To apply fuzzy logic, two major tasks need to be performed: the derivation of production rules and the determination of membership functions. These tasks are often difficult and time consuming. This paper presents an algorithmic method for generating membership functions and fuzzy production rules; the method includes an entropy minimization for screening analog values. Membership functions are...

In this paper, we investigate the performance of the fuzzy entropy approach when it is applied to the segmentation of infrared objects. Through a number of examples, the performance is compared with those using existing entropy-based object segmentation approaches and the superiority of the fuzzy entropy method is demonstrated. In addition, the ant colony optimization (ACO) is used to obtain th...

[naeini1] in this paper, a new method is proposed to find the fuzzy optimal solution of fuzzy multi-objective linear programming problems (fmolpp) with fuzzy right hand side and fuzzy decision variables. due to the imprecise nature of available resources, determination of a definitive solution to the model seems impossible. therefore, the proposed model is designed in order to make fuzzy decisi...

The equipment investment scheme evaluation of enterprise is usually multi-objective optimization problem affected by many factors. Among many optimization methods, the common fuzzy matter element is based on the matter-element analysis and combined with the concepts of fuzzy mathematics, which can reflect the subjective fuzzy judgment; the common entropy weight method makes use of the judgment ...

Aimed at fuzzy clustering based on the generalized entropy, an image segmentation algorithm by joining space information of image is presented in this paper. For solving the optimization problem with generalized entropy’s fuzzy clustering, both Hopfield neural network and multi-synapse neural network are used in order to obtain cluster centers and fuzzy membership degrees. In addition, to impro...

Shannon entropy was defined for probability distributions and then its using was expanded to measure the uncertainty of knowledge for systems with complete information. In this article, it is proposed to extend the using of Shannon entropy to under-defined or over-defined information systems. To be able to use Shannon entropy, the information is normalized by an affine transformation. The const...

In the image acquisition or transmission, the image may be damaged and distorted due to various reasons; therefore, in order to satisfy people’s visual effects, these images with degrading quality must be processed to meet practical needs. Integrating artificial bee colony algorithm and fuzzy set, this paper introduces fuzzy entropy into the self-adaptive fuzzy enhancement of image so as to rea...

Fuzzy clustering is capable of finding vague boundaries that crisp clustering fails to obtain. But time complexity of fuzzy clustering is usually high, and the need to specify complicated parameters hinders its use. In this paper, an entropy-based fuzzy clustering method is proposed. It automatically identifies the number and initial locations of cluster centers. It calculates the entropy at ea...

The paper aims at developing a hierarchical algorithm for matching a given template of m × non an image of M × N pixels partitioned into equal sized blocks of m × n pixels. The algorithm employs a fuzzy metric to measure the dispersion of individual feature of a block with respect to that of the template. A fuzzy threshold, preset by the user, is employed to restrict less likely blocks from par...

Interval type-2 fuzzy sets, each of which is characterized by the footprint of uncertainty, are a very useful means to depict the linguistic information in the process of decision making. In this article, we investigate the group decision making problems in which all the linguistic information provided by the decision makers is expressed as interval type-2 fuzzy decision matrices where each of ...