the input and the output space, respectively, and let F(:) denote the collection of all fuzzy subsets. The support of a fuzzy set A 2 F(X) is Supp...

potato image segmentation is an important part of image-based potato defect detection. this paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on genetic algorithm (ga) optimization and morphological operators. the proposed potato color image segmentation is robust against variation of background, distance and ...

Article history: Available online 4 March 2011

We propose a fuzzy rule-based system to map representations of the emotional state of an animated agent onto muscle contraction values for the appropriate facial expressions. Our implementation pays special attention to the way in which continuous changes in the intensity of emotions can be displayed smoothly on the graphical face. The rule system we have defined implements the patterns describ...

1 Dental Diagnosis from X-Ray Images using Fuzzy Rule-Based Systems; Tran Manh Tuan, School of Information and Communication Technology, Thai Nguyen University, Thai Nguyen, Vietnam Nguyen Thanh Duc, Hanoi University of Science and Technology, Hanoi, Vietnam Pham Van Hai, Hanoi University of Science and Technology, Hanoi, Vietnam Le Hoang Son, VNU University of Science, Vietnam National Univers...

recently, tuning the weights of the rules in fuzzy rule-base classification systems is researched in order to improve the accuracy of classification. in this paper, a margin-based optimization model, inspired by support vector machine classifiers, is proposed to compute these fuzzy rule weights. this approach not only  considers both accuracy and generalization criteria in a single objective fu...

This paper considers the automatic design of fuzzy rule-basedclassification systems based on labeled data. The classification performance andinterpretability are of major importance in these systems. In this paper, weutilize the distribution of training patterns in decision subspace of each fuzzyrule to improve its initially assigned certainty grade (i.e. rule weight). Ourapproach uses a punish...