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...

In order to avoid the usual binary definition of poverty, a fuzzy set approach to poverty measurement has been used. The poor are typically analyzed by partitioning the total population in three mutually exclusive groups around the poverty line. This builds on fuzzy sets theory whereby the definition of the threshold of who is poor or non-poor, is fuzzy. This paper presents a method that consis...

Digital image processing is widely used by many research oriented fields. Edge detection method is one of the important techniques in image segmentation, which is used to find out exact position of objects in the given image. Edge detection can be achieved by various approaches such as Canny, Prewitt, Sobel, etc. Fuzzy Logic techniques have been used in image understanding applications such as ...

One of the most frequently used models for classification tasks is the Probabilistic Neural Network. Several improvements of the Probabilistic Neural Network have been proposed such as the Evolutionary Probabilistic Neural Network that employs the Particle Swarm Optimization stochastic algorithm for the proper selection of its spread (smoothing) parameters and the prior probabilities. To furthe...

|Fuzzy information processing systems start with expert knowledge which is usually formulated in terms of words from natural language. This knowledge is then usually reformulated in computer-friendly terms of membership functions, and the system transform these input membership functions into the membership functions which describe the result of fuzzy data processing. It is then desirable to tr...

In this paper first we review two ranking methods for intuitionistic fuzzy numbers (IF numbers), then we proposed a new ordering method for IF numbers in which we consider two characteristic values of membership and non-membership for an IF number. Key-Words: Intuitionistic Fuzzy Number, Ranking Function Methods, Characteristic Value

we propose a generation method of membership function for extracting features of brain tissues on images of Magnetic Resonance Imaging (MRI). This method is derived from histogram analysis to create a membership function. According to a priori knowledge given by the neuro-radiologist, such as the features of gray level of differentiate brain tissues in MR images, we detect the peak or valley fe...

A normally distributed fuzzy variable is defined by a unimodal and symmetric membership function, and is proved to be the most uncertain one when expected value and variance are given. In this note, a lognormally distributed fuzzy variable is defined and the membership function, expected value and variance of this variable are discussed. Based on these results, some properties about geometric L...