Fuzzy Membership Grades Research Articles

Hypervolemia screening in predialysis healthcare for hemodialysis patients using fuzzy color reason analysis

Maintaining adequate dry weight and fluid volume balance is an important issue for dialysis patients. Malnutrition and sodium intake are the primary factors that cause fluid volume imbalance and changes in body weights. Inadequate dry weight control results in higher levels of blood pressures and is related to various complications, such as volume overload, hypertension, congestive symptoms, and cardiovascular diseases. Moreover, inadequate fluid removal provokes hypotension during dialysis treatment. Thus, we propose an early warning tool based on fuzzy color reason analysis in predialysis healthcare for hypervolemia screening. The anthropometric method is a rapid, non-invasive, and simple technique for estimating the total body water. In this study, Watson standard formula is employed to estimate cross-sectional standard of total body water with the patient characteristics, including gender, age, height, and weight. In contrast to the experienced anthropometric formulas, Watson formula has less than 2% of margin errors and provides a criterion as a reference manner to estimate the total body water in patient’s normal dry weight. In addition, inadequate dry weight and total body water controls will lead to higher blood pressures. The systolic blood pressure is also an indicator to evaluate pre-hypertension of 120–139 mmHg and hypertension of greater than or equal to 140 mmHg. Therefore, the levels of two indicators, total body water and systolic blood pressure, are parameterized with fuzzy membership grades to describe the normal and the specific ranges of undervolemia and hypervolemia. A color reason analysis utilizes a hue–saturation–value color model to design a color perceptual manner for separating normal condition from hypervolemia or undervolemia. Normalized hue angle and saturation value provide a promising visual representation with color codes to realize the patients’ diagnosis. Dialysis patients with hypertension demonstrated that the proposed model can be used in clinical applications. In addition, a healthcare chair is carried out to measure blood pressure and weight in predialysis. The proposed assistant tool integrates an electronic pressure monitor and an electronic weight monitor, and fuzzy color reason analysis is also intended to be established in an intelligent vehicle via a WiFi wireless local area network for cloud computing.

Orthogonal Experimental Design and Fuzzy Grey Relational Analysis for emitter efficiency of the micro-cylindrical combustor with a step

The micro combustor is the key component in the micro thermophotovoltaic (TPV) system. Moreover, emitter efficiency is the most important performance parameter of micro combustors. In this work, a method for evaluating the effects of various factors on the emitter efficiency of micro combustors is proposed. Firstly, the Orthogonal Experimental Design (OED) is employed for obtaining the simulation conditions of test cases. Then, the impacts of four factors (H2/air equivalence ratio, H2 flow rate, wall thermal conductivity and wall emissivity) on emitter efficiency are evaluated by fuzzy membership grades and Euclidean grey relational grades, respectively. Finally, Fuzzy Grey Relational Analysis (FGRA) was employed to make a comprehensive evaluation. Results show that the fuzzy grey relational grades of the four factors are 0.7817, 0.5317, 0.7042 and 0.7018, respectively, meaning that the impacts of the four factors are ranked from the most important to the least important as H2/air equivalence ratio, thermal conductivity, wall emissivity and H2 flow rate. This work offers us great reference value for optimizing micro combustors.

Applying fuzzy logic decision-making approach to analyse the students' self-management perspective in professional education

Professional education has become very much challenging as it is competitive, research-oriented and result-oriented. The students have to struggle and toil to cope with their academic schedule. As the day to day activities are hectic and tightly scheduled, the students have to balance their academic and personal life, for which emotional stability is very much essential. Stress is the main factor responsible for weakening the emotional balance of students. This paper has made an attempt to peruse and analyse the causes and effects of stress using an optimisation technique with fuzzy logic decision-making (FLDM). This paper has applied multi-criteria decision-making (MCDM) tool as demonstrated by Tseng and Klein (1992) for analysing the above scenario and has offered valid findings and valuable suggestions. The purpose of this paper is to understand and demonstrate the concepts of fuzzy logic decision-making (FLDM) approach. The methodology involves identifying the key parameters and developing the fuzzy sets for the same. From the developed fuzzy sets membership grades are calculated, tabulated and ranked as per fuzzy norms. The study is confined to the students of the engineering (UG all years of information technology and mechanical engineering). The study reveals the various stress causing factors and suggests some stress relieving methods.

Applying fuzzy logic decision-making approach to analyse the students' self-management perspective in professional education

Professional education has become very much challenging as it is competitive, research-oriented and result-oriented. The students have to struggle and toil to cope with their academic schedule. As the day to day activities are hectic and tightly scheduled, the students have to balance their academic and personal life, for which emotional stability is very much essential. Stress is the main factor responsible for weakening the emotional balance of students. This paper has made an attempt to peruse and analyse the causes and effects of stress using an optimisation technique with fuzzy logic decision-making (FLDM). This paper has applied multi-criteria decision-making (MCDM) tool as demonstrated by Tseng and Klein (1992) for analysing the above scenario and has offered valid findings and valuable suggestions. The purpose of this paper is to understand and demonstrate the concepts of fuzzy logic decision-making (FLDM) approach. The methodology involves identifying the key parameters and developing the fuzzy sets for the same. From the developed fuzzy sets membership grades are calculated, tabulated and ranked as per fuzzy norms. The study is confined to the students of the engineering (UG all years of information technology and mechanical engineering). The study reveals the various stress causing factors and suggests some stress relieving methods.

Iterative Linear Interpolation Based on Fuzzy Gradient Model for Low-Cost VLSI Implementation

In this paper, we propose an iterative linear interpolation (ILI) algorithm, which produces quadratic ILI polynomials to perform the most cost-effective interpolation among state-of-the-art quadratic and cubic methods. Unlike traditional point and area pixel models, the ILI adopts the fuzzy gradient model to estimate gradients of the target point according to its neighbor sample points in different directions. By weighing the gradients using fuzzy membership grades, the ILI estimates the difference between the target point and its neighbor sample points and finally obtains the target point. In 1-D signal reconstructions, using only three multipliers, the ILI obviously outperforms both conventional quadratic Lagrange interpolation and cubic interpolation. To approximate 2-D signals, we use five 1-D ILIs, which costs only eight multipliers to obtain similar peak signal-to-noise ratio (PSNR) performance but better robustness compared with bi-cubic interpolation. Reusing the ILI polynomials of the previous target point, we further reduce the cost of ILI to three multipliers and eight adders. The VLSI implementation using TSMC 0.18-μm technology shows that only 7256 gates are required for running a 200-MHz, 8-bit input/output, 15-bit fix-point data path, and 10-stage pipelined 2-D ILI, which is the quadratic interpolation of lowest cost but with PSNR performance closest to state-of-the-art bi-cubic methods.

Adaptive Image Generalized Fuzzy Enhancement and Quality Evaluation

Before image feature detection and recognition, image enhancement can highlight the main people or things and their details from foreground, and also can suppress the useless information from background effectively. An algorithm model of adaptive image generalized fuzzy enhancement is established. For all aspects of the algorithm model, a variety of computing forms are put forward, and the evaluation standard of image quality is defined. The principle of algorithm is to achieve space transform between image gray space and generalized fuzzy space using generalized membership transform and its adverse transform. In the process of space transform, the contrast among successive region for space of generalized fuzzy membership grade is enhanced by generalized fuzzy enhancement function. Enhanced images are evaluated by quality standard, and the optimal values of adjustable parameters of membership grade transformation function and the fuzzy enhancement function are selected adaptively based on the optimal quality. Then, the enhanced image with best quality can be obtained. Experiments show that the extracted contour of enhanced image is structured, weak-edge-highlighting, and rich-detail.

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.

Developing an Object-based Hyperspatial Image Classifier with a Case Study Using WorldView-2 Data

Recent advancements in remote sensing technology have provided a plethora of very high spatial resolution images. From pixel-based processing designed for low spatial resolution data, image processing has shifted towards object-based analysis in order to adapt to the hyperspatial nature of currently available remote sensing data. However, standard object-based classifiers work with only object-level summary statistics of the reflectance values and do not sufficiently exploit within-object reflectance pattern. In this research, a novel approach of utilizing the object-level distribution of reflectance values is presented. A fuzzy Kolmogorov-Smirnov based classifier is proposed to provide an object-to-object matching of the empirical distribution of the reflectance values of each object and derive a fuzzy membership grade to each class. This object- based classifier is tested for urban objects recognition from WorldView-2 data. Results indicate at least 10 percent increase in overall classification accuracy using the proposed classifier in comparison to various popular object- and pixel-based classifiers.

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.

A Probabilistic and Decision Attitude Aggregation Operator for Intuitionistic Fuzzy Environment

In this paper, a new operator for aggregation of uncertain information under intuitionistic fuzzy environment is proposed. A novel approach is proposed for the selection of best alternative action in the face of the imprecise probabilities and the complex attitudinal character of the decision makers (DMs). This approach is distinguished with its capacity to accommodate the linguistic specification of probabilities as provided by human experts directly without the need to determine the fuzzy membership grades. The focus is to compute the net payoff for each alternative in the face of uncertain states of nature and DM’s attitude. The proposed operator and the approach are illustrated through two real case studies. C � 2013 Wiley Periodicals, Inc.

Automatic Detection and Reconstruction of Building Radar Footprints From Single VHR SAR Images

The spaceborne synthetic aperture radar (SAR) systems Cosmo-SkyMed, TerraSAR-X, and TanDEM-X acquire imagery with very high spatial resolution (VHR), supporting various important application scenarios, such as damage assessment in urban areas after natural disasters. To ensure a reliable, consistent, and fast extraction of the information from the complex SAR scenes, automatic information extraction methods are essential. Focusing on the analysis of urban areas, which is of prime interest of VHR SAR, in this paper, we present a novel method for the automatic detection and 2-D reconstruction of building radar footprints from VHR SAR scenes. Unlike most of the literature methods, the proposed approach can be applied to single images. The method is based on the extraction of a set of low-level features from the images and on their composition to more structured primitives using a production system. Then, the concept of semantic meaning of the primitives is introduced and used for both the generation of building candidates and the radar footprint reconstruction. The semantic meaning represents the probability that a primitive belongs to a certain scattering class (e.g., double bounce, roof, facade) and has been defined in order to compensate for the lack of detectable features in single images. Indeed, it allows the selection of the most reliable primitives and footprint hypotheses on the basis of fuzzy membership grades. The efficiency of the proposed method is demonstrated by processing a 1-m resolution TerraSAR-X spotbeam scene containing flat- and gable-roof buildings at various settings. The results show that the method has a high overall detection rate and that radar footprints are well reconstructed, in particular for medium and large buildings.

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.

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.

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.

Grey relational analysis model for dynamic hybrid multiple attribute decision making

In this paper, the dynamic hybrid multiple attribute decision making problems, in which the decision information, provided by decision makers at different periods, is expressed in real numbers, interval numbers or linguistic labels (linguistic labels can be described by triangular fuzzy numbers), respectively, are investigated. The method first utilizes three different GRA (grey relational analysis (real-valued GRA, interval-valued GRA and fuzzy-valued GRA) to calculate the individual grey relational degree of each alternative to the positive and negative ideal alternatives based on the decision information expressed in real numbers, interval numbers and linguistic labels, respectively, provided by each decision maker at each period, and then adopt the concept of fuzzy membership grade and clustering to aggregate the grey relational degree of all the evaluated periods. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

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.

Discriminant analysis approach using fuzzy fourfold subspaces model

In this paper, some studies have been made on the essence of a novel fuzzy discriminant analysis (FDA) on the fourfold-objective model (FOM). First, a fourfold-objective model on the discriminant analysis is developed, by which a set of integrated subspaces derived from within-class and between-class scatter matrices are constructed, respectively. Second, an improved FDA (IFDA) algorithm based on the relaxed normalized condition is proposed to achieve the distribution information of each sample represented with fuzzy membership grade, which is incorporated into the redefinition of Fisher's scatter matrices. Therefore, the presented algorithm has the potential to outperform the traditional subspace learning algorithms, especially in the cases of small sample size. Experimental results conducted on the ORL, NUST603, FERET and Yale face image databases demonstrate the effectiveness of the proposed method.

An Evolving Fuzzy Neural Network Based on the Mapping of Similarities

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Evolving fuzzy systems (EFSs) use online learning to extract knowledge from data, perform a high-level adaptation of the network structure, and learn parameters. In this paper, we describe the performance of an EFS that is called similarity mapping, where the training pairs (<formula formulatype="inline"><tex Notation="TeX">${\bf x}_i$</tex></formula> and <formula formulatype="inline"><tex Notation="TeX">${\bf z}_i$</tex></formula>) are compressed into input and output clusters. The predictive error is minimized using a procedure that is very similar to the one implemented in fuzzy-adaptive resonance theory map (ARTMAP) and resource-allocation networks. However, in the recall phase, a fuzzy membership grade is calculated for each input cluster and used in the weighting of the output clusters to obtain the final output vector. By modifying the spread of the cluster membership function, different approximative interpolating functions can be implemented. A similarity function, which was initially proposed for ART 1 implementations, is extended to the processing of analog vectors and used to calculate the membership grades of the input clusters. Several examples show the behavior of the network, as well as its capability to classify, eliminate noise, and predict chaotic time series. </para>

A complete fuzzy discriminant analysis approach for face recognition

In this paper, some studies have been made on the essence of fuzzy linear discriminant analysis (F-LDA) algorithm and fuzzy support vector machine (FSVM) classifier, respectively. As a kernel-based learning machine, FSVM is represented with the fuzzy membership function while realizing the same classification results with that of the conventional pair-wise classification. It outperforms other learning machines especially when unclassifiable regions still remain in those conventional classifiers. However, a serious drawback of FSVM is that the computation requirement increases rapidly with the increase of the number of classes and training sample size. To address this problem, an improved FSVM method that combines the advantages of FSVM and decision tree, called DT-FSVM, is proposed firstly. Furthermore, in the process of feature extraction, a reformative F-LDA algorithm based on the fuzzy k-nearest neighbors (FKNN) is implemented to achieve the distribution information of each original sample represented with fuzzy membership grade, which is incorporated into the redefinition of the scatter matrices. In particular, considering the fact that the outlier samples in the patterns may have some adverse influence on the classification result, we developed a novel F-LDA algorithm using a relaxed normalized condition in the definition of fuzzy membership function. Thus, the classification limitation from the outlier samples is effectively alleviated. Finally, by making full use of the fuzzy set theory, a complete F-LDA (CF-LDA) framework is developed by combining the reformative F-LDA (RF-LDA) feature extraction method and DT-FSVM classifier. This hybrid fuzzy algorithm is applied to the face recognition problem, extensive experimental studies conducted on the ORL and NUST603 face images databases demonstrate the effectiveness of the proposed algorithm.

Web retrieval: Techniques for the aggregation and selection of queries and answers

In this paper we present a set of techniques for grouping and aggregating queries and search results, in the context of an Internet “search engine.'' (1) In the case of the initial grouping of the queries, we consider the Fuzzy c-Means1 and Kohonen SOM2 techniques. It is proposed that FCM may be more adequate than k-Means3 for the grouping of certain data types. We evaluate how we can use FCM to calculate the fuzzy membership grades for a set of Web queries and their corresponding results. (2) In the case of the aggregation of data from different information sources (the clustering techniques), we will consider weighted ordered weighted averaging (WOWA).4 WOWA is used to choose the most adequate cluster and to identify the historical query in that cluster, which is most similar to a new query. We will see that the WOWA operator offers a wide flexibility for data processing. © 2008 Wiley Periodicals, Inc.