Search results for " Opera"

A functional approach to monitor and recognize patterns of daily traffic profiles

2014

Functional Data Analysis (FDA) is a collection of statistical techniques for the analysis of information on curves or functions. This paper presents a new methodology for analyzing the daily traffic flow profiles based on the employment of FDA. A daily traffic profile corresponds to a single datum rather than a large set of traffic counts. This insight provides ideal information for strategic decision-making regarding road expansion, control, and other long-term decisions. Using Functional Principal Component Analysis the data are projected into a low dimensional space: the space of the first functional principal components. Each curve is represented by their vector of scores on this basis.…

Representation of knowledge using Fuzzy set theory

1989

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 genetic integrated fuzzy classifier

2005

This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.

Fuzzy Classifier Based on Fuzzy Decision Tree

2007

A popular method for making a fuzzy decision tree for classification is Fuzzy ID3 algorithm. We introduce a new approach that uses cumulative information estimations of initial data. Based on these estimations we propose a new greedy version of fuzzy ID3 algorithm to be used to generate understandable fuzzy classification rules. The goal is to find a sequence of rules that causes near minimal classification costs.

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.

Different averages of a fuzzy set with an application to vessel segmentation

2005

Image segmentation is a major problem in image processing, particularly in medical image analysis. A great number of segmentation procedures produce intermediate gray-scale images that can be understood as fuzzy sets. Additionally, some segmentation procedures tend to leave free tuning parameters (very influential in the final binary image) for the user. These different binary images can be easily aggregated (into a fuzzy set) by making use of fuzzy set theory. In any case, a single binary image is required so our interest is to associate a crisp set to a given fuzzy set in an intelligent and unsupervised manner. The main idea of this paper is to define the averages of a given fuzzy set by …

Fuzzy Discrete Event Simulation for Fuzzy Production Systems Analysis

1998

Abstract Fuzzy production systems are characterised by vagueness in data and requirements that very often cannot be reduced to stochastic models. Therefore, such production systems cannot be analysed by using classical techniques such as Queue Theory or Discrete Simulation Analysis. On the other hand the great diffusion of Fuzzy Production environments in small and medium enterprises claims for the development of new analysis tools. This paper proposes a new approach to Discrete Event Simulation able to treat with fuzzy variables. A new methodology has been proposed to process fuzzy information within discrete event simulation and a prototype of a Fuzzy Discrete Event Simulator has been dev…

Decision Suport System for Manufacturing Processes Reengineering based upon Fuzzy Logic Techniques

2012

Abstract This work presents a method for taking the decision of reengineering a production system, based upon fuzzy techniques. The main advantage of this method is, after authors' opinion, is the ease of its implementation together with the reduced time for gathering data and processing it. Multi-variable decision systems are usually based upon complicated mathematical methods and involved a large amount of data to be processed. The fuzzy approach presented here is based only on five input variables and one output variable. The data for the model are gathered by simple queries and quizzes. Human perception, the main point of fuzzy logic, is widely used here for gathering input data for the…