In this paper, application of possibilistic clustering techniques to identification of local linear models will be discussed. In particular, a generalisation of some possibilistic algorithms in the bibliography is obtained. With the presented procedures, a trade-off between an “expected shape” of the membership functions and model fit can be stated. Possibilistic clustering may allow for better...

Corresponding Author: Andréia Alves dos Santos Schwaab Department of Informatics and Statistics, Federal University of Santa Catarina, Florianopolis, Brazil Email:[email protected] Abstract: Generation of membership functions is an important step in construction of fuzzy systems. Since membership functions reflect what is known about the variables involved in a problem, when they are c...

In this paper, we incorporate the concept of fuzzy set theory into the support vector regression (SVR). In our proposed method, target outputs of training samples are considered to be fuzzy numbers and then, membership function of actual output (objective hyperplane in high dimensional feature space) is obtained. Two main properties of our proposed method are: (1) membership function of actual ...

Rubin and Narsimhan [1984] proposed a method for formulating fuzzy priorities in goal programming. They used the concept that the membership function based on nested priorities has to be concave to solve the fuzzy goal programming problem by linear programming method. In this paper, we relax the condition of concave membership function by quasi concave membership function. We showed that the fu...

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This paper describes our approach to discriminate news report from others such as commercials and music in broadcast news programs based on audio information. The reported work here is part of the effort at AT&T to hierarchically segment broadcast news programs into semantically meaningful units at different levels of abstraction. At the coarse level, using the described approach we preprocess ...

The basic concepts of some near open subgraphs, near rough, near exact and near fuzzy graphs are introduced and sufficiently illustrated. The Gm-closure space induced by closure operators is used to generalize the basic rough graph concepts. We introduce the near exactness and near roughness by applying the near concepts to make more accuracy for definability of graphs. We give a new definition...

An analog sequential architecture for efficient neuro-fuzzy models implementation is proposed. The best features of digital and analog domains are combined to provide a high degree of flexibility (in terms of number of inputs, number of membership functions per input and number of fuzzy rules) when handling real world tasks. The performance estimations show a good area/throughput ratio, thus ma...

A method for segmentation assessment is proposed. The technique is based on a comparison of the segmentation produced by an algorithm with an ideal segmentation. The procedure to obtain the ideal segmentation is described in detail. Uncertainty regarding the edge positions is accounted for in the discrepancy calculation of each edge using fuzzy reasoning. The uncertaintymeasurement consists of ...

This paper aims in the design of an intelligent Fuzzy Inference System that evaluates risk due to natural disasters. Though its basic framework can be easily adjusted to perform in any type of natural hazard, it has been specifically designed to be applied in the case of forest fire risk in the area of the Greek terrain. Its purpose is to create a descending list of the areas under study, accor...