Based on the conclusions drawn in the bijective transformation between possibility and probability, a method is proposed to estimate the fuzzy membership function for pattern recognition purposes. A rational function approximation to the probability density function is obtained from the histogram of a finite (and sometimes very small) number of samples. This function is normalized such that the highest ordinate is one. The parameters representing the rational function are used for classifying the pattern samples based on a max-min decision rule. The method is illustrated with examples
The present work aims at a statistically motivated parameterisation for a fuzzy classification appro...
The problem of histogram sharpening and thresholding by minimising greylevel fuzziness is considered...
An adaptive learning scheme, based on a fuzzy approximation\ud to the gradient descent method for tr...
Based on the conclusions drawn in the bijective transformation between possibility and probability, ...
AbstractThe estimation of membership functions from data is an important step in many applications o...
This paper introduces a novel way to compute the membership function of a fuzzy set approximating th...
In previous papers from the authors fuzzy model identification methods were discussed. The bacterial...
In previous papers from the authors fuzzy model identification methods were discussed. The bacterial...
In this paper, an unsupervised approach incorporating variable bandwidth mean-shift and robust stati...
Fuzzy histograms are a fuzzy generalization of ordinary crisp histograms. In this paper, fuzzy histo...
The paper deals with the problem of constructing Gaussian membership functions of fuzzy sets for fuz...
AbstractA practical problem in the identification of fuzzy systems from data, is the design and the ...
Taking over the skills of the human expert will make it possible to develop decision-making algorith...
The assessment of the physiological state of an individual requires an objective evaluation of biolo...
In the field of pattern recognition, there exist several unwieldy problems which have been avoided c...
The present work aims at a statistically motivated parameterisation for a fuzzy classification appro...
The problem of histogram sharpening and thresholding by minimising greylevel fuzziness is considered...
An adaptive learning scheme, based on a fuzzy approximation\ud to the gradient descent method for tr...
Based on the conclusions drawn in the bijective transformation between possibility and probability, ...
AbstractThe estimation of membership functions from data is an important step in many applications o...
This paper introduces a novel way to compute the membership function of a fuzzy set approximating th...
In previous papers from the authors fuzzy model identification methods were discussed. The bacterial...
In previous papers from the authors fuzzy model identification methods were discussed. The bacterial...
In this paper, an unsupervised approach incorporating variable bandwidth mean-shift and robust stati...
Fuzzy histograms are a fuzzy generalization of ordinary crisp histograms. In this paper, fuzzy histo...
The paper deals with the problem of constructing Gaussian membership functions of fuzzy sets for fuz...
AbstractA practical problem in the identification of fuzzy systems from data, is the design and the ...
Taking over the skills of the human expert will make it possible to develop decision-making algorith...
The assessment of the physiological state of an individual requires an objective evaluation of biolo...
In the field of pattern recognition, there exist several unwieldy problems which have been avoided c...
The present work aims at a statistically motivated parameterisation for a fuzzy classification appro...
The problem of histogram sharpening and thresholding by minimising greylevel fuzziness is considered...
An adaptive learning scheme, based on a fuzzy approximation\ud to the gradient descent method for tr...