Fuzzy Grade Research Articles

Minimization of Deterministic Fuzzy Tree Automata

Until now, some methods for minimizing deterministic fuzzy finite tree automata (DFFTA) and weighted tree automata have been established by researchers. Those methods are language preserving, but the behavior of original automata and minimized one may be different. This paper, considers both language preserving and behavior preserving in minimization process. We drive Myhill-Nerode kind theorems corresponding to each proposed method and introduce PTIME algorithms for behaviorally and linguistically minimization. The proposed minimization algorithms are based on two main steps. The first step includes finding dependency between equivalency of states, according to the set of transition rules of DFFTA, and making merging dependency graph (MDG). The second step is refinement of MDG and making minimization equivalency set (MES). Additionally, behavior preserving minimization of DFFTA requires a pre-processing for modifying fuzzy membership grade of rules and final states, which is called normalization.

Atanassov's intuitionistic fuzzy grade of a class of non-complete 1-hypergroups

This paper continues the study of the length of the sequence of join spaces and Atanassov's intuitionistic fuzzy sets associated with a hypergroup H. The classes of non-complete 1-hypergroups are considered and analyzed their Atanassov's intuitionistic fuzzy grade.

Modelling port choice in an uncertain environment

Port choice is an important issue to be investigated to ensure the effective integration of container supply chains and the sustainable development of regional economy. The selection of appropriate ports to facilitate shipping activities and international trade is crucial for many stakeholders, including shipping lines, port administrators, cargo shippers and national governments. The task is essentially a process of multiple criterion decision-making (MCDM) under uncertainty, requiring analysts to derive rational decisions from uncertain and incomplete data related to different quantitative and qualitative determinants. This paper aims at proposing a new conceptual port choice method by explaining the role fuzzy logic in evidential reasoning in a complementary way, in which various forms of raw data (either objective or subjective) collected to evaluate port performance can first be converted into and presented as fuzzy grades defined using linguistics terms with degrees of belief (DoBs) and second be combined using evidential reasoning to produce a port choice preference score. The method is applied to analyse the selection of major Northeast Asian (NEA) container ports from a shipping line’s perspective. The outcome, a port choice preference score, is calculated using evidential reasoning to directly synthesize the true estimation of the port with respect to each criterion and therefore, unlike a relative ranking index, keeps the ‘goodness’ of port evaluation, capable of benchmarking a specific port’s performance and monitoring the increase of its competitiveness in a longitude study with respect to an individual criterion or all the criteria as a whole.

Analysis of machining characteristics in drilling of GFRP composite with application of fuzzy logic approach

Article history: Received June 1 2013 Received in revised format July 15 2013 Accepted August 1 2013 Available online August 3 2013 This paper discusses the application of the Taguchi method to optimize the machining parameters for machining of GFRP composite in drilling for individual responses such as thrust force and delamination factor. Moreover, a multi-response performance characteristic is used for optimization of process parameters with application of grey relational analysis. An orthogonal array (L9), grey relational generation, grey relational coefficient and grey – fuzzy grade obtained from the grey relational analysis applied as performance index to solve the optimization problem of drilling parameters for GFRP composite. Taguchi orthogonal array, the signal-to-noise ratio, and the analysis of variance are used to investigate the optimal levels of cutting parameters. The confirmation tests are conducted to verify the results and it is observed that grey-fuzzy approach is efficient in determining the optimal cutting parameters. © 2013 Growing Science Ltd. All rights reserved

A New Fault Diagnosis Method of Hydraulic Pressure System Based on Fuzzy Theory

in the thesis we introduce fuzzy mathematic method into fault diagnosis of hydraulic pressure system, bring forward the fuzzy membership grade method which is based on the history data and expert preferential ordinal number, set up a mathematical model of fault diagnosis, and carry out the fuzzy diagnosis on hydraulic pressure system fault in computer. The example shows that this method is advantageous to accuracy and credibility of decision and is valid and viable in the application.

Evaluating the Efficiency of School Preceptors by Fuzzy Risk Analysis

This paper presents a new method for evaluating the efficiency of school preceptors based on fuzzy number arithmetic operations. It uses fuzzy numbers to represent fuzzy grades. The fuzzy weights of criteria are automatically generated from the opinions of evaluators. The simplified fuzzy number arithmetic operations are used for calculating the average of fuzzy numbers. It can evaluate the efficiency of school preceptors in a more flexible and more intelligent manner.

FUZZY GRADE OF THE COMPLETE HYPERGROUPS

This paper continues the study of the connection between hyper- groups and fuzzy sets, investigating the length of the sequence of join spaces associated with a hypergroup. The classes of complete hypergroups and of 1-hypergroups are considered and analyzed in this context. Finally, we give a method to construct a nite hypergroup with the strong fuzzy grade equal to a given natural number

ATANASSOV'S INTUITIONISTIC FUZZY GRADE OF I.P.S. HYPERGROUPS OF ORDER LESS THAN OR EQUAL TO 6

z Abstract The length of the sequence of join spaces and Atanassov's intuitionistic fuzzy sets associated with a hypergroupoid H is called the intuitionistic fuzzy grade of H. In this paper, we consider the class of the complete hypergroups of order less than or equal to 6, determining their intu- itionistic fuzzy grade. 2000 AMS Classification: 20N20; 03E72.

A fast SVM-based wrapper feature selection method driven by a fuzzy complementary criterion

The selection of informative and non-redundant features has become a prominent step in pattern classification. However, despite the intensive research, it is still an open issue to identify valuable feature subsets, especially in highly dimensional feature spaces. This paper proposes a wrapper feature selection method, in the context of support vector machines (SVMs), named Wr-SVM-FuzCoC. Our method combines effectively the advantages of the wrapper and filter approaches, achieving three goals simultaneously: classification performance, dimensionality reduction, and computational efficiency. In the filter part, a forward feature search methodology is developed, driven by a fuzzy complementary criterion, whereby at each iteration a feature is selected that exhibits the maximum additional contribution in regard to the previously selected subset. The quality of single features or feature subsets is assessed via a fuzzy local evaluation criterion with respect to patterns. This is achieved by the so-called fuzzy partition vector (FPV), comprising the fuzzy membership grades of every pattern in their target classes. Derivation of the feature FPVs is accomplished by incorporating a fuzzy output kernel-based support vector machine. The proposed method is favorably compared with existing SVM-based wrapper methods, in terms of performance capability and computational speed. Experimental investigation is carried out using a diverse pool of real datasets, including moderate and high-dimensional feature spaces.

On The Fuzzy Grade of Hypergroups

One constructs the sequence of Join Spaces and of Fuzzy Subsets determined by a hypergroup H, which is the hyperproduct of two equal hypergroups of order 4, such that @(H) = 4. A rich References on the connections between Hyperstructures and Fuzzy Sets completes the paper.

Drilling Wear Recognition Based on Fuzzy C-Means Clustering Algorithm

Fuzzy c-means clustering algorithm was introduced in detail to classify a set of original sampling data on drilling wear in this paper. Simulation results by Matlab programming show that drill wear modes can be successfully represented by four fuzzy grades after fuzzy clustering and classification. The analysis result indicates that fuzzy description can properly reflect drill wear, FCM can effectively identify different wear modes. It is suggested that the severe degree of membership of wear be used as a criterion for replacement of a drill. This technique is simple and is adaptable to different environment in automatic manufacturing

Atanassov's intuitionistic fuzzy grade of a sequence of fuzzy sets and join spaces determined by a hypergraph

The length of the sequence of join spaces and Atanassov's intuitionistic fuzzy sets associated with a hypergroupoid H is called the Atanassov's intuitionistic fuzzy grade of H. In this paper, we consider a certain sequence of Atanassov's intuitionistic fuzzy sets and join spaces determined by a hypergroupoid associated with a hypergraph. We study this sequence in several particular cases and determine the Atanassov's intuitionistic fuzzy grade of a such sequence.

An empirical study on assessing optimal type of distribution park: Applying fuzzy multicriteria Q-analysis method

In this paper, through an empirical study it is explored how respondents viewed suitable modes on locations for developing a distribution park. A fuzzy multiple criteria Q-analysis (MCQA) method is used to empirically evaluate location development for suitable types of international distribution park. The fuzzy MCQA method integrates MCQA, a fuzzy measure method and a fuzzy grade classification method. This improves the constraints evaluated by decision-makers, resulting in an explicit result value for each criterion to be evaluated, greatly decreasing the complexity of the evaluation process and preserving the advantages of the traditional MCQA method.

A Soft Linen Emulsion Rate Intelligent Control System Based on the Domain Interpolation Algorithm with Self Adjusting

A soft linen emulsion rate intelligent control system based on the domain interpolation algorithm with self adjusting is introduced. In the hemp textile industry, soft linen deal processes can directly affect its following process. We take the lead to use the fuzzy control method applied in soft linen emulsion rate of Intelligent Control System. In the research process, the improvement of product quality is not satisfied by basic fuzzy control method. By improving the algorithm, combined advanced interpolation algorithm into fuzzy decision process, that experience rule set has no restriction to access control list, enhanced the flexibility of the method, and obtain fine enough fuzzy control table under the same rules. But as the fuzzy grade classification of fuzzy control input data base on interpolation algorithm can be directly affect the number of control rules, and make more control rules, to affect the realization of deduce speed and storage process, which can not adapt the continence changing ability of soft linen emulsion. We innovate during practice, to combine domain interpolation self adjusting method with fuzzy controller based on intelligent algorithm, to adjust the size of actual measurement and reduce fuzzy control rules, to design a fuzzy controller by domain interpolation algorithm with self adjusting and achieved. After running, the system shows that: 27% Qualified in manual; 48% Qualified by the basic fuzzy control system. 62% Qualified by interpolation algorithm fuzzy control system; 78% Qualified by domain interpolation algorithm with self adjusting fuzzy control system. It proved that, the application of fuzzy control system based on domain interpolation algorithm with self adjusting, can improve control precise which improved production quality.

Axiomatic Theory of Complex Fuzzy Logic and Complex Fuzzy Classes

Complex fuzzy sets, classes, and logic have an important role in applications, such as prediction of periodic events and advanced control systems, where several fuzzy variables interact with each other in a multifaceted way that cannot be represented effectively via simple fuzzy operations such as union, intersection, complement, negation, conjunction and disjunction. The initial formulation of these terms stems from the definition of complex fuzzy grade of membership. The problem, however, with these definitions are twofold: 1) the complex fuzzy membership is limited to polar representation with only one fuzzy component. 2) The definition is based on grade of membership and is lacking the rigor of axiomatic formulation. A new interpretation of complex fuzzy membership enables polar and Cartesian representation of the membership function where the two function components carry uncertain information. Moreover, the new interpretation is used to define complex fuzzy classes and develop an axiomatic based theory of complex propositional fuzzy logic. Additionally, the generalization of the theory to multidimensional fuzzy grades of membership has been demonstrated. In this paper we propose an axiomatic framework for first order predicate complex fuzzy logic and use this framework for axiomatic definition of complex fuzzy classes. We use these rigorous definitions to exemplify inference in complex economic systems. The new framework overcomes the main limitations of current theory and provides several advantages. First, the derivation of the new theory is based on axiomatic approach and does not assume the existence of complex fuzzy sets or complex fuzzy classes. Second, the new form significantly improves the expressive power and inference capability of complex fuzzy logic and class theory. The paper surveys the current state of complex fuzzy sets, complex fuzzy classes, and complex fuzzy logic; and provides an axiomatic basis for first order predicate complex fuzzy logic and complex class theory.

On the grade of a sequence of fuzzy sets and join spaces determined by a hypergraph II

Corsini and Leoreanu (in SABM 34(2):231–242, 2010) proposed an open problem, claiming that for every positive interger s ≥ 3, the fuzzy grade of a particular hypergroupoid Hs might be 1. With the help of MATLAB, in this paper, we obtain that for s = 6, 7, 8, the fuzzy grade of Hs is 5. Thereby solving the open problem in the negative.

Estimating the quality of process yield by fuzzy sets and systems

Fuzzy grading is a multi-class problem, and is used for grading the product according to the degree of fitness for use, customer acceptance or commercial value. In this respect, the production system requires intelligent adjustments. Fuzzy set theory has a variety of applications in different fields. The most fruitful applications are in the field of modeling and control of production systems. Fuzzy logic may be used to control the key quality parameters, grade product quality to reduce the variations and adjust to the specification limits.Fuzzy grading expresses the quality level of product by membership degrees in which belonging or not-belonging to a quality set is gradual. Similarly, the quality control charts are also focused on the reduction of variability and grading the key quality characteristics. The control limits are used to establish the natural spread or range of process so the controller will not signal changes in the process until the natural limits are exceeded. However, there is a logical inconsistency in control chart approaches, due to their crisp grading nature which are expressed as either conforming (good) or nonconforming (poor) to specifications. In this study, a new fuzzy grading approach was developed based on a fuzzy expert system. The outcomes of the fuzzy grading system were clearly proven to be more vigorous and flexible than the crisp control methods.

An Analysis of Rationality of Landuse Based on the Fuzzy Patern Recognition Theory

The study on land use is one of the important content in global change research, and is also of the hot site in the geographical study. Taking rationality of land use of YULIN as the research object, this paper focuses on the method to classify rationality in the view of fuzzy pattern recognition theory. During the analysis, firstly, authors establish classification indexes system by choosing eight factors (cultivated land, woodland, meadow, etc) as the evaluate indexes, and then classification grade criterion determined by classifying eight factors into six grades. Moreover, values of 5,4,3,2,1 are evaluated to the eight indexes of classification grade criterion respectively. In order to evaluate rationality of land use, functions that determine the rationality subjection of fuzzy grade are established on the basis of gray system theory in this paper. In order to insure the oneness of each index of classification indexes system, the authors have transformed original data, and the ratio of land use is got from original data of each county's land use of YULING. By using the function established in this paper, fuzzy grades matrix of each classification index are calculated, and rationality grade of land use of each county's in YULIN is made out by using fuzzy synthetical measurement method. According to the calculation and analyses, rationality grades is made out respectively as following: YULIN, SHENMU, SHUIDE belongs to the grade of 5; HENGSHAN belongs to the grade of 4; FUGU, MIZHI, JIAXIAN, WUBAO, QINGJIAN, ZIZHOU belongs to the grade of 3; JINBIA belongs to the grade of 2; DINGBIAN belongs to the grade of 1, which is close to the fact of land use of YULIN area. This study shows: fuzzy synthetical measurement is a scientific and practical method to be used to classify the grade of land use rationality, which takes on characters of complexity, random city, uncertainty and other non line features.

Application of Fuzzy Mathematics in Diagnosis of WP-JET Engine Ability Failure

In this thesis we draw fuzzy mathematic methods into the diagnosis of failures of WP-jet engine ability, bring forward setting up the fuzzy membership grade method which was based on the weight statistical, found the fuzzy diagnostic model, resolve to several malfunctions, how nicety to find cause failure really reason, and proffer scientific and feasible method to the airplane failure diagnosis service.

The Application of Fuzzy Mathematics in Shearer Malfunction Diagnosis

The subject relationship between the AM500 Shearer malfunction omen and reason is fuzzy, therefore, in the thesis we draw fuzzy mathematic method into Shearer malfunction diagnosis, bring forward setting up the fuzzy membership grade method which is based on the history data and expert preferential ordinal number, found the fuzzy diagnostic model, carry out the fuzzy diagnosis on AM500 Shearer malfunction in computer.