Search results for "Fuzzy Logic."

A Formal Skeleton of Commonsense Reasoning

2022

After referring several times to Commonsense or Ordinary Reasoning, let’s devote a few pages to present a (minimal) mathematical model of it that can be seen as the ‘Skeleton’ of Reasoning, since it is defined by a set of few, simple laws appearing in the models of particular and specialized modes of reasoning like, for instance: Boolean Algebras for the reasoning with precise concepts; Orto-modular lattices for the reasoning with the concepts of Quantum Physics; and also in the so called Algebras of Fuzzy Sets for the reasoning with imprecise concepts, and among them De Morgan-Kleene algebras. All these models have interesting applications.

Fuzzy Logic, Vagueness and Uncertainty

2009

Automatic detection of cardiac contours on MR Images using fuzzy logic and dynamic programming

1997

International audience; Abstract: This paper deals with the use of fuzzy logic and dynamic programming in the detection of cardiac contours in MR Images. The definition of two parameters for each pixel allows the construction of the fuzzy set of the cardiac contour points. The first parameter takes into account the grey level, and the second the presence of an edge. A corresponding fuzzy matrix is derived from the initial image. Finally, a dynamic programming with graph searching is performed on this fuzzy matrix. The method has been tested on several MR images and the results of the contouring were validated by an expert in the domain. This preliminary work clearly demonstrates the interes…

Fuzzy methods for analysing fuzzy production environment

1998

Abstract Very recently, in production management research literature, the necessity to extend production systems analysis techniques, such as queue theory, Mean Value Analysis (MVA) and discrete simulation, to Fuzzy Production Environments, i.e. to those production situations in which data are vague, has emerged. Fuzzy set theory is a powerful tool to model vagueness and, therefore, fuzzy mathematics can be used to extend classical production system analysis techniques. This paper proposes a methodology based on fuzzy relation algebra to extend classical MVA and discrete event simulation.

A fuzzy framework to explain musical tuning in practice

2013

A theoretical tuning system is a set of pitches that can be used to play music. It is a fact that the human ear perceives notes with very close frequencies as if they were the same note. Therefore, in our approach a musical note and its pitch sensation are modeled as L-R fuzzy numbers with a modal interval and a bounded support. We pay particular attention to the 12-tone equal temperament (12-TET) for being the most widely used tuning system and we define the fuzzy 12-TET composed of 12 fuzzy notes. A similarity relation between a fuzzy note and a theoretical note can be defined, and subsequently a similarity class associated to each one of the fuzzy notes in the fuzzy 12-TET arises. Finall…

An integrated fuzzy cells-classifier

2007

This paper introduces a genetic algorithm able to combine different classifiers based on different distance functions. The use of a genetic algorithm is motivated by the fact that the combination phase is based on the optimization of a vote strategy. The method has been applied to the classification of four types of biological cells, results show an improvement of the recognition rate using the genetic algorithm combination strategy compared with the recognition rate of each single classifier.

Combining one class fuzzy KNN’s

2007

This paper introduces a parallel combination of N > 2 one class fuzzy KNN (FKNN) classifiers. The classifier combination consists of a new optimization procedure based on a genetic algorithm applied to FKNN’s, that differ in the kind of similarity used. We tested the integration techniques in the case of N = 5 similarities that have been recently introduced to face with categorical data sets. The assessment of the method has been carried out on two public data set, the Masquerading User Data (www.schonlau.net) and the badges database on the UCI Machine Learning Repository (http://www.ics.uci.edu/~mlearn/). Preliminary results show the better performance obtained by the fuzzy integration …

An Approach to the Concept of Soft Fuzzy Proximity

2014

The purpose of this paper is to introduce the concept of soft fuzzy proximity. Firstly, we give the definitions of soft fuzzy proximity and Katsaras soft fuzzy proximity, and also we investigate the relations between the soft fuzzy proximity and slightly modified version of Katsaras soft fuzzy proximity. Secondly, we induce a soft fuzzy topology from a given soft fuzzy proximity by using soft fuzzy closure operator. Then, we obtain the initial soft fuzzy proximity from a given family of soft fuzzy proximities. So, we describe products in the category of soft fuzzy proximities. Finally, we show that a family of all soft fuzzy proximities on a given set constitutes a complete lattice.

Unsupervised tissue classification of brain MR images for voxel-based morphometry analysis

2016

In this article, a fully unsupervised method for brain tissue segmentation of T1-weighted MRI 3D volumes is proposed. The method uses the Fuzzy C-Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro-radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial-and-error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro-Spinal Fluid in …

Fuzzy C-Means Inspired Free Form Deformation Technique for Registration

2009

This paper presents a novel method aimed to free form deformation function approximation for purpose of image registration. The method is currently feature-based. The algorithm is inspired to concepts derived from Fuzzy C-means clustering technique such as membership degree and cluster centroids. After algorithm explanation, tests and relative results obtained are presented and discussed. Finally, considerations on future improvements are elucidated.