In this paper, the stability study of fuzzy control systems is presented. The approach developed is based on the convergence of regular vector norms, where the comparison, the overvaluing principle and the Borne and Gentina criterion are used. The controller is of type PI-fuzzy with different partition of the two fuzzy inputs. The system to be controlled is a nonlinear system and the applicatio...

This paper proposes a fusion model to reinforce fuzzy association rules, which contains two main procedures: (1) employing the cumulative probability distribution approach (CPDA) to partition the universe of discourse and build membership functions; and (2) using the AprioriTid mining algorithm to extract fuzzy association rules. The proposed model is more objective and reasonable in determinin...

One of the main challenges of fuzzy community detection problems is to be able to measure the quality of a fuzzy partition. In this paper, we present an alternative way of measure the quality of a fuzzy community detection output based on n-dimensional grouping and overlapping functions that generalize the classical modularity for crisp community detection problems and also for crisp overlappin...

The aim of this paper is to propose a new method for building a fuzzy partition on multi-dimensional spaces. This method is apply for creating fuzzy symbolic sensors which use multi-component measurements. Colour sensing is an interesting perception to apply this method. Indeed, men have good control on this sensing but can not explain it simply. Then, the fuzzy colour sensor can learn human pe...

Random projection is a popular method for dimensionality reduction due to its simplicity and efficiency. In the past few years, random projection and fuzzy c-means based cluster ensemble approaches have been developed for high dimensional data clustering. However, they require large amounts of space for storing a big affinity matrix, and incur large computation time while clustering in this aff...

This paper presents adaptive and non-adaptive fuzzy c-means clustering methods for partitioning symbolic interval data. The proposed methods furnish a fuzzy partition and prototype for each cluster by optimizing an adequacy criterion based on suitable squared Euclidean distances between vectors of intervals. Moreover, various cluster interpretation tools are introduced. Experiments with real an...

A new methodology for analysis and forecasting of time series is proposed. It directly employs two techniques: the fuzzy transform and the perception-based logical deduction. Due to the usage of both of them and due to the innovative approach consisting in a construction of several independent models, the methodology is successfully applicable for robust long time predictions. Keywords— Time se...

Methods are investigated in order to partition in k groups a set of n multivariate observation vectors located at neighboring geographic sites; applications include image segmentation, ecological or soil data cartography. In this perspective, the deterministic variant of the EM procedure described in Zhang (1992) for hidden Markov random fields is shown to be equivalent to the optimization of a...