293 research outputs found
Geometric Defuzzification Revisited
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In this paper the Geometric Defuzzification strategy for type-2 fuzzy sets is reappraised. For both discretised and geometric fuzzy sets the techniques for type-1, interval type-2, and generalised type-2 defuzzification are presented in turn. In the type-2 case the accuracy of Geometric Defuzzification is assessed through a series of test runs on interval type-2 fuzzy sets, using Exhaustive Defuzzification as the benchmark method. These experiments demonstrate the Geometric Defuzzifier to be wildly inaccurate. The test sets take many shapes; they are not confined to those type-2 sets with rotational symmetry that have previously been acknowledged by the technique’s developers to be problematic as regards accuracy. Type-2 Geometric Defuzzification is then examined theoretically. The defuzzification strategy is demonstrated to be built upon a fallacious application of the concept of centroid. This explains the markedly inaccurate experimental results. Thus the accuracy issues of type-2 Geometric Defuzzification are revealed to be inevitable, fundamental and significant
Defuzzification of groups of fuzzy numbers using data envelopment analysis
Defuzzification is a critical process in the implementation of fuzzy systems that converts fuzzy numbers to crisp representations. Few researchers have focused on cases where the crisp outputs must satisfy a set of relationships dictated in the
original crisp data. This phenomenon indicates that these crisp outputs are mathematically dependent on one another. Furthermore, these fuzzy numbers may
exist as a group of fuzzy numbers. Therefore, the primary aim of this thesis is to develop a method to defuzzify groups of fuzzy numbers based on Charnes, Cooper, and Rhodes (CCR)-Data Envelopment Analysis (DEA) model by modifying the Center of Gravity (COG) method as the objective function. The constraints represent the relationships and some additional restrictions on the allowable crisp outputs with their dependency property. This leads to the creation of crisp values with preserved
relationships and/or properties as in the original crisp data. Comparing with Linear Programming (LP) based model, the proposed CCR-DEA model is more efficient, and also able to defuzzify non-linear fuzzy numbers with accurate solutions. Moreover, the crisp outputs obtained by the proposed method are the nearest points to the fuzzy numbers in case of crisp independent outputs, and best nearest points to the fuzzy numbers in case of dependent crisp outputs. As a conclusion, the proposed
CCR-DEA defuzzification method can create either dependent crisp outputs with preserved relationship or independent crisp outputs without any relationship. Besides, the proposed method is a general method to defuzzify groups or individuals
fuzzy numbers under the assumption of convexity with linear and non-linear membership functions or relationships
Applying a Fuzzy Analytic Hierarchy Process to Demand Considerations of Households Opting for Mortgage Loans
The need for high economic development across the entire globe and Sub-Saharan Africa in particular has led to the awareness of the need to increase the housing base across the continent. The astronomical increase in population and urbanisation and its associated problems of accommodation call for the need to provide good housing for the people of Ghana; the provision of which could depends largely on the availability of mortgage facilities. However, obtaining the right mortgage is as crucial as obtaining the right home, yet buyers seemingly do not invest as much time and effort in a mortgage search as in house searches. It is against this backdrop that this study investigates factors considered by households before acquiring mortgage loans. A questionnaire was administered within the Accra metropolis, the area of Ghana with the most mortgage loan providers. We employed the fuzzy analytic hierarchy process (FAHP) to analyze the thought processes of households when making their decisions on acquiring a mortgage loan. The results indicated that factors considered by households when opting for a mortgage loan, ordered based on their degree of importance, were "Employment", "Housing Market Conditions", "Personal Factors", "Economic Factors", "Mortgage Lender Policy", "Housing Alternatives", "Knowledge", and "Social Factors". The weights of the first four factors were as high as 70.99% (Buckley’s method) and 69.70% (Chang’s method). These four items, have the most impact on household demand considerations when opting for a mortgage loan. If these factors are significantly improved, then, they can have a positive microeconomic impact on actual households demand for mortgage loans, in turn making the mortgage market a lucrative business
Applying a Fuzzy Analytic Hierarchy Process to Demand Considerations of Households Opting for Mortgage Loans
The need for high economic development across the entire globe and Sub-Saharan Africa in particular has led to the awareness of the need to increase the housing base across the continent. The astronomical increase in population and urbanisation and its associated problems of accommodation call for the need to provide good housing for the people of Ghana; the provision of which could depends largely on the availability of mortgage facilities. However, obtaining the right mortgage is as crucial as obtaining the right home, yet buyers seemingly do not invest as much time and effort in a mortgage search as in house searches. It is against this backdrop that this study investigates factors considered by households before acquiring mortgage loans. A questionnaire was administered within the Accra metropolis, the area of Ghana with the most mortgage loan providers. We employed the fuzzy analytic hierarchy process (FAHP) to analyze the thought processes of households when making their decisions on acquiring a mortgage loan. The results indicated that factors considered by households when opting for a mortgage loan, ordered based on their degree of importance, were "Employment", "Housing Market Conditions", "Personal Factors", "Economic Factors", "Mortgage Lender Policy", "Housing Alternatives", "Knowledge", and "Social Factors". The weights of the first four factors were as high as 70.99% (Buckley’s method) and 69.70% (Chang’s method). These four items, have the most impact on household demand considerations when opting for a mortgage loan. If these factors are significantly improved, then, they can have a positive microeconomic impact on actual households demand for mortgage loans, in turn making the mortgage market a lucrative business
Measurement and Fuzzy Scales
Concept measurement presents several difficulties and the tools used to collect qualitative ordinal variables are not always satisfactory. The Likert scale is examined in the context of student evaluation of teaching activity and the measurement approach considers the possibility to apply the fuzzy inference system method to obtain individual values being near the reality and coherent with the prescriptions of the measurement process. The paper presents the results of a survey carried out in the Faculty of Economics at the University of Modena and Reggio Emilia to ascertain the differences between two scales (options): one proposed by the Italian Committee for University System Evaluation (Comitato nazionale per la valutazione del sistema universitario) and another one corresponding to the traditional marks used in the evaluation of student performances in the schools attended before the university (mark scale). The results showed that the latter seemed more coherent with the score (in the decimal scale) assigned to the modalities of the scale
Distance Measurement-Based Cooperative Source Localization: A Convex Range-Free Approach
One of the most essential objectives in WSNs is to determine the spatial coordinates
of a source or a sensor node having information. In this study, the problem of range
measurement-based localization of a signal source or a sensor is revisited. The main challenge of the problem results from the non-convexity associated with range measurements
calculated using the distances from the set of nodes with known positions to a xed sen-
sor node. Such measurements corresponding to certain distances are non-convex in two
and three dimensions. Attempts recently proposed in the literature to eliminate the non-
convexity approach the problem as a non-convex geometric minimization problem, using
techniques to handle the non-convexity.
This study proposes a new fuzzy range-free sensor localization method. The method
suggests using some notions of Euclidean geometry to convert the problem into a convex
geometric problem. The convex equivalent problem is built using convex fuzzy sets, thus
avoiding multiple stable local minima issues, then a gradient based localization algorithm
is chosen to solve the problem.
Next, the proposed algorithm is simulated considering various scenarios, including the
number of available source nodes, fuzzi cation level, and area coverage. The results are
compared with an algorithm having similar fuzzy logic settings. Also, the behaviour of
both algorithms with noisy measurements are discussed. Finally, future extensions of the
algorithm are suggested, along with some guidelines
Space-Related Applications of Intelligent Control: Which Algorithm to Choose? (Theoretical Analysis of the Problem)
For a space mission to be successful it is vitally important to have a good control strategy. For example, with the Space Shuttle it is necessary to guarantee the success and smoothness of docking, the smoothness and fuel efficiency of trajectory control, etc. For an automated planetary mission it is important to control the spacecraft's trajectory, and after that, to control the planetary rover so that it would be operable for the longest possible period of time. In many complicated control situations, traditional methods of control theory are difficult or even impossible to apply. In general, in uncertain situations, where no routine methods are directly applicable, we must rely on the creativity and skill of the human operators. In order to simulate these experts, an intelligent control methodology must be developed. The research objectives of this project were: to analyze existing control techniques; to find out which of these techniques is the best with respect to the basic optimality criteria (stability, smoothness, robustness); and, if for some problems, none of the existing techniques is satisfactory, to design new, better intelligent control techniques
Development of Web Services for Computational and Analytical Processing Code of Software Metric
This study is to develop a web service, which allows to calculate and conduct analytical processing software metrics. To achieve the goal we need to an analytical overview of the software that allows to calculate code metrics, examine the existing metrics, learn the basic architectural approaches of building a web service, explore the applicability of fuzzy logic algorithms (West et.al., 2015) for the implementation of intelligent analysis software metrics, implement a web service that allows you to calculate and conduct analytical processing software metrics based on expert opinion and make testing design a web service. There are many approaches to solving the problem of testing and verification of software. During the development of this application was used unit testing. The purpose of unit testing - to isolate parts of the program and to show that these parts are operable individually. During the development process have been implemented unit tests, as a result of testing problems have been identified. The operation of the Web service is fully consistent with the previously defined functional requirements. Consequently, the task of determining the effectiveness of the programmer and the source code evaluation solved. Keywords: fuzzy logic algorithms, SaaS, Object-oriented, cyclomatic complexity, Mamdani algorithm, RESTful, API, SVN-repository
Fuzzy Distributed Genetic Approaches for Image Segmentation
This paper presents a new image segmentation algorithm (called
FDGA-Seg) based on a combination of fuzzy logic, multiagent
systems and genetic algorithms. We propose to use a fuzzy
representation of the image site labels by introducing some
imprecision in the gray tones values. The distributivity of
FDGA-Seg comes from the fact that it is designed around a
MultiAgent System (MAS) working with two different architectures
based on the master-slave and island models. A rich set of
experimental segmentation results given by FDGA-Seg is discussed
and compared to the ICM results in the last section
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