DIAGNOSIS THROUGH BILATERAL MEMBERSHIP FUNCTIONS AND PATTERN RECOGNITION

1999 ◽  
Vol 07 (05) ◽  
pp. 501-540
Author(s):  
EMMANUEL BOUTLEUX ◽  
BERNARD DUBUISSON
Keyword(s):  
Pattern Recognition ◽  
Membership Function ◽  
Functional State ◽  
Euclidean Distance ◽  
Telephone Network ◽  
Gravity Level ◽  
Membership Functions ◽  
Fuzzy Subset ◽  
Functional States ◽  
Functional Mode

On real processes many functional states are usually observed. however some of them may represent difference significant levels (or rates) of the same functional mode. They do not represent by themselves a functional mode, they are only sub-modes of the same functional mode (e.g. one of the functional mode of my car is tank empty; some functional states observed may be 10% empty tank, 50% empty tank, 96% empty tank). Thus the interest is not only to diagnose the functional mode (e.g. tank empty, sleeping driver) but also to highlight the gravity level of this functional mode (e.g. the reaction in the middle of desert, confronted to a 4% empty tank is not the same as confronted to a 98% empty tank). The aim of this paper is to present a diagnosis of the current functional mode of a process and its gravity level. Usually in pattern recognition area, a membership function is a monotonic decreasing function of a Euclidean distance between two objects. Those objects represent two states of the process and a distance here is a dissimilarity measure between those states. Such a distance is defined in all the fuzzy subset associated with this membership function. So that function is monotonic decreasing in all subset directions. In this paper directional membership functions are proposed. In this case the distance is defined only by reference to a path describing the evolution from one functional state to another one. Then the obtained membership function is oriented according to this path and do not decrease identically within all directions in the subset. Such membership functions are then suitable in order to diagnose the state associated with data evoluting between known functional modes. An application to the French telephone network illustrates this method.

scholarly journals IMAGE DAN PRE-IMAGE TRANSLASI PADA GRUP FUZZY INTUITIONISTIK

2016 ◽  
Vol 8 (2) ◽  
pp. 41
Author(s):  
Dian Pratama
Keyword(s):  
Membership Function ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Translation Operator ◽  
Membership Functions ◽  
Fuzzy Subset ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Group ◽  
Fuzzy Subgroup

A intuitionistic fuzzy set in is set gives a membership function and a non-membership function (the complement of membership function) for each with the sum worth one. When it’s applied in group’s theory, it will called intuitionistic fuzzy group with conditions membership function is fuzzy subgroup and non-membership function is anti-fuzzy subgroup. In this research, the operator will be given a fuzzy set is called fuzzy translation operator. This operator is a mapping imposed on membership functions (fuzzy subset) to interval [ 0,1]. This research will discuss the properties homomorphism of translation on intuitionistic fuzzy groups. These properties is the structure of the image and pre-image homomorphism of translation on intuitionistic fuzzy group. We obtain that image and pre-image of translation on intuitionistic fuzzy (normal) groups is also intuitionistic fuzzy (normal) groups.

Palm Vein Recognition Based on Independent Component Analysis

2013 ◽  
Vol 333-335 ◽  
pp. 1106-1109
Author(s):  
Wei Wu
Keyword(s):  
Pattern Recognition ◽  
Independent Component Analysis ◽  
Euclidean Distance ◽  
Recognition Performance ◽  
Component Analysis ◽  
Independent Component ◽  
Experimental Results ◽  
Vein Pattern ◽  
Vein Recognition ◽  
Palm Vein

Palm vein pattern recognition is one of the newest biometric techniques researched today. This paper proposes project the palm vein image matrix based on independent component analysis directly, then calculates the Euclidean distance of the projection matrix, seeks the nearest distance for classification. The experiment has been done in a self-build palm vein database. Experimental results show that the algorithm of independent component analysis is suitable for palm vein recognition and the recognition performance is practical.

AUTOMATIC DEVELOPMENT OF FUZZY MEMBERSHIP FUNCTIONS ON HEPATITIS PATIENTS DATA USING PARTICLE SWARM OPTIMIZATION (PSO)

2015 ◽  
Vol 77 (22) ◽  
Author(s):  
Candra Dewi ◽  
Ratna Putri P.S ◽  
Indriati Indriati
Keyword(s):  
Particle Swarm Optimization ◽  
Membership Function ◽  
Fuzzy System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Membership Functions ◽  
Swarm Optimization ◽  
Global Weight ◽  
The Status ◽  
And Function

Information about the status of disease (prognosis) for patients with hepatitis is important to determine the type of action to stabilize and cure this disease. Among some system, fuzzy system is one of the methods that can be used to obtain this prognosis. In the fuzzification process, the determination of the exact range of membership function will influence the calculation of membership degree and of course will affect the final value of fuzzy system. This range and function can usually be formed using intuition or by using an algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is implemented to form the triangular membership functions in the case of patients with hepatitis. For testing process, this paper conducts four scenarios to find the best combination of PSO parameter values . Based on the testing it was found that the best parameters to form a membership function range for the hepatitis data is about 0.9, 0.1, 2, 2, 100, 500 for inertia max, inertia min, local ballast constant, global weight constant, the number of particles, and maximum iterations respectively.  

scholarly journals A Partitioning Based Algorithm to Fuzzy Tricluster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yongli Liu ◽  
Tengfei Yang ◽  
Lili Fu
Keyword(s):  
Membership Function ◽  
Fuzzy Clustering ◽  
Experimental Studies ◽  
Three Dimensional ◽  
Three Dimensions ◽  
Membership Functions ◽  
Automatic Categorization ◽  
Dimensional Cube ◽  
Data Collections ◽  
Multiple Clusters

Fuzzy clustering allows an object to exist in multiple clusters and represents the affiliation of objects to clusters by memberships. It is extended to fuzzy coclustering by assigning both objects and features membership functions. In this paper we propose a new fuzzy triclustering (FTC) algorithm for automatic categorization of three-dimensional data collections. FTC specifies membership function for each dimension and is able to generate fuzzy clusters simultaneously on three dimensions. Thus FTC divides a three-dimensional cube into many little blocks which should be triclusters with strong coherent bonding among its members. The experimental studies onMovieLensdemonstrate the strength of FTC in terms of accuracy compared to some recent popular fuzzy clustering and coclustering approaches.

scholarly journals Membership Functions for Fuzzy Focal Elements

2016 ◽  
Vol 26 (3) ◽  
pp. 395-427 ◽  
Author(s):  
Sebastian Porębski ◽  
Ewa Straszecka
Keyword(s):  
Membership Function ◽  
Data Driven ◽  
Membership Functions ◽  
Data Set ◽  
Dempster Shafer Theory ◽  
Elimination Algorithm ◽  
Rule Sets ◽  
Shafer Theory ◽  
Iris Data

Abstract The paper presents a study on data-driven diagnostic rules, which are easy to interpret by human experts. To this end, the Dempster-Shafer theory extended for fuzzy focal elements is used. Premises of the rules (fuzzy focal elements) are provided by membership functions which shapes are changing according to input symptoms. The main aim of the present study is to evaluate common membership function shapes and to introduce a rule elimination algorithm. Proposed methods are first illustrated with the popular Iris data set. Next experiments with five medical benchmark databases are performed. Results of the experiments show that various membership function shapes provide different inference efficiency but the extracted rule sets are close to each other. Thus indications for determining rules with possible heuristic interpretation can be formulated.

MULTI-CHANNEL FUZZY QUEUING SYSTEMS AND MEMBERSHIP FUNCTIONS OF RELATED FUZZY SERVICES AND FUZZY INTER-ARRIVAL TIMES

2008 ◽  
Vol 25 (05) ◽  
pp. 697-713 ◽  
Author(s):  
ÖZLEM AYDIN ◽  
AYŞEN APAYDIN
Keyword(s):  
Membership Function ◽  
Measurement Errors ◽  
Waiting Times ◽  
Membership Functions ◽  
Queuing Systems ◽  
Queuing Model ◽  
Arrival Times ◽  
Queuing Models ◽  
Service Times ◽  
The One

Queuing models need well defined knowledge on arrivals and service times. However, in real applications, because of some measurement errors or some loss of information, it is hard to achieve deterministic knowledge. Non-deterministic knowledge interferes or complicates analysis of the queuing model. Additionally, when the customers are asked about their impressions on waiting times or service times, mostly the answers are linguistic expressions like "I waited too much", "service was fast", and that the responses are. Linguistic statements and ill defined data make the sense of imprecision in the model. In this study, arrivals and service times are defined as fuzzy numbers in order to represent this imprecision. Fuzzy multi-channel queuing systems and membership functions are introduced in defining the arrivals and service times. Besides, a new membership function based on a probability function is studied. Fuzzy queuing characteristics are calculated via different membership functions and the results are compared on simulations. Among models it is found that, Generalized Beta Distribution membership function is the one that minimized the queuing characteristics.

scholarly journals IDEAL PRIMA FUZZY DI SEMIGRUP S

2017 ◽  
Vol 2 (2) ◽  
pp. 46
Author(s):  
Abdurahim Abdurahim
Keyword(s):  
Prime Ideal ◽  
Membership Function ◽  
Closed Interval ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Fuzzy Subset ◽  
Fuzzy Ideal ◽  
Function Domain

A fuzzy membership function is a function that maps the nonempty set  to a closed interval . Furthermore, if the function domain is replaced with a semigroup, then the function is called fuzzy subset. A fuzzy subset mapping  to  is denoted by . A fuzzy subset  is called fuzzy ideal if it satisfies both  and . Moreover,  is called a fuzzy prime ideal if for any fuzzy ideal  and , with  implies  or . In this paper be investigated about some characteristics of prime fuzzy ideals and some example of them.

scholarly journals Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric

2018 ◽  
Vol 3 (7) ◽  
pp. 67
Author(s):  
Mousumi Hasan Mukti ◽  
Quazi Saad-Ul-Mosaher ◽  
Khalil Ahammad
Keyword(s):  
Pattern Recognition ◽  
Fourier Transform ◽  
Character Recognition ◽  
Euclidean Distance ◽  
Great Part ◽  
Research Area ◽  
Input Image ◽  
Handwritten Character ◽  
Input Dataset ◽  
Implementation Tool

Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800 handwritten character texts from different people has been developed for this purpose and these character texts are converted to their equivalent printed version to implement this research. MATLAB has been used as an implementation tool for different preprocessing techniques like cropping, resizing, flood filling, thinning etc. Processed text images are used as input to the system and they are converted to FT. Handwritten character of different person may be of different style and angle. The input dataset is collected from various types of people including age level from 5 to 70 years, from different professions like pre-schooling students, graduate students, doctors, teachers and housewives. So, to match the input image with printed dataset (PDS) each printed data is rotated up to 450 left and right and then their FT is computed. The Euclidean distance among the input image and the rotated 30 images of each printed text are taken as intermediate distance set. The minimum value of Euclidean distance for a character is used to recognize the targeted character from the intermediate set. Wrongly detected texts are not thrown away from the system rather those are stored in the named character or digits file so that those can be used in future for deep learning. By following the proposed methodology, the research has achieved 98.88% recognition accuracy according to the input and PDS.

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