Fuzzy Relation Matrix Research Articles

PSO-based high order time invariant fuzzy time series method: Application to stock exchange data

Fuzzy time series methods are effective techniques to forecast time series. Fuzzy time series methods are based on fuzzy set theory. In the early years, classical fuzzy set operations were used in the fuzzy time series methods. In recent years, artificial intelligence techniques have been used in different stages of fuzzy time series methods. In this paper, a novel fuzzy time series method which is based on particle swarm optimization is proposed. A high order fuzzy time series forecasting model is used in the proposed method. In the proposed method, determination of fuzzy relations is performed by estimating the optimal fuzzy relation matrix. The performance of the proposed method is compared to some methods in the literature by using three real world time series. It is shown that the proposed method has better performance than other methods in the literature.

Fuzzy Comprehensive Evaluation of Water Resources Carrying Capacity based on Vague Method

China is a water resource-poor country, in recent years, rapid population growth resulting in water issues have become increasingly prominent. Taking Anhui Province as an example, using fuzzy synthetic evaluation method and combining the analytic hierarchy process evaluation analysis of water resources carrying capacity. Firstly, establishing the evaluation criteria of the index system of regional water resources carrying capacity and individual indicators, calculating evaluation index weight,establishing the weight fuzzy matrix, using Vague set and analytic hierarchy process method to determine the weight vector.Secondly, compositing a fuzzy matrix of weight values after obtaining the value of the weights of the various indicators, calculating the degree of membership, establishing the fuzzy relation matrix, fuzzy subset into a comprehensive evaluation model, and seeking the comprehensive evaluation value .Finally, determining the level of sustainable use of water resources in the specific area based on the principle of maximum degree of membership . After the evaluation are analyzed and compared in order to elaborate the evaluation method is feasible and effective.

The Diagnosis of Abnormal Assembly Quality Based on Fuzzy Relation Equations

The relationship between quality abnormality and anomalous causes in the assembly process of CNC machine was described by fuzzy relation equations, because they were not one to one. The fuzzy relation equations were established according to the fuzzy relation matrix and membership degree of abnormality mode and were translated into optimal solution problems by fuzzy deconvolution method. The interval solution of the fuzzy relation equation was obtained by minimal mean square error of BP algorithm, realizing section locating of the contribution of anomalous causes to quality abnormality for a given problem, thereby gaining the optimal solution. Finally, the viability and effectiveness of this method were verified by the quality abnormity diagnosis in the assembly process of a NC rotary table.

A New Method For Face Recognition Using Feature Clustering With Fuzzy Parameters

In this paper, we have applied Gabor filter for fiducial point localisation. The fiducial points are represented as trapezoidal fuzzy numbers. These fiducial points are then transformed into crisp numbers. The number of fiducial points are then reduced by using a distance formula. The distance of each of these fiducial points are then stored in the database of the system. The same methodology is applied on the input face which is to be matched with the faces available in the database. Then a fuzzy preference relation matrix is obtained .The largest eigen value of this matrix is then determined. Once the largest eigen value is determined the corresponding priority vector can easily be obtained, from which we can easily match the input face with the database. In the broadest sense we have observed we have used the fuzzy mathematics to counter the impreciseness in facial recognition.

A hybrid fuzzy technique for the selection of warehouse location in a supply chain under a utopian environment

This paper proposes a novel hybrid fuzzy multi criteria decision-making technique for warehouse location selection in a supply chain under a utopian environment. In the proposed methodology, the concept of fuzzy set theory is used to measure subjective performance ratings of warehouse locations and weights of criteria. The simple additive weighting method and factor rating systems are combined to calculate the final fuzzy values (FFVs) of each warehouse location. FFVs are implemented to evaluate preference relationships between the alternatives. Pairwise comparison of the preference relationships generate a fuzzy preference relation matrix (FPRM). This investigation introduces four key selection parameters computed from the FPRM, viz. row sum, column sum, row sum − column sum difference and row sum–column sum ratio to select the best warehouse location. The proposed algorithm is illustrated with the solution of a real life problem of warehouse location selection. A comparative analysis is accomplished to show the acceptability and effectiveness of the proposed method. The result of this study clearly establishes the proposed methodology as one of the most erudite decision-making tools in a supply chain.

Fuzzy Hierarchy Analysis of Gravel Pile Stability

This thesis introduces a brief of the development status of the gravel pile and the basic principles, and analysis of vibration displacement stone column foundation treatment influencing factors by the Analytic Hierarchy Process. To compare and analyzed from it, and apply the square root method to calculate their weight; According to the evaluation set structure distribution trapezoidal membership function, and get the final membership of each index to evaluating set and fuzzy relation matrix; By the analysis of application of a wharf, and find out the comprehensive score stability gravel pile system, we have certain reference from it in the future.

Fuzzy Relational Clustering Based on Knowledge Mesh and its Application

A selection method of knowledge meshes based on fuzzy relational clustering is proposed. Considering the perfection degree, the matching degree among knowledge meshes and the level frame of knowledge mesh, the similarity function is defined. Its properties are proved. The similarity values between knowledge meshes are regarded as clustering data. The fuzzy relational matrix is constructed and decomposed. The knowledge meshes with high membership in each class are regarded as referenced knowledge meshes. Or the knowledge meshes in the class are further chosen according to users needs. Finally the example shows that the method is effective.

A Note on Fuzzy Relational Matrices

In this article, we would like to revisit the existing definitions of cartesian product of fuzzy sets and the fuzzy relational matrix from the standpoint of the new definition of fuzzy sets on the basis of reference function. In the process it is observed that there are drawbacks in the current defintions and hence new definitions of terms mentioned are proposed in this article. Some numerical examples are also presented to make the matter clear and simple.

A Study on Integrated Condition Assessment System for Bridge

This paper mechanically applies the thought of recursive hierarchical structure in analytic hierarchy process, by means of fuzzy mathematics, with the investigation, analysis, induction, the fuzzy relation matrix has been found, at the same time, the assignment weight of index is merged into that. Then the integrated condition assessment results of bridge are gained finally. This method greatly reduces the happening of ill-considering the influencing factors, make the results more real, specific and comprehensive, avoid effectively great loss which is caused by the defects of safety and service performance.

Evaluation on Degree and Result of Bilingual Education of Business Courses in GDUFS

From the connotation of bilingual education in higher education in China, this paper analyzes the factors that may affect the quality and result of bilingual education, such as faculty and students’ English level, teaching materials, curriculum system, classroom instruction, teaching quality control, as well as incentives and other factors. The paper focuses on Fuzzy-AHP model as a university bilingual education quality evaluation system, and build an evaluation index system for quality control of bilingual teaching. It also conducts quantitative analysis using analytic hierarchy process and fuzzy comprehensive evaluation on the degree of the bilingual education of business courses in Guangdong University of Foreign Studies (GDUFS), China. By selecting evaluation indicators and using analytic hierarchy process, the paper determines corresponding weight of the indicators. It also establishes sets of standard on evaluation and fuzzy relationship matrix, and conducts an empirical study on the degree of bilingual education of business courses in GDUFS. The paper has certain theoretical and practical significance for bilingualism in higher education in China.

A New Early Warning Evaluation Method and Decision Mechanism for Urban Significant Emergency in Uncertain Environment

This study presents a new early warning evaluating method and decision mechanism for urban emergency in which the risk factors are easily assessed by fuzzy numbers. By using the fuzzy preference relation matrix and the extended fuzzy AHP, the relative weight of each risk factor can be estimated. Then we evaluate the total fuzzy risk value by aggregating the severity of loss of risk factor and the relative weight of risk factor. According to the similarity between the fuzzy comprehensive risk value and the pre-established risk grade, the early warning grade of the urban significant emergency can be determined for urgent emergency decision-making.

A Fuzzy Preference Relation Based Method for Face Recognition by Gabor Filters

In this paper we have applied Gabor filter for fiducial point localization. After obtaining the fiducial points the number of fiducial points are reduced using a distance formula. The distance of each of this fiducial point is then calculated by the distance formula and stored in the database of the system. The same methodology is also applied on the input face which is to be matched with the faces available in the database. Then a fuzzy preference relation matrix is obtained . the largest eigen value of this matrix is then determined by algebraic method or numerical method depending on the order of the matrix. To apply the numerical method which is more easier for large order matrices we have used the C programming of this method . Once the largest eigen value is determined the corresponding priority vector can easily be obtained from which we can easily match the input face with the database.

Fault Diagnosis of Construction Elevator Based on Fuzzy Recognition

Fault eigenvector, fault fuzzy vector and fuzzy relation matrix of the construction elevator is built on using the fuzzy recognition method in the fault diagnosis system of the elevator. On the basis of every fault membership obtained through fuzzy recognition, the equipment fault is confirmed according to max membership principle. The test shows it can effectively help to estimate cause of fault and to avoid the accidents when the fuzzy recognition method is used.

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.

Fuzzy Complexity Model for Enterprise Maintenance Projects

Complexity measures can be used as a yardstick for budgeting, resource allocation, and planning in enterprise maintenance projects. Maintenance projects have two primary areas of complexity (i.e., technical and managerial aspects) that may not be measured precisely due to uncertain situations. In this study, a fuzzy graph-based model to measure the relative complexity of projects is presented that uses an aggregation operator to mitigate the conflict of experts' opinions on a complexity relation. Using a fuzzy relation matrix to represent the degree of assurance of complexity, the model maps a fuzzy graph into a scaled Cartesian diagram that depicts the relative degree of complexity among projects. An illustrative example for several maintenance projects is demonstrated to present the application of the model.

프로젝트 성과요인에 대한 퍼지구조분석

The structure of layer for success factors of company project can be known by FSM which is available to recognize structure of the system. However, the relationship between the layers which belongs to the success factors of company projects can be unknown. From these viewpoints, this study is to analyze the structure of the relation between the layers contained the success factors of company projects using fuzzy system. Therefore, the relationship between lower vs middle layer and middle vs upper layer are obtained by evaluation model of company projects which has possibility and necessity of fuzzy system as fuzzy relation matrix. Using these results, fuzzy relation analysis is done and showed the usefulness.

Fuzzy Synthetic Estimation of Multi-Process Planning Schemes in CAPP

Aiming at the evalution and selection of multi-process plans in CAPP, a general kind of fuzzy comprehensive evalution is proposed according to the principle of fuzzy consistent relation and fuzzy consistent matrix and the correlation of gray system theory, and detailed evalution step is given. Three kind of the process plans are evaluated with the method of fuzzy comprehensive evaluation. The fuzzy synthetic estimation module is founded based on the product quality , manufacturing cost and production cycle. It has been adopted to cases in production and has realized the optimization of process schemes in CAPP.

Fuzzy Comprehensive Evaluation of the Suitability of Downhole Inflow Control Device in Sandstone Reservoir

The downhole inflow control device (ICD) could control the production profile of every section in horizontal well and the edge and bottom water coning, so it is important in enhancing oil and gas recovery. To quantitatively depict ICD's suitability in horizontal wells requires analyzing reservoir parameters like permeability variation coefficient, average permeability, the ratio of vertical permeability to horizontal permeability and the technology parameters like tube string size, horizontal section length, segment number and the pressure loss ratio between ICD and reservoir. With fuzzy mathematical method, this work has analyzed these factors through relative subordinate function and established the fuzzy comprehensive evaluation model to study the suitability of ICD in sandstone reservoirs by the weight of each factor and fuzzy relationship matrix. The fuzzy evaluation and simulation results prove this method could well evaluate the suitability of ICD in sandstone reservoirs and it is significant for the application of ICD in horizontal well development.

A New Time-Invariant Fuzzy Time Series Forecasting Method Based on Genetic Algorithm

In recent years, many fuzzy time series methods have been proposed in the literature. Some of these methods use the classical fuzzy set theory, which needs complex matricial operations in fuzzy time series methods. Because of this problem, many studies in the literature use fuzzy group relationship tables. Since the fuzzy relationship tables use order of fuzzy sets, the membership functions of fuzzy sets have not been taken into consideration. In this study, a new method that employs membership functions of fuzzy sets is proposed. The new method determines elements of fuzzy relation matrix based on genetic algorithms. The proposed method uses first-order fuzzy time series forecasting model, and it is applied to the several data sets. As a result of implementation, it is obtained that the proposed method outperforms some methods in the literature.

Fuzzy Identification of Nonlinear Systems via Orthogonal Transform

This paper is concerned with the application of orthogonal transforms and fuzzy competitive learning to extract fuzzy rules from data. The least square algorithm with orthogonal transforms is proposed to supervise the progress of fuzzy competitive learning. First of all, competitive learning takes place in the product space of system inputs and outputs and each cluster corresponds to a fuzzy IF–THEN rule. The fuzzy relation matrix, confirmed by fuzzy competitive learning, is studied by the orthogonal least square algorithm. The validity of fuzzy rules is obtained by analyzing the effect of orthogonal vectors in the fuzzy model, and subsequently removing less important ones. The structure identification and parameter identification of the fuzzy model are simultaneously confirmed in the proposed algorithm. Using simulation results as an example, the fuzzy model of non-linear systems can be built by using the proposed algorithm. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society