The purpose of this paper is to develop a new two-stage method for fuzzy multi-objective linear program and apply to engineering project portfolio selection. In the fuzzy multi-objective linear program, all the objective coefficients, technological coefficients and resources are trapezoidal fuzzy numbers (TrFNs). An order relationship for TrFNs is introduced by using the interval expectation of...

Possibilistic Defeasible Logic Programming (P-DeLP) is a logic programming language which combines features from argumentation theory and logic programming, incorporating the treatment of possibilistic uncertainty at object-language level. This paper presents a first approach towards extending P-DeLP to incorporate fuzzy constants and fuzzy propositional variables. We focus on how to characteri...

This paper presents a new clustering algorithm named improved type-2 possibilistic fuzzy c-means (IT2PFCM) for fuzzy segmentation of magnetic resonance imaging, which combines the advantages of type 2 fuzzy set, the fuzzy c-means (FCM) and Possibilistic fuzzy c-means clustering (PFCM). First of all, the type 2 fuzzy is used to fuse the membership function of the two segmentation algorithms (FCM...

Focusing on the environmental issues, including the reduction of scrap and consumer residuals, along with the benefiting from the economic value during the life cycle of goods/products leads the companies to have an important competitive approach. The aim of this paper is to present a new mixed nonlinear facility locationallocation model in recycling collection networks by considering multi-ech...

In some complicated datasets, due to the presence of noisy data points and outliers, cluster validity indices can give conflicting results in determining optimal number clusters. This paper presents a new index for fuzzy-possibilistic c-means clustering called Fuzzy-Possibilistic)FP (index, which works well clusters that vary shape density. Moreover, FPCM like most algorithms is susceptible ini...

In many modern data analysis scenarios the first and most urgent task consists of reducing the redundancy in high dimensional input spaces. A method is presented that quantifies the discriminative power of the input features in a fuzzy model. A possibilistic information measure of the model is defined on the basis of the available fuzzy rules and the resulting possibilistic information gain, as...

In this paper, fuzzy possibilistic c-means (FPCM) approach based on penalized and compensated constraints are proposed to vector quantization (VQ) in discrete cosine transform (DCT) for image compression. These approaches are named penalized fuzzy possibilistic c-means (PFPCM) and compensated fuzzy possibilistic c-means (CFPCM). The main purpose is to modify the FPCM strategy with penalized or ...

A quality of centroid-based clustering is highly dependent on initialization. In the article we propose initialization based on the probability of finding objects, which could represent individual clusters. We present results of experiments which compare the quality of clustering obtained by k-means algorithm and by selected methods for fuzzy clustering: FCM (fuzzy c-means), PCA (possibilistic ...

In this paper, we analyzed the segmentation of MRI brain image into different tissue types on brain image using Possibilistic fuzzy c-means (PFCM) clustering. Application of this method to MRI brain image gives the better segmentation result in compare with Fuzzy c-mean (FCM) and fuzzy possibilistic c-means (FPCM). The results are verified quantitatively using similarity metrics, false positive...

Transfer point location problem in which demand points are weighted and their coordinates are uniformly distributed for the case of minimax objective and planar topology has been introduced recently. In the real problems, such as disaster cases, different points of an area might be demand locations with different possibility degrees. Thus, developing a more applicable model is critical for thes...