Membership Functions for Fuzzy Focal Elements
Directory of Open Access Journals (Sweden)
Porębski Sebastian
2016-09-01
Full Text Available 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.
Image segmentation based on scaled fuzzy membership functions
DEFF Research Database (Denmark)
Jantzen, Jan; Ring,, P.; Christiansen, Pernille
1993-01-01
As a basis for an automated interpretation of magnetic resonance images, the authors propose a fuzzy segmentation method. The method uses five standard fuzzy membership functions: small, small medium, medium, large medium, and large. The method fits these membership functions to the modes...... of interest in the image histogram by means of a piecewise-linear transformation. A test example is given concerning a human head image, including a sensitivity analysis based on the fuzzy area measure. The method provides a rule-based interface to the physician...
Uncertainty in Fuzzy Membership Functions for a River Water Quality Management Problem
Karmakar, Subhankar; Mujumdar, PP
2004-01-01
Uncertainty associated with fuzzy membership functions for a water quality management problem is addressed through interval grey numbers. The lower and upper bounds of the membership functions are expressed as interval grey numbers, and the membership functions are modeled as imprecise membership functions. A grey fuzzy optimization model for water quality management of a river system is developed. Application of the optimization model with imprecise membership functions is illustrated with a...
Memristor Crossbar-based Hardware Implementation of Fuzzy Membership Functions
Merrikh-Bayat, Farnood
2010-01-01
In May 1, 2008, researchers at Hewlett Packard (HP) announced the first physical realization of a fundamental circuit element called memristor that attracted so much interest worldwide. This newly found element can easily be combined with crossbar interconnect technology which this new structure has opened a new field in designing configurable or programmable electronic systems. These systems in return can have applications in signal processing and artificial intelligence. In this paper, based on the simple memristor crossbar structure, we propose new and simple circuits for hardware implementation of fuzzy membership functions. In our proposed circuits, these fuzzy membership functions can have any shapes and resolutions. In addition, these circuits can be used as a basis in the construction of evolutionary systems.
Fuzzy adaptive learning control network with sigmoid membership function
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
To get simpler operation in modified fuzzy adaptive learning control network (FALCON) in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing (SA) learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived;and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.
Fuzzy Programming With Quadratic Membership Functions For Multi-objective Transportation Problem
Directory of Open Access Journals (Sweden)
Satyanarayana Murthy Akkapeddi
2015-08-01
Full Text Available In the present paper, a fuzzy programming model with quadratic membership functions has been developed for the solution of a Multi-Objective Transportation problem. In literature, several fuzzy programming approaches exist with various types of membership functions such as linear, exponential, hyperbolic etc. These membership functions are defined, by taking the lower and upper values of the objective functions into account. In some cases, these methods fail to obtain an integer compromise optimal solution. In the present method, two coefficients of the quadratic membership functions are determined by the lower and upper values of the objective functions. The other coefficient is taken as a variable in the fuzzy programming approach. This means that the membership curve is fixed at the two end points and set free in between. Application of the method on numerical examples proved that the approach could generate integer compromise optimal solutions.
A genetic algorithms approach for altering the membership functions in fuzzy logic controllers
Shehadeh, Hana; Lea, Robert N.
1992-01-01
Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.
Solutions of Multi Objective Fuzzy Transportation Problems with Non-Linear Membership Functions
Directory of Open Access Journals (Sweden)
Dr. M. S. Annie Christi
2016-11-01
Full Text Available Multi-objective transportation problem with fuzzy interval numbers are considered. The solution of linear MOTP is obtained by using non-linear membership functions. The optimal compromise solution obtained is compared with the solution got by using a linear membership function. Some numerical examples are presented to illustrate this.
Fuzzy optimization of primal-dual pair using piecewise linear membership functions
Directory of Open Access Journals (Sweden)
Pandey D.
2012-01-01
Full Text Available Present paper improves the model of Bector and Chandra [Fuzzy Sets and Systems, 125 (2002 317-325] on duality in fuzzy linear programming by using non-linear membership functions. Numerical problem discussed by these authors has also been worked out through our non-linear model to demonstrate improved optimality of the results.
Properties of Fuzzy Entropy Based on the Shape Change of Membership Function
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Modification of a fuzzy partition often leads to the change of fuzziness of a fuzzy system. Researches on the change of fuzzy entropy of a fuzzy set, responding to shape alteration of membership function, therefore, play a significant role in analysis of the change of fuzziness of a fuzzy system because a fuzzy partition consists of a set of fuzzy sets which satisfy some special constraints. This paper has shown several results about entropy changes of a fuzzy set. First, the entropies of two same type of fuzzy sets have a constant proportional relationship which depends on the ratio of the sizes of their support intervals. Second, as for Triangular Fuzzy Numbers (TFNs), the entropies of any two TFNs which can not be always the same type, also,have a constant proportional relationship which depends on the ratio of the sizes of their support intervals. Hence, any two TFNs with the same sizes of support intervals have the same entropies. Third, concerning two Triangular Fuzzy Sets (TFSs) with same sizes of support intervals and different heights, the relationship of their entropies lies on their height.Finally, we point it out a mistake that Chen's assertion that the entropy of resultant fuzzy set of elevation operation is directly proportional to that of the original one while elevation factor just acts as a proportional factor. These results should contribute to the analysis and design of a fuzzy system.
Application of a New Membership Function in Nonlinear Fuzzy PID Controllers with Variable Gains
Directory of Open Access Journals (Sweden)
Xuda Zhang
2014-01-01
Full Text Available This paper proposes a nonlinear fuzzy PID control algorithm, whose membership function (MF is adjustable, is universal, and has a wide adjustable range. Appling this function to fuzzy control theory will increase system’s tunability. The continuity of this function is proved. This method was employed in the simulation and HIL experiments. Effectiveness and feasibility of this function are demonstrated in the results.
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
An output expression of a class of dual-input single-output fuzzy controllers using pseudo trapezoid shaped membership function is given. By structure analysis it is proved that this class of fuzzy controllers is the sum of a global two-dimensional multi-level relay and a local linear or nonlinear proportional-integral or proportional-differential controller. And the output of this class of fuzzy controllers is a continuous, non-decreasing function of its input variables. These and other meaningful results derived from structure analysis based on the output expressions can guide the design of fuzzy controllers.
Institute of Scientific and Technical Information of China (English)
曾珂; 张乃尧; 徐文立
2000-01-01
An output expression of a class of dual-input single-output fuzzy controllers using pseudo trapezoid shaped membership function is given. By structure analysis it is prdved that this class of fuzzy controllers is the sum of a global two-dimensional multi-level relay and a local linear or nonlinear proportional-integral or proportional-differential controller. And the output of this class of fuzzy controllers is a continuous, non-decreasing function of its input variables. These and other meaningful results derived from structure analysis based on the output expressions can guide the design of fuzzy controllers.
Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function
Directory of Open Access Journals (Sweden)
Jian Shi
2016-11-01
Full Text Available Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied in the fields of statistics and machine learning. In this paper a novel fuzzy support vector machine (SVM credit scoring model is proposed for credit risk analysis, in which fuzzy membership is adopted to indicate different contribution of each input point to the learning of SVM classification hyperplane. Considering the methodological consistency, support vector data description (SVDD is introduced to construct the fuzzy membership function and to reduce the effect of outliers and noises. The SVDD-based fuzzy SVM model is tested against the traditional fuzzy SVM on two real-world datasets and the research results confirm the effectiveness of the presented method.
Fuzzy Multi-Objective Transportation Planning with Modified S-Curve Membership Function
Peidro, D.; Vasant, P.
2009-08-01
In this paper, the S-Curve membership function methodology is used in a transportation planning decision (TPD) problem. An interactive method for solving multi-objective TPD problems with fuzzy goals, available supply and forecast demand is developed. The proposed method attempts simultaneously to minimize the total production and transportation costs and the total delivery time with reference to budget constraints and available supply, machine capacities at each source, as well as forecast demand and warehouse space constraints at each destination. We compare in an industrial case the performance of S-curve membership functions, representing uncertainty goals and constraints in TPD problems, with linear membership functions.
Lam, Hak-Keung
2016-01-01
This book presents recent research on the stability analysis of polynomial-fuzzy-model-based control systems where the concept of partially/imperfectly matched premises and membership-function dependent analysis are considered. The membership-function-dependent analysis offers a new research direction for fuzzy-model-based control systems by taking into account the characteristic and information of the membership functions in the stability analysis. The book presents on a research level the most recent and advanced research results, promotes the research of polynomial-fuzzy-model-based control systems, and provides theoretical support and point a research direction to postgraduate students and fellow researchers. Each chapter provides numerical examples to verify the analysis results, demonstrate the effectiveness of the proposed polynomial fuzzy control schemes, and explain the design procedure. The book is comprehensively written enclosing detailed derivation steps and mathematical derivations also for read...
An improved a-cut approach to transforming fuzzy membership function into basic belief assignment
Institute of Scientific and Technical Information of China (English)
Yang Yi; X.Rong Li; Han Deqiang
2016-01-01
In practical applications, pieces of evidence originated from different sources might be modeled by different uncertainty theories. To implement the evidence combination under the Dempster–Shafer evidence theory (DST) framework, transformations from the other type of uncer-tainty representation into the basic belief assignment are needed. a-Cut is an important approach to transforming a fuzzy membership function into a basic belief assignment, which provides a bridge between the fuzzy set theory and the DST. Some drawbacks of the traditional a-cut approach caused by its normalization step are pointed out in this paper. An improved a-cut approach is pro-posed, which can counteract the drawbacks of the traditional a-cut approach and has good prop-erties. Illustrative examples, experiments and related analyses are provided to show the rationality of the improved a-cut approach.
An improved α-cut approach to transforming fuzzy membership function into basic belief assignment
Directory of Open Access Journals (Sweden)
Yang Yi
2016-08-01
Full Text Available In practical applications, pieces of evidence originated from different sources might be modeled by different uncertainty theories. To implement the evidence combination under the Dempster–Shafer evidence theory (DST framework, transformations from the other type of uncertainty representation into the basic belief assignment are needed. α-Cut is an important approach to transforming a fuzzy membership function into a basic belief assignment, which provides a bridge between the fuzzy set theory and the DST. Some drawbacks of the traditional α-cut approach caused by its normalization step are pointed out in this paper. An improved α-cut approach is proposed, which can counteract the drawbacks of the traditional α-cut approach and has good properties. Illustrative examples, experiments and related analyses are provided to show the rationality of the improved α-cut approach.
Fuzzy logic membership implementation using optical hardware components
Moniem, T. A.; Saleh, M. H.
2012-10-01
Intelligent control techniques consist of knowledge-based expert or fuzzy logic control. One obvious drawback in many such applications is that fuzzy logic memberships are implemented at the lowest level. In high-bandwidth processes, this form of fuzzy logic membership implementation would require high speed and accuracy in the presence of strong nonlinearities and dynamic coupling. This paper presents a novel methodology called the Opto-fuzzy method to design a fuzzy logic membership using an optical hardware component. The proposed scheme is applied to triangular-shaped and half trapezoidal-shaped membership functions.
Narimani, Mohammand; Lam, H K; Dilmaghani, R; Wolfe, Charles
2011-06-01
Relaxed linear-matrix-inequality-based stability conditions for fuzzy-model-based control systems with imperfect premise matching are proposed. First, the derivative of the Lyapunov function, containing the product terms of the fuzzy model and fuzzy controller membership functions, is derived. Then, in the partitioned operating domain of the membership functions, the relations between the state variables and the mentioned product terms are represented by approximated polynomials in each subregion. Next, the stability conditions containing the information of all subsystems and the approximated polynomials are derived. In addition, the concept of the S-procedure is utilized to release the conservativeness caused by considering the whole operating region for approximated polynomials. It is shown that the well-known stability conditions can be special cases of the proposed stability conditions. Simulation examples are given to illustrate the validity of the proposed approach.
Fuzzy - Rough Feature Selection With {\\Pi}- Membership Function For Mammogram Classification
Thangavel, K
2012-01-01
Breast cancer is the second leading cause for death among women and it is diagnosed with the help of mammograms. Oncologists are miserably failed in identifying the micro calcification at the early stage with the help of the mammogram visually. In order to improve the performance of the breast cancer screening, most of the researchers have proposed Computer Aided Diagnosis using image processing. In this study mammograms are preprocessed and features are extracted, then the abnormality is identified through the classification. If all the extracted features are used, most of the cases are misidentified. Hence feature selection procedure is sought. In this paper, Fuzzy-Rough feature selection with {\\pi} membership function is proposed. The selected features are used to classify the abnormalities with help of Ant-Miner and Weka tools. The experimental analysis shows that the proposed method improves the mammograms classification accuracy.
Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty
Starczewski, Janusz T
2013-01-01
This book generalizes fuzzy logic systems for different types of uncertainty, including - semantic ambiguity resulting from limited perception or lack of knowledge about exact membership functions - lack of attributes or granularity arising from discretization of real data - imprecise description of membership functions - vagueness perceived as fuzzification of conditional attributes. Consequently, the membership uncertainty can be modeled by combining methods of conventional and type-2 fuzzy logic, rough set theory and possibility theory. In particular, this book provides a number of formulae for implementing the operation extended on fuzzy-valued fuzzy sets and presents some basic structures of generalized uncertain fuzzy logic systems, as well as introduces several of methods to generate fuzzy membership uncertainty. It is desirable as a reference book for under-graduates in higher education, master and doctor graduates in the courses of computer science, computational intelligence, or...
Jati, A; Singh, G; Koley, S; Konar, A; Ray, A K; Chakraborty, C
2015-03-01
Medical image segmentation demands higher segmentation accuracy especially when the images are affected by noise. This paper proposes a novel technique to segment medical images efficiently using an intuitionistic fuzzy divergence-based thresholding. A neighbourhood-based membership function is defined here. The intuitionistic fuzzy divergence-based image thresholding technique using the neighbourhood-based membership functions yield lesser degradation of segmentation performance in noisy environment. Its ability in handling noisy images has been validated. The algorithm is independent of any parameter selection. Moreover, it provides robustness to both additive and multiplicative noise. The proposed scheme has been applied on three types of medical image datasets in order to establish its novelty and generality. The performance of the proposed algorithm has been compared with other standard algorithms viz. Otsu's method, fuzzy C-means clustering, and fuzzy divergence-based thresholding with respect to (1) noise-free images and (2) ground truth images labelled by experts/clinicians. Experiments show that the proposed methodology is effective, more accurate and efficient for segmenting noisy images.
DEFF Research Database (Denmark)
Boiocchi, Riccardo; Gernaey, Krist; Sin, Gürkan
2016-01-01
A methodology is developed to systematically design the membership functions of fuzzy-logic controllers for multivariable systems. The methodology consists of a systematic derivation of the critical points of the membership functions as a function of predefined control objectives. Several...... rules, determine the long term reachability of the control objectives by the fuzzy logic controller. The methodology is highlighted using a single-stage side-stream partial nitritation/Anammox reactor as a case study. As a result, a new fuzzy-logic controller for high and stable total nitrogen removal......, the controller was tested, and showed robustness, against measurement noise levels typical for wastewater sensors. A feedforward-feedback configuration using the present controller would give even better performance. In comparison, a previously developed fuzzy-logic controller using merely expert and intuitive...
Special functions in Fuzzy Analysis
Directory of Open Access Journals (Sweden)
Angel Garrido
2006-01-01
Full Text Available In the treatment of Fuzzy Logic an useful tool appears: the membership function, with the information about the degree of completion of a condition which defines the respective Fuzzy Set or Fuzzy Relation. With their introduction, it is possible to prove some results on the foundations of Fuzzy Logic and open new ways in Fuzzy Analysis.
Research on projected fuzzy subset membership function%关于落影模糊集隶属函数的探讨(续)
Institute of Scientific and Technical Information of China (English)
薛秀云; 江佩荣
2000-01-01
Using the basic theory of fuzzy subset projected from random intervals, based on research on the compute of membership function of fuzzy subset projected from two random variables, fuzzy uniform distribution, fuzzy exponential distribution, fuzzy X2 distribution, fuzzy 0～1 distribution, fuzzy geometric distribution, fuzzy binomial distribution and poisson distribution are established in this paper. The calculation formula of their membership function is given, and their basic characters are studied.%利用随机区间落影模糊集的基本理论，在研究了两个随机变量导出的落影模糊集隶属函数解析计算公式的基础上，进一步建立了模糊均匀分布、模糊指数分布、模糊X2-分布、模糊0～1分布、模糊几何分布、模糊二项分布和模糊泊松分布．给出了它们隶属函数的解析计算公式，研究了其基本性态．
Boiocchi, Riccardo; Gernaey, Krist V; Sin, Gürkan
2016-10-01
A methodology is developed to systematically design the membership functions of fuzzy-logic controllers for multivariable systems. The methodology consists of a systematic derivation of the critical points of the membership functions as a function of predefined control objectives. Several constrained optimization problems corresponding to different qualitative operation states of the system are defined and solved to identify, in a consistent manner, the critical points of the membership functions for the input variables. The consistently identified critical points, together with the linguistic rules, determine the long term reachability of the control objectives by the fuzzy logic controller. The methodology is highlighted using a single-stage side-stream partial nitritation/Anammox reactor as a case study. As a result, a new fuzzy-logic controller for high and stable total nitrogen removal efficiency is designed. Rigorous simulations are carried out to evaluate and benchmark the performance of the controller. The results demonstrate that the novel control strategy is capable of rejecting the long-term influent disturbances, and can achieve a stable and high TN removal efficiency. Additionally, the controller was tested, and showed robustness, against measurement noise levels typical for wastewater sensors. A feedforward-feedback configuration using the present controller would give even better performance. In comparison, a previously developed fuzzy-logic controller using merely expert and intuitive knowledge performed worse. This proved the importance of using a systematic methodology for the derivation of the membership functions for multivariable systems. These results are promising for future applications of the controller in real full-scale plants. Furthermore, the methodology can be used as a tool to help systematically design fuzzy logic control applications for other biological processes.
Directory of Open Access Journals (Sweden)
Juan Carlos Figueroa García
2011-12-01
The presented approach uses an iterative algorithm which finds stable solutions to problems with fuzzy parameter sinboth sides of an FLP problem. The algorithm is based on the soft constraints method proposed by Zimmermann combined with an iterative procedure which gets a single optimal solution.
Indian Academy of Sciences (India)
Brijesh Kumar Sriwastava; Subhadip Basu; Ujjwal Maulik
2015-10-01
Protein–protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.
Sriwastava, Brijesh Kumar; Basu, Subhadip; Maulik, Ujjwal
2015-10-01
Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.
A Fuzzy Description Logic with Automatic Object Membership Measurement
Cai, Yi; Leung, Ho-Fung
In this paper, we propose a fuzzy description logic named f om -DL by combining the classical view in cognitive psychology and fuzzy set theory. A formal mechanism used to determine object memberships automatically in concepts is also proposed, which is lacked in previous work fuzzy description logics. In this mechanism, object membership is based on the defining properties of concept definition and properties in object description. Moreover, while previous works cannot express the qualitative measurements of an object possessing a property, we introduce two kinds of properties named N-property and L-property, which are quantitative measurements and qualitative measurements of an object possessing a property respectively. The subsumption and implication of concepts and properties are also explored in our work. We believe that it is useful to the Semantic Web community for reasoning the fuzzy membership of objects for concepts in fuzzy ontologies.
Nelson, Bruce N.; Birenzvige, Amnon
2004-04-01
In support of the Disparate Sensor Integration (DSI) Program a number of imaging sensors were fielded to determine the feasibility of using information from these systems to discriminate between chemical and conventional munitions. The camera systems recorded video from 160 training and 100 blind munitions detonation events. Two types of munitions were used; 155 mm conventional rounds and 155 mm chemical simulant rounds. In addition two different modes of detonation were used with these two classes of munitions; detonation on impact (point detonation) and detonation in the air (airblasts). The cameras fielded included two visible wavelength cameras, a near infrared camera (peak responsivity of approximately 1μm), a mid wavelength infrared camera system (3 μm to 5 μm) and a long wavelength infrared camera system (7.5 μm to 13 μm). Our recent work has involved developing Linguistic-Fuzzy Classifiers for performing munitions detonation classification with the DSI visible and infrared imaging sensors data sets. In this initial work, the classifiers were heuristically developed based on analyses of the training data features distributions. In these initial classification systems both the membership functions and the feature weights were hand developed and tuned. We have recently developed new methodologies to automatically generate membership functions and weights in Linguistic-Fuzzy Classifiers. This paper will describe this new methodology and provide an example of its efficacy for separating munitions detonation events into either air or point detonation. This is a critical initial step in achieving the overall goal of DSI; the classification of detonation events as either chemical or conventional. Further, the detonation mode is important as it significantly effects the dispersion of agents. The results presented in this paper clearly demonstrate that the automatically developed classifiers perform as well in this classification task as the previously developed
Voltage sag loss estimation based on fuzzy membership function%基于模糊隶属函数的电压骤降损失估算
Institute of Scientific and Technical Information of China (English)
赵会茹; 欧大昌; 李天友; 张奇
2012-01-01
通过分析电压骤降及敏感设备电压骤降耐受能力的特点,扩展用户的电压骤降耐受曲线的状态区域.以骤降幅值和持续时间为电压骤降的主要特征量,提出基于二维隶属分布函数的电压骤降经济损失估算方法.首先以用户历史生产中断损失为基础,将骤降幅值和持续时间设为自变量,通过确定合理的生产中断模糊隶属函数形式与参数,获取电压骤降对中断故障的隶属度;然后以隶属度为转换因子,将电压骤降的经济损失转换为不完全的生产中断损失.实例分析证明了所提方法可行,且适用于大部分敏感设备.%The user's state area of voltage sag tolerance curve is expanded by analyzing the characteristics of voltage sag and the voltage sag tolerance ability of sensitive equipment. An estimation method based on the two-dimensional membership distribution function is proposed for the economic loss of voltage sag,which takes the sag magnitude and duration as its main characteristic variants. With the sag magnitude and duration as the independent variables, the membership degree of voltage sag towards the interrupt fault is obtained by setting the reasonable form and parameters of production interruption fuzzy membership function based on user's historical production interrupt costs,and with the membership degree as the conversion factor,the economic loss of voltage sag is then converted into the incomplete production interrupt cost. Example analysis proves the proposed method is feasible and applicable for most sensitive equipments.
Indian Academy of Sciences (India)
Amit Kumar; Manjot Kaur
2014-02-01
Several authors have proposed different methods for solving fuzzy minimum cost flow (MCF) problems. In this paper, some single and multi-objective fuzzy MCF problems are chosen which cannot be solved by using any of the existing methods and a new method is proposed for solving such type of problems. The main advantage of the proposed method over existing methods is that the fuzzy MCF problems which can be solved by using the existing methods can also be solved by the proposed method. But, there exist several fuzzy MCF problems which can be solved only by using the proposed method i.e., it is not possible to solve these problems by using the existing methods. To illustrate the proposed method and also to show the advantages of the proposed method over existing methods some single and multiobjective fuzzy MCF problems which cannot be solved by using the existing methods are solved by using the proposed method and the obtained results are discussed.
Feature Selection Based on Adaptive Fuzzy Membership Functions%基于自适应隶属度函数的特征选择
Institute of Scientific and Technical Information of China (English)
谢衍涛; 桑农; 张天序
2006-01-01
Neuro-fuzzy (NF) networks are adaptive fuzzy inference systems (FIS) and have been applied to feature selection by some researchers. However, their rule number will grow exponentially as the data dimension increases. On the other hand, feature selection algorithms with artificial neural networks (ANN) usually require normalization of input data, which will probably change some characteristics of original data that are important for classification. To overcome the problems mentioned above, this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron (MLP) to form a new artificial neural network. Furthermore, fuzzification strategy and feature measurement based on membership space are proposed for feature selection.Finally, experiments with both natural and artificial data are carried out to compare with other methods, and the results approve the validity of the algorithm.
Directory of Open Access Journals (Sweden)
Artur Zbroński
2013-12-01
Full Text Available This paper introduces choosing location for additional reactive power sources for a power grid. The fuzzy logic method for localization is described, with special attention focused on choosing the proper fuzzification method, used for reasoning. Results of using such a method are presented for an example power network.
Models of neural networks with fuzzy activation functions
Nguyen, A. T.; Korikov, A. M.
2017-02-01
This paper investigates the application of a new form of neuron activation functions that are based on the fuzzy membership functions derived from the theory of fuzzy systems. On the basis of the results regarding neuron models with fuzzy activation functions, we created the models of fuzzy-neural networks. These fuzzy-neural network models differ from conventional networks that employ the fuzzy inference systems using the methods of neural networks. While conventional fuzzy-neural networks belong to the first type, fuzzy-neural networks proposed here are defined as the second-type models. The simulation results show that the proposed second-type model can successfully solve the problem of the property prediction for time – dependent signals. Neural networks with fuzzy impulse activation functions can be widely applied in many fields of science, technology and mechanical engineering to solve the problems of classification, prediction, approximation, etc.
Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.
Sriwastava, Brijesh K; Basu, Subhadip; Maulik, Ujjwal
2015-01-01
Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.
Rough function model and rough membership function
Institute of Scientific and Technical Information of China (English)
Wang Yun; Guan Yanyong; Huang Zhiqin
2008-01-01
Two pairs of approximation operators, which are the scale lower and upper approximations as well as the real line lower and upper approximations, are defined. Their properties and antithesis characteristics are analyzed. The rough function model is generalized based on rough set theory, and the scheme of rough function theory is made more distinct and complete. Therefore, the transformation of the real function analysis from real line to scale is achieved. A series of basic concepts in rough function model including rough numbers, rough intervals, and rough membership functions are defined in the new scheme of the rough function model. Operating properties of rough intervals similar to rough sets are obtained. The relationship of rough inclusion and rough equality of rough intervals is defined by two kinds of tools, known as the lower (upper) approximation operator in real numbers domain and rough membership functions. Their relative properties are analyzed and proved strictly, which provides necessary theoretical foundation and technical support for the further discussion of properties and practical application of the rough function model.
Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru
1991-01-01
Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.
Dasgupta, Arunima; Sastry, K. L. N.; Dhinwa, P. S.; Rathore, V. S.; Nathawat, M. S.
2013-08-01
Desertification risk assessment is important in order to take proper measures for its prevention. Present research intends to identify the areas under risk of desertification along with their severity in terms of degradation in natural parameters. An integrated model with fuzzy membership analysis, fuzzy rule-based inference system and geospatial techniques was adopted, including five specific natural parameters namely slope, soil pH, soil depth, soil texture and NDVI. Individual parameters were classified according to their deviation from mean. Membership of each individual values to be in a certain class was derived using the normal probability density function of that class. Thus if a single class of a single parameter is with mean μ and standard deviation σ, the values falling beyond μ + 2 σ and μ - 2 σ are not representing that class, but a transitional zone between two subsequent classes. These are the most important areas in terms of degradation, as they have the lowest probability to be in a certain class, hence highest probability to be extended or narrowed down in next or previous class respectively. Eventually, these are the values which can be easily altered, under extrogenic influences, hence are identified as risk areas. The overall desertification risk is derived by incorporating the different risk severity of each parameter using fuzzy rule-based interference system in GIS environment. Multicriteria based geo-statistics are applied to locate the areas under different severity of desertification risk. The study revealed that in Kota, various anthropogenic pressures are accelerating land deterioration, coupled with natural erosive forces. Four major sources of desertification in Kota are, namely Gully and Ravine erosion, inappropriate mining practices, growing urbanization and random deforestation.
On Fourier series of fuzzy-valued functions.
Kadak, Uğur; Başar, Feyzi
2014-01-01
Fourier analysis is a powerful tool for many problems, and especially for solving various differential equations of interest in science and engineering. In the present paper since the utilization of Zadeh's Extension principle is quite difficult in practice, we prefer the idea of level sets in order to construct a fuzzy-valued function on a closed interval via related membership function. We derive uniform convergence of a fuzzy-valued function sequences and series with level sets. Also we study Hukuhara differentiation and Henstock integration of a fuzzy-valued function with some necessary inclusions. Furthermore, Fourier series of periodic fuzzy-valued functions is defined and its complex form is given via sine and cosine fuzzy coefficients with an illustrative example. Finally, by using the Dirichlet kernel and its properties, we especially examine the convergence of Fourier series of fuzzy-valued functions at each point of discontinuity, where one-sided limits exist.
On fuzzy almost continuous convergence in fuzzy function spaces
Directory of Open Access Journals (Sweden)
A.I. Aggour
2013-10-01
Full Text Available In this paper, we study the fuzzy almost continuous convergence of fuzzy nets on the set FAC(X, Y of all fuzzy almost continuous functions of a fuzzy topological space X into another Y. Also, we introduce the notions of fuzzy splitting and fuzzy jointly continuous topologies on the set FAC(X, Y and study some of its basic properties.
Set-membership fuzzy filtering for nonlinear discrete-time systems.
Yang, Fuwen; Li, Yongmin
2010-02-01
This paper is concerned with the set-membership filtering (SMF) problem for discrete-time nonlinear systems. We employ the Takagi-Sugeno (T-S) fuzzy model to approximate the nonlinear systems over the true value of state and to overcome the difficulty with the linearization over a state estimate set rather than a state estimate point in the set-membership framework. Based on the T-S fuzzy model, we develop a new nonlinear SMF estimation method by using the fuzzy modeling approach and the S-procedure technique to determine a state estimation ellipsoid that is a set of states compatible with the measurements, the unknown-but-bounded process and measurement noises, and the modeling approximation errors. A recursive algorithm is derived for computing the ellipsoid that guarantees to contain the true state. A smallest possible estimate set is recursively computed by solving the semidefinite programming problem. An illustrative example shows the effectiveness of the proposed method for a class of discrete-time nonlinear systems via fuzzy switch.
New Methods of Fitting the Membership Function of Oceanic Water Masses
Institute of Scientific and Technical Information of China (English)
LI Fengqi; XIE Jun; LI Yao
2004-01-01
After reviewing the analytical theories of T-S curve, some methods of T-S relationship, and fuzzy sets for studying water masses, new methods of fitting the membership function of oceanic water masses are presented based on the characteristics of T-S curve family of oceanic water masses. The membership functions of oceanic Subsurface Water Mass with high salinity and Intermediate Water Mass with low salinity are fitted and discussed using the new methods. The proposed methods are useful in analyzing the mixing and modifying processes of these water masses, especially in tracing their sources.The principles and formulae of the new methods and examples are given.
Axiomatic of Fuzzy Complex Numbers
Angel Garrido
2012-01-01
Fuzzy numbers are fuzzy subsets of the set of real numbers satisfying some additional conditions. Fuzzy numbers allow us to model very difficult uncertainties in a very easy way. Arithmetic operations on fuzzy numbers have also been developed, and are based mainly on the crucial Extension Principle. When operating with fuzzy numbers, the results of our calculations strongly depend on the shape of the membership functions of these numbers. Logically, less regular membership functions may lead ...
Energy Technology Data Exchange (ETDEWEB)
Singh, Jeetendra B.; Reddy, Vijay S.; Jana, Soumya [Indian Institute of Technology, Hyderabad (India). Dept. of Electrical Engineering; De, Swades [Indian Institute of Technology, Delhi (India). Dept. of Electrical Engineering
2013-07-01
Air quality is an important determinant of individual as well as broader well-being. Major pollutants include gasses as well as assorted suspended particulate matter (PM). In this paper, we focus on PM10, which are a collection of particles with median aerodynamic diameter less than 10 {mu}m that remains suspended in the air for long periods. PM10, usually consist of smoke, dirt and dust particles, as well as spores and pollen, could easily be inhaled deep into lung. As a result, high outdoor PM10 concentration poses significant health hazard, and accurate modeling and prediction of health risk due to PM10 assume importance in pollution and public health management. In this backdrop, we propose an improved health risk assessment technique, and demonstrate its efficacy using widely used California PM10 database. At the heart of the proposed method lies indicator kriging, a well-known risk estimation technique. However, improved assessment of subjective health risk is achieved by posing the problem in a fuzzy setting, and optimizing the associated membership functions. In particular, we employ particle swarm optimization (PSO) algorithm, which has been motivated by natural behavior of organisms such as fish-schooling and bird flocking, and proven effective in various optimization contexts. We apply the fuzzy PSO membership grade kriging technique to predict the PM10 spatial distribution over the entire California state. (orig.)
A fuzzy-set-theory-based approach to analyse species membership in DNA barcoding.
Zhang, A-B; Muster, C; Liang, H-B; Zhu, C-D; Crozier, R; Wan, P; Feng, J; Ward, R D
2012-04-01
Reliable assignment of an unknown query sequence to its correct species remains a methodological problem for the growing field of DNA barcoding. While great advances have been achieved recently, species identification from barcodes can still be unreliable if the relevant biodiversity has been insufficiently sampled. We here propose a new notion of species membership for DNA barcoding-fuzzy membership, based on fuzzy set theory-and illustrate its successful application to four real data sets (bats, fishes, butterflies and flies) with more than 5000 random simulations. Two of the data sets comprise especially dense species/population-level samples. In comparison with current DNA barcoding methods, the newly proposed minimum distance (MD) plus fuzzy set approach, and another computationally simple method, 'best close match', outperform two computationally sophisticated Bayesian and BootstrapNJ methods. The new method proposed here has great power in reducing false-positive species identification compared with other methods when conspecifics of the query are absent from the reference database.
On Uniform Convergence of Sequences and Series of Fuzzy-Valued Functions
Directory of Open Access Journals (Sweden)
Uğur Kadak
2015-01-01
Full Text Available The class of membership functions is restricted to trapezoidal one, as it is general enough and widely used. In the present paper since the utilization of Zadeh’s extension principle is quite difficult in practice, we prefer the idea of level sets in order to construct for a fuzzy-valued function via related trapezoidal membership function. We derive uniform convergence of fuzzy-valued function sequences and series with some illustrated examples. Also we study Hukuhara differentiation and Henstock integration of a fuzzy-valued function with some necessary inclusions. Furthermore, we introduce the power series with fuzzy coefficients and define the radius of convergence of power series. Finally, by using the notions of H-differentiation and radius of convergence we examine the relationship between term by term H-differentiation and uniform convergence of fuzzy-valued function series.
Indian Academy of Sciences (India)
Arunima Dasgupta; K L N Sastry; P S Dhinwa; V S Rathore; M S Nathawat
2013-08-01
Desertification risk assessment is important in order to take proper measures for its prevention. Present research intends to identify the areas under risk of desertification along with their severity in terms of degradation in natural parameters. An integrated model with fuzzy membership analysis, fuzzy rulebased inference system and geospatial techniques was adopted, including five specific natural parameters namely slope, soil pH, soil depth, soil texture and NDVI. Individual parameters were classified according to their deviation from mean. Membership of each individual values to be in a certain class was derived using the normal probability density function of that class. Thus if a single class of a single parameter is with mean and standard deviation , the values falling beyond + 2 and − 2 are not representing that class, but a transitional zone between two subsequent classes. These are the most important areas in terms of degradation, as they have the lowest probability to be in a certain class, hence highest probability to be extended or narrowed down in next or previous class respectively. Eventually, these are the values which can be easily altered, under extrogenic influences, hence are identified as risk areas. The overall desertification risk is derived by incorporating the different risk severity of each parameter using fuzzy rule-based interference system in GIS environment. Multicriteria based geo-statistics are applied to locate the areas under different severity of desertification risk. The study revealed that in Kota, various anthropogenic pressures are accelerating land deterioration, coupled with natural erosive forces. Four major sources of desertification in Kota are, namely Gully and Ravine erosion, inappropriate mining practices, growing urbanization and random deforestation.
A novel classification method based on membership function
Peng, Yaxin; Shen, Chaomin; Wang, Lijia; Zhang, Guixu
2011-03-01
We propose a method for medical image classification using membership function. Our aim is to classify the image as several classes based on a prior knowledge. For every point, we calculate its membership function, i.e., the probability that the point belongs to each class. The point is finally labeled as the class with the highest value of membership function. The classification is reduced to a minimization problem of a functional with arguments of membership functions. Three novelties are in our paper. First, bias correction and Rudin-Osher-Fatemi (ROF) model are adopted to the input image to enhance the image quality. Second, unconstrained functional is used. We use variable substitution to avoid the constraints that membership functions should be positive and with sum one. Third, several techniques are used to fasten the computation. The experimental result of ventricle shows the validity of this approach.
Staff Association
2016-01-01
Join the Staff Association now for 2017, the remaining quarter of 2016 is free! The membership fee of the Staff Association is free for everyone joining during the last quarter of 2016. Take this opportunity to become a member of the SA. You can also enjoy our offers and partnerships, especially as we approach the holiday season. As a reminder, the membership fee is: 0.2 % of the annual basic salary for staff members with an indefinite contract (IC); the amount will be automatically charged on the salary of January; 50.00 CHF for staff members with a limited duration contract (LD), fellows and associated members of personnel. Don’t wait any longer, join the Staff Association. We represent and defend all of you! More information on http://staff-association.web.cern.ch/
Staff Association
2016-01-01
Join the Staff Association now for 2017, the remaining quarter of 2016 is free! The membership fee of the Staff Association is free for everyone joining during the last quarter of 2016. Take this opportunity to become a member of the SA. You can also enjoy our offers and partnerships, especially as we approach the holiday season. As a reminder, the membership fee is: 0.2 % of the annual basic salary for staff members with an indefinite contract (IC); the amount will be automatically; 50.00 CHF for staff members with a limited duration contract (LD), fellows and associated members of personnel. Don’t wait any longer, join the Staff Association that represents all of you! More information on http://staff-association.web.cern.ch/
Directory of Open Access Journals (Sweden)
Sakshi Gupta
2015-12-01
Full Text Available In this paper, application of fuzzy logic technique using triangular membership function for developing models for predicting compressive strength of concrete with partial replacement of cement with nanosilica has been carried out. For this, the data have been taken from various literatures and help in optimizing the constituents available and reducing cost and efforts in studying design to develop mixes by predefining suitable range for experimenting. The use of nanostructured materials in concrete can add many benefits that are directly related to the durability of various cementitious materials, besides the fact that it is possible to reduce the quantities of cement in the composite. Successful prediction by the model indicates that fuzzy logic could be a useful modelling tool for engineers and research scientists in the area of cement and concrete. Compressive strength values of concrete can be predicted in fuzzy logic models without attempting any experiments in a quite short period of time with tiny error rates.
Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure
Directory of Open Access Journals (Sweden)
Ashraf Ahmed Fahmy
2014-03-01
Full Text Available This paper presents an adaptive functional-based Neuro-fuzzy-PID incremental (NFPID controller structure that can be tuned either offline or online according to required controller performance. First, differential membership functions are used to represent the fuzzy membership functions of the input-output space of the three term controller. Second, controller rules are generated based on the discrete proportional, derivative, and integral function for the fuzzy space. Finally, a fully differentiable fuzzy neural network is constructed to represent the developed controller for either offline or online controller parameter adaptation. Two different adaptation methods are used for controller tuning, offline method based on controller transient performance cost function optimization using Bees Algorithm, and online method based on tracking error minimization using back-propagation with momentum algorithm. The proposed control system was tested to show the validity of the controller structure over a fixed PID controller gains to control SCARA type robot arm.
Function Approximation Using Probabilistic Fuzzy Systems
J.H. van den Berg (Jan); U. Kaymak (Uzay); R.J. Almeida e Santos Nogueira (Rui Jorge)
2011-01-01
textabstractWe consider function approximation by fuzzy systems. Fuzzy systems are typically used for approximating deterministic functions, in which the stochastic uncertainty is ignored. We propose probabilistic fuzzy systems in which the probabilistic nature of uncertainty is taken into account.
Axiomatic of Fuzzy Complex Numbers
Directory of Open Access Journals (Sweden)
Angel Garrido
2012-04-01
Full Text Available Fuzzy numbers are fuzzy subsets of the set of real numbers satisfying some additional conditions. Fuzzy numbers allow us to model very difficult uncertainties in a very easy way. Arithmetic operations on fuzzy numbers have also been developed, and are based mainly on the crucial Extension Principle. When operating with fuzzy numbers, the results of our calculations strongly depend on the shape of the membership functions of these numbers. Logically, less regular membership functions may lead to very complicated calculi. Moreover, fuzzy numbers with a simpler shape of membership functions often have more intuitive and more natural interpretations. But not only must we apply the concept and the use of fuzzy sets, and its particular case of fuzzy number, but also the new and interesting mathematical construct designed by Fuzzy Complex Numbers, which is much more than a correlate of Complex Numbers in Mathematical Analysis. The selected perspective attempts here that of advancing through axiomatic descriptions.
Structure Analysis and Function Evaluation of a Kind of Fuzzy PID Controllers
Institute of Scientific and Technical Information of China (English)
DUXinyu; ZHANGNaiyao; YUNa
2004-01-01
In this paper, a kind of fuzzy PID (Proportional integral and derivate) controllers is discussed, which has 3 input variables (error, difference of error, sum of error) and one output variable; triangular fully-overlapped symmetric membership function for input variables; singleton equally-spaced membership function for the output variable; linear control rules; Sum-Product inference method; and Center of area (COA) defuzzification algorithm. The paper consists of three main parts. In the first part, the structure properties of fuzzy PID controllers are studied. The explicit expression of this kind of fuzzy PID controllers is derived. It is proved that the analytical structure of fuzzy PID controllers is the sum of a global three-dimensional multi-level relay and a local nonlinear controller. When the number of fuzzy sets tends to infinity, the local nonlinear controller will disappear, and the degree of nonlinearity of the fuzzy PID controller becomes zero. In the second part, the function properties of fuzzy PID controllers are studied. It is revealed that the fuzzy PID controller is a variable gain nonlinear PID controller; so linear PID controllers can be regarded as a special example of fuzzy PID controllers. Moreover, they are equivalent to the sum of three fuzzy controllers with one-to-one mapping; so they do not suffer from some weaknesses such as composed-action, input coupling, etc. Based on these theoretical results, a systematic design approach of fuzzy PID control systems is proposed and demonstrated by 2 simulation examples in the third part of this paper. It is shown that the proposed fuzzy PID controller not only has good structure and function characteristics, but also can be very simply and quickly designed; therefore, it is very suitable for a wide range of applications.
The Multi-Criteria Negotiation Analysis Based on the Membership Function
Directory of Open Access Journals (Sweden)
Roszkowska Ewa
2014-08-01
Full Text Available In this paper we propose a multi-criteria model based on the fuzzy preferences approach which can be implemented in the prenegotiation phase to evaluate the negotiations packages. The applicability of some multi-criteria ranking methods were discussed for building a scoring function for negotiation packages. The first one is Simple Additive Weighting (SAW technique which determines the sum of the partial satisfactions from each negotiation issue and aggregate them using the issue weights. The other one is Distance Based Methods (DBM, with its extension based on the distances to ideal or anti-ideal package, i.e. the TOPSIS procedure. In our approach the negotiator's preferences over the issues are represented by fuzzy membership functions and next a selected multi-criteria decision making method is adopted to determine the global rating of each package. The membership functions are used here as the equivalents of utility functions spread over the negotiation issues, which let us compare different type of data. One of the key advantages of the approach proposed is its usefulness for building a general scoring function in the ill-structured negotiation problem, namely the situation in which the problem itself as well as the negotiators preferences cannot be precisely defined, the available information is uncertain, subjective and vague. Secondly, all proposed variants of scoring functions produce consistent rankings, even though the new packages are added (or removed and do not result in rank reversal.
An Approach to Formulation of FNLP with Complex Piecewise Linear Membership Functions
Institute of Scientific and Technical Information of China (English)
闻博; 李宏光
2014-01-01
Traditionally, extra binary variables are demanded to formulate a fuzzy nonlinear programming (FNLP) problem with piecewise linear membership functions (PLMFs). However, this kind of methodology usually suffers increasing computational burden associated with formulation and solution, particularly in the face of complex PLMFs. Motivated by these challenges, this contribution introduces a novel approach free of additional binary variables to formulate FNLP with complex PLMFs, leading to superior performance in reducing computational complexity as well as simplifying formulation. A depth discussion about the approach is conducted in this paper, along with a numerical case study to demonstrate its potential benefits.
Fuzzy controllers based on some fuzzy implication operators and their response functions
Institute of Scientific and Technical Information of China (English)
LI Hongxing; YOU Fei; PENG Jiayin
2004-01-01
The fuzzy controllers constructed by 23 fuzzy implication operators based on CRI algorithm and their response functions are discussed.The conclusions show that the fuzzy controllers constructed by 9 fuzzy implication operators are universal approximators to continuous functions and can be used in practical fuzzy control systems.And these 9 fuzzy implication operators except for Einstein operator intersection are all the adjoint pairs of some fuzzy implication operators.Besides, there are 3 other fuzzy controllers formed by fuzzy implication operators being regarded approximately as fitted functions.
Semi-Continuity of Complex Fuzzy Functions
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
This paper introduces the concept of semi-continuity of complex fuzzy functions, and discusses some of their elementary properties, such as the sum of two complex fuzzy functions of type I upper (lower)semi-continuity is type I upper (lower) semi-continuous, and the opposite of complex fuzzy functions of type I upper (lower) semi-continuity is type I lower (upper) semi-continuous. Based on some assumptions on two complex fuzzy functions of type I upper (lower) semi-continuity, it is shown that their product is type I upper (lower) semi-continuous. The paper also investigates the convergence of complex fuzzy functions. In particular, sign theorem, boundedness theorem, and Cauchy's criterion for convergence are kept. In this paper the metrics introduced by Zhang Guangquan was used. This paper gives a contribution to the study of complex fuzzy functions, and extends the corresponding work of Zhang Guangquan.
Fuzzy Functional Dependencies and Bayesian Networks
Institute of Scientific and Technical Information of China (English)
LIU WeiYi(刘惟一); SONG Ning(宋宁)
2003-01-01
Bayesian networks have become a popular technique for representing and reasoning with probabilistic information. The fuzzy functional dependency is an important kind of data dependencies in relational databases with fuzzy values. The purpose of this paper is to set up a connection between these data dependencies and Bayesian networks. The connection is done through a set of methods that enable people to obtain the most information of independent conditions from fuzzy functional dependencies.
Fuzzy Model for Trust Evaluation
Institute of Scientific and Technical Information of China (English)
Zhang Shibin; He Dake
2006-01-01
Based on fuzzy set theory, a fuzzy trust model is established by using membership function to describe the fuzziness of trust. The trust vectors of subjective trust are obtained based on a mathematical model of fuzzy synthetic evaluation. Considering the complicated and changeable relationships between various subjects, the multi-level mathematical model of fuzzy synthetic evaluation is introduced. An example of a two-level fuzzy synthetic evaluation model confirms the feasibility of the multi-level fuzzy synthesis evaluation model. The proposed fuzzy model for trust evaluation may provide a promising method for research of trust model in open networks.
A KIND OF FUZZY MULTI-OBJECTIVE LINEAR PROGRAMMING PROBLEMS BASED ON INTERVAL VALUED FUZZY SETS
Institute of Scientific and Technical Information of China (English)
XU Jiuping
2001-01-01
This paper presents a general solution procedure and an interactive fuzzy satisfying method for a kind of fuzzy multi-objective linear programming problems based on interval valued fuzzy sets. Firstly, a fuzzy set of the fuzzy solutions, which can be focused on providing complete information for the final decision, can be obtained by the proposed tolerance analysis of a non-dominated set. Secondly, the satisfying solution for the decisionmaker can be derived from Pareto optimal solutions by updating the current reference membership levels on the basis of the current levels of the membership functions together with the trade-off rates between the membership functions.
Liu, Chuang; Lam, Hak-Keung; Fernando, Tyrone; Iu, Herbert Ho-Ching
2016-05-02
In this paper, we investigate the stability of Takagi-Sugeno fuzzy-model-based (FMB) functional observer-control system. When system states are not measurable for state-feedback control, a fuzzy functional observer is designed to directly estimate the control input instead of the system states. Although the fuzzy functional observer can reduce the order of the observer, it leads to a number of observer gains to be determined. Therefore, a new form of fuzzy functional observer is proposed to facilitate the stability analysis such that the observer gains can be numerically obtained and the stability can be guaranteed simultaneously. The proposed form is also in favor of applying separation principle to separately design the fuzzy controller and the fuzzy functional observer. To design the fuzzy controller with the consideration of system stability, higher order derivatives of Lyapunov function (HODLF) are employed to reduce the conservativeness of stability conditions. The HODLF generalizes the commonly used first-order derivative. By exploiting the properties of membership functions and the dynamics of the FMB control system, convex and relaxed stability conditions can be derived. Simulation examples are provided to show the relaxation of the proposed stability conditions and the feasibility of designed fuzzy functional observer-controller.
Chaira, Tamalika
2014-06-01
In this paper automatic leukocyte segmentation in pathological blood cell images is proposed using intuitionistic fuzzy and interval Type II fuzzy set theory. This is done to count different types of leukocytes for disease detection. Also, the segmentation should be accurate so that the shape of the leukocytes is preserved. So, intuitionistic fuzzy set and interval Type II fuzzy set that consider either more number of uncertainties or a different type of uncertainty as compared to fuzzy set theory are used in this work. As the images are considered fuzzy due to imprecise gray levels, advanced fuzzy set theories may be expected to give better result. A modified Cauchy distribution is used to find the membership function. In intuitionistic fuzzy method, non-membership values are obtained using Yager's intuitionistic fuzzy generator. Optimal threshold is obtained by minimizing intuitionistic fuzzy divergence. In interval type II fuzzy set, a new membership function is generated that takes into account the two levels in Type II fuzzy set using probabilistic T co norm. Optimal threshold is selected by minimizing a proposed Type II fuzzy divergence. Though fuzzy techniques were applied earlier but these methods failed to threshold multiple leukocytes in images. Experimental results show that both interval Type II fuzzy and intuitionistic fuzzy methods perform better than the existing non-fuzzy/fuzzy methods but interval Type II fuzzy thresholding method performs little bit better than intuitionistic fuzzy method. Segmented leukocytes in the proposed interval Type II fuzzy method are observed to be distinct and clear.
FUZZY OPTIMIZATION USING EXTENDED KALMAN FILTER
Directory of Open Access Journals (Sweden)
M.DIVYA
2013-01-01
Full Text Available Fuzzy Logic is based on the idea that in fuzzy sets each element in the set can assume a value from 0 to 1, not only 0 or 1, as in crisp set theory. The degree of membership function is defined as the gradation in the extent to which an element is belonging to the relevant sets. Optimizing the membership functions of a fuzzy system can be viewed as a system identification problem for nonlinear dynamic system. In this paper two input and one output fuzzy controller is designed for the dynamic process of aircraft. The addition of an EKF in the feedback loop improved the system response by blocking possible effects of measurement error based on Predictor-Corrector algorithm. An Extended Kalman Filter approach to optimize the membership functions of the inputs and outputs of the fuzzy controller. The performance of the fuzzy system before and after the optimization are compared, as well as the membership functions.
Fuzzy weakly preopen (preclosed) function in Kubiak-Sostak fuzzy topological spaces
Energy Technology Data Exchange (ETDEWEB)
Zahran, A.M. [Department of Mathematics, Faculty of Science, Al azhar University, Assuit (Egypt)], E-mail: zahran15@hotmail.com; Abd-Allah, M. Azab. [Department of Mathematics, Faculty of Science, Assuit University, Assuit (Egypt)], E-mail: mazab57@yahoo.com; Abd El-Rahman, Abd El-Nasser G. [Department of Mathematics, Faculty of Science, South valley University, Qena 83523 (Egypt)], E-mail: ghareeb_nasser@yahoo.com
2009-02-15
In this paper, we introduce and characterize fuzzy weakly preopen and fuzzy weakly preclosed functions between L-fuzzy topological spaces in Kubiak-Sostak sense and also study these functions in relation to some other types of already known functions.
Consolidity analysis for fully fuzzy functions, matrices, probability and statistics
Directory of Open Access Journals (Sweden)
Walaa Ibrahim Gabr
2015-03-01
Full Text Available The paper presents a comprehensive review of the know-how for developing the systems consolidity theory for modeling, analysis, optimization and design in fully fuzzy environment. The solving of systems consolidity theory included its development for handling new functions of different dimensionalities, fuzzy analytic geometry, fuzzy vector analysis, functions of fuzzy complex variables, ordinary differentiation of fuzzy functions and partial fraction of fuzzy polynomials. On the other hand, the handling of fuzzy matrices covered determinants of fuzzy matrices, the eigenvalues of fuzzy matrices, and solving least-squares fuzzy linear equations. The approach demonstrated to be also applicable in a systematic way in handling new fuzzy probabilistic and statistical problems. This included extending the conventional probabilistic and statistical analysis for handling fuzzy random data. Application also covered the consolidity of fuzzy optimization problems. Various numerical examples solved have demonstrated that the new consolidity concept is highly effective in solving in a compact form the propagation of fuzziness in linear, nonlinear, multivariable and dynamic problems with different types of complexities. Finally, it is demonstrated that the implementation of the suggested fuzzy mathematics can be easily embedded within normal mathematics through building special fuzzy functions library inside the computational Matlab Toolbox or using other similar software languages.
Fuzzy Boundary and Fuzzy Semiboundary
Athar, M.; Ahmad, B.
2008-01-01
We present several properties of fuzzy boundary and fuzzy semiboundary which have been supported by examples. Properties of fuzzy semi-interior, fuzzy semiclosure, fuzzy boundary, and fuzzy semiboundary have been obtained in product-related spaces. We give necessary conditions for fuzzy continuous (resp., fuzzy semicontinuous, fuzzy irresolute) functions. Moreover, fuzzy continuous (resp., fuzzy semicontinuous, fuzzy irresolute) functions have been characterized via fuzzy-derived (resp., fuzz...
Age Estimation of Face Image Based on Fuzzy Membership Degrees%基于模糊隶属度的人脸图像年龄估计
Institute of Scientific and Technical Information of China (English)
张天刚; 康苏明; 张景安
2013-01-01
由于人脸面貌特征与年龄存在着较大的不确定性,提出了基于模糊隶属度的人脸图像年龄估计.用对光照、尺度变化具有很强鲁棒性的Gabor小波变换提取人脸特征,为了避免维数灾难,降低后续计算量,利用主成份分析方法对提取到的特征进行降维,细致推导了适用于人脸图像年龄估计的模糊函数,根据最大隶属度原则,来估计人脸的年龄.在FG-NET人脸库及自建的FAID人脸库中进行了实验,取得了94％的最高识别率.%Because of the greater uncertainty exists in both face features and age,a novel method based on fuzzy membership degrees for age estimation of face image is proposed.Face features are extracted by Gabor wavelet transform which are robust to the illumination change and scale variations.In order to avoid dimensions disaster and reduce the follow-up calculation,the dimensions of the extracted features are reduced by means of principal component analysis.The fuzzy function is appropriate for age estimation of face image was derived rigorous.The principle of maximum membership degree is used to age estimation,the experiments were conducted on the FG-NET face database and own FAID face database,the highest recognition rate of 94％ was achieved.
A neural fuzzy controller learning by fuzzy error propagation
Nauck, Detlef; Kruse, Rudolf
1992-01-01
In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.
Research on Bounded Rationality of Fuzzy Choice Functions
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Xinlin Wu
2014-01-01
Full Text Available The rationality of a fuzzy choice function is a hot research topic in the study of fuzzy choice functions. In this paper, two common fuzzy sets are studied and analyzed in the framework of the Banerjee choice function. The complete rationality and bounded rationality of fuzzy choice functions are defined based on the two fuzzy sets. An assumption is presented to study the fuzzy choice function, and especially the fuzzy choice function with bounded rationality is studied combined with some rationality conditions. Results show that the fuzzy choice function with bounded rationality also satisfies some important rationality conditions, but not vice versa. The research gives supplements to the investigation in the framework of the Banerjee choice function.
Shahi Ferdows, Mohammad; Ramazi, Hamidreza
2015-12-01
The selection of a suitable membership function and its parameters plays a critical role in the integration of layer information by the fuzzy method. In this paper, parameters of membership function for induced polarization (IP) and resistivity (RS) data (in the Hamyj copper deposit) have been determined by the threshold parameter of IP and resistivity data, already determined by expert opinion or drilling data. The Hamyj deposit is located about 80 km west of Birjand city, South Khorasan province, Iran. In this area, resistivity and induced polarization data have been surveyed by dipole-dipole array. In this paper, outlier-induced polarization data have been corrected by the Doerffel method and then IP and resistivity data have been inversed by the Newton and Gauss-Newton methods. The threshold of the IP data is recognized by statistical (gap statistic) and fractal (concentration-area) methods. The determined threshold by the fractal method is higher than the gap statistic. These two thresholds have been used to determine the S-shape function for the IP data. The thresholds of the RS data are recognized by the fractal method. These two thresholds have been used to determine the Z-shape function for the RS data. The integration of geoelectrical layer information has been carried out by the Gama method. Finally, the best drilling points were proposed based on fuzzy modelling for the area. The results show that the optimum exploration borehole is located at a depth of 25 m.
Functional Equations in Fuzzy Banach Spaces
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M. Eshaghi Gordji
2012-01-01
generalized Hyers-Ulam stability of the following additive-quadratic functional equation f(x+ky+f(x−ky=f(x+y+f(x−y+(2(k+1/kf(ky−2(k+1f(y for fixed integers k with k≠0,±1 in fuzzy Banach spaces.
Application of genetic algorithms to tuning fuzzy control systems
Espy, Todd; Vombrack, Endre; Aldridge, Jack
1993-01-01
Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.
An analysis of possible applications of fuzzy set theory to the actuarial credibility theory
Ostaszewski, Krzysztof; Karwowski, Waldemar
1992-01-01
In this work, we review the basic concepts of actuarial credibility theory from the point of view of introducing applications of the fuzzy set-theoretic method. We show how the concept of actuarial credibility can be modeled through the fuzzy set membership functions and how fuzzy set methods, especially fuzzy pattern recognition, can provide an alternative tool for estimating credibility.
The Fuzzy Sets Approach to Pattern Recognition
Wilson, T.
1972-01-01
The fuzzy set concept is defined and its application to pattern recognition is illustrated. An iterative procedure for learning the equi-membership surfaces and for generating a set of discriminate functions for two pattern classes is given.
A Fuzzy View on k-Means Based Signal Quantization with Application in Iris Segmentation
Popescu-Bodorin, Nicolaie
2011-01-01
This paper shows that the k-means quantization of a signal can be interpreted both as a crisp indicator function and as a fuzzy membership assignment describing fuzzy clusters and fuzzy boundaries. Combined crisp and fuzzy indicator functions are defined here as natural generalizations of the ordinary crisp and fuzzy indicator functions, respectively. An application to iris segmentation is presented together with a demo program.
Taste Identification of Tea Through a Fuzzy Neural Network Based on Fuzzy C-means Clustering
Institute of Scientific and Technical Information of China (English)
ZHENG Yan; ZHOU Chun-guang
2003-01-01
In this paper, we present a fuzzy neural network model based on Fuzzy C-Means (FCM) clustering algorithm to realize the taste identification of tea. The proposed method can acquire the fuzzy subset and its membership function in an automatic way with the aid of FCM clustering algorithm. Moreover, we improve the fuzzy weighted inference approach. The proposed model is illustrated with the simulation of taste identification of tea.
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Sridevi.Ravada,
2011-07-01
Full Text Available A fuzzy filter is constructed from a set of fuzzy IF-THEN rules, these fuzzy rules come either from human experts or by matching input-output pairs .in this paper we propose a new fuzzy filter for the noise reduction of images corrupted with additive noise. here in this approach ,initially fuzzy derivatives for all eight directions that is N,E,W,S, NE,NW,SE,SW are calculated using “fuzzy IF-THEN rules “ and membership functions . Further the fuzzy derivative values obtained are used in the fuzzy smoothing for determining the correction term. Finally correction term can be added to the processed pixel value. Iteratively apply the fuzzy filter to reduce the noise and at each and every iteration membership function iscalculated based on the remaining noise level. A statistical model for the noise distribution can be incorporated to relate the homogeneity to the adaptation scheme of the membership functions.
Describing fuzzy sets using a new concept:fuzzify functor
Institute of Scientific and Technical Information of China (English)
魏克新; 王兆霞; 王权
2009-01-01
This paper proposed a fuzzify functor as an extension of the concept of fuzzy sets.The fuzzify functor and the first-order operated fuzzy set are defined.From the theory analysis,it can be observed that when the fuzzify functor acts on a simple crisp set,we get the first order fuzzy set or type-1 fuzzy set.By operating the fuzzify functor on fuzzy sets,we get the higher order fuzzy sets or higher type fuzzy sets and their membership functions.Using the fuzzify functor we can exactly describe the type-1 fuzz...
Clinical effect of fuzzy numbers based on center of gravity
African Journals Online (AJOL)
Jane
2011-10-05
Oct 5, 2011 ... the extension principle, if M is a fuzzy number with membership function i ... On the other hand, from Equation (5) each constraint of the problem (. ) ... objective function using linear programming algorithms such as the simplex ...
On rarely generalized regular fuzzy continuous functions in fuzzy topological spaces
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Appachi Vadivel
2016-11-01
Full Text Available In this paper, we introduce the concept of rarely generalized regular fuzzy continuous functions in the sense of A.P. Sostak's and Ramadan is introduced. Some interesting properties and characterizations of them are investigated. Also, some applications to fuzzy compact spaces are established.
CALCULATION OF FUZZY RELIABILITYIN THE CASE OF RANDOM STRESSAND FUZZY FATIGUE STRENGTH
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
The fuzzy sets theory is introduced into the fatigue reliability analysis.The concepts of maximizing set and minimizing set are developed to decide the ordering value of each fuzzy number,and these values can be used to determine the order of the fuzzy numbers.On the basis of the works mentioned above,the membership function defining the fuzzy safety event can be calculated,and then the fuzzy reliability in the case of random stress and fuzzy fatigue strength is deduced.An example is given to illustrate the method.
Zhao, Tao; Dian, Songyi
2017-09-01
This paper addresses a fuzzy dynamic output feedback H∞ control design problem for continuous-time nonlinear systems via T-S fuzzy model. The stability of the fuzzy closed-loop system which is formed by a T-S fuzzy model and a fuzzy dynamic output feedback H∞ controller connected in a closed loop is investigated with Lyapunov stability theory. The proposed fuzzy controller does not share the same membership functions and number of rules with T-S fuzzy systems, which can enhance design flexibility. A line-integral fuzzy Lyapunov function is utilized to derive the stability conditions in the form of linear matrix inequalities (LMIs). The boundary information of membership functions is considered in the stability analysis to reduce the conservativeness of the imperfect premise matching design technique. Two simulation examples are provided to demonstrate the effectiveness of the proposed approach. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
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Dhruba Das
2015-04-01
Full Text Available In this article, based on Zadeh’s extension principle we have apply the parametric programming approach to construct the membership functions of the performance measures when the interarrival time and the service time are fuzzy numbers based on the Baruah’s Randomness- Fuzziness Consistency Principle. The Randomness-Fuzziness Consistency Principle leads to defining a normal law of fuzziness using two different laws of randomness. In this article, two fuzzy queues FM/M/1 and M/FM/1 has been studied and constructed their membership functions of the system characteristics based on the aforesaid principle. The former represents a queue with fuzzy exponential arrivals and exponential service rate while the latter represents a queue with exponential arrival rate and fuzzy exponential service rate.
FUZZY ARITHMETIC AND SOLVING OF THE STATIC GOVERNING EQUATIONS OF FUZZY FINITE ELEMENT METHOD
Institute of Scientific and Technical Information of China (English)
郭书祥; 吕震宙; 冯立富
2002-01-01
The key component of finite element analysis of structures with fuzzy parameters,which is associated with handling of some fuzzy information and arithmetic relation of fuzzy variables, was the solving of the governing equations of fuzzy finite element method. Based on a given interval representation of fuzzy numbers, some arithmetic rules of fuzzy numbers and fuzzy variables were developed in terms of the properties of interval arithmetic.According to the rules and by the theory of interval finite element method, procedures for solving the static governing equations of fuzzy finite element method of structures were presented. By the proposed procedure, the possibility distributions of responses of fuzzy structures can be generated in terms of the membership functions of the input fuzzy numbers.It is shown by a numerical example that the computational burden of the presented procedures is low and easy to implement. The effectiveness and usefulness of the presented procedures are also illustrated.
Fuzzy C e-I(ec, eo) and Fuzzy Completely C e-I(rc, eo) Functions via Fuzzy e-Open Sets
Kamala, K.
2016-01-01
We introduced the notions of fuzzy C e-I(ec, eo) functions and fuzzy completely C e-I(rc, eo) functions via fuzzy e-open sets. Some properties and several characterization of these types of functions are investigated. PMID:27051858
Fuzzy functions: a fuzzy extension of the category SET and some related categories
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Ulrich Höhle
2000-10-01
Full Text Available In research Works where fuzzy sets are used, mostly certain usual functions are taken as morphisms. On the other hand, the aim of this paper is to fuzzify the concept of a function itself. Namely, a certain class of L-relations F : X x Y -> L is distinguished which could be considered as fuzzy functions from an L-valued set (X,Ex to an L-valued set (Y,Ey. We study basic properties of these functions, consider some properties of the corresponding category of L-valued sets and fuzzy functions as well as briefly describe some categories related to algebra and topology with fuzzy functions in the role of morphisms.
Fuzzy-Contextual Contrast Enhancement.
Parihar, Anil; Verma, Om; Khanna, Chintan
2017-02-08
This paper presents contrast enhancement algorithms based on fuzzy contextual information of the images. We introduce fuzzy similarity index and fuzzy contrast factor to capture the neighborhood characteristics of a pixel. A new histogram, using fuzzy contrast factor of each pixel is developed, and termed as the fuzzy dissimilarity histogram (FDH). A cumulative distribution function (CDF) is formed with normalized values of FDH and used as a transfer function to obtain the contrast enhanced image. The algorithm gives good contrast enhancement and preserves the natural characteristic of the image. In order to develop a contextual intensity transfer function, we introduce a fuzzy membership function based on fuzzy similarity index and coefficient of variation of the image. The contextual intensity transfer function is designed using the fuzzy membership function to achieve final contrast enhanced image. The overall algorithm is referred as the fuzzy contextual contrast-enhancement (FCCE) algorithm. The proposed algorithms are compared with conventional and state-of-art contrast enhancement algorithms. The quantitative and visual assessment of the results is performed. The results of quantitative measures are statistically analyzed using t-test. The exhaustive experimentation and analysis show the proposed algorithm efficiently enhances contrast and yields in natural visual quality images.
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Xian-Xia Zhang
2013-01-01
Full Text Available This paper presents a reference function based 3D FLC design methodology using support vector regression (SVR learning. The concept of reference function is introduced to 3D FLC for the generation of 3D membership functions (MF, which enhance the capability of the 3D FLC to cope with more kinds of MFs. The nonlinear mathematical expression of the reference function based 3D FLC is derived, and spatial fuzzy basis functions are defined. Via relating spatial fuzzy basis functions of a 3D FLC to kernel functions of an SVR, an equivalence relationship between a 3D FLC and an SVR is established. Therefore, a 3D FLC can be constructed using the learned results of an SVR. Furthermore, the universal approximation capability of the proposed 3D fuzzy system is proven in terms of the finite covering theorem. Finally, the proposed method is applied to a catalytic packed-bed reactor and simulation results have verified its effectiveness.
Fuzzy Control Method with Application for Functional Neuromuscular Stimulation System
Institute of Scientific and Technical Information of China (English)
吴怀宇; 周兆英; 熊沈蜀
2001-01-01
A fuzzy control technique is applied to a functional neuromuscular stimulation (FNS) physicalmultiarticular muscle control system. The FNS multiarticular muscle control system based on the fuzzy controllerwas developed with the fuzzy control rule base. Simulation experiments were then conducted for the joint angletrajectories of both the elbow flexion and the wrist flexion using the proposed fuzzy control algorithm and aconventional PID control algorithm with the FNS physical multiarticular muscle control system. The simulationresults demonstrated that the proposed fuzzy control method is more suitable for the physiologicalcharacteristics than conventional PID control. In particular, both the trajectory-following and the stability of theFNS multiarticular muscle control system were greatly improved. Furthermore, the stimulating pulse trainsgenerated by the fuzzy controller were stable and smooth.``
Fuzzy Reliability Analysis of the Shaft of a Steam Turbine
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Field surveying shows that the failure of the steam turbine's coupling is due to fatigue that is caused by compound stress. Fuzzy mathematics was applied to get the membership function of the fatigue strength rule. A formula of fuzzy reliability of the coupling was derived and a theory of coupling's fuzzy reliability is set up. The calculating method of the fuzzy reliability is explained by an illustrative example.
Nonadditive Set Functions Defined by Aumann Fuzzy Integrals
Institute of Scientific and Technical Information of China (English)
刘彦奎; 刘宝碇
2003-01-01
A novel concept, called nonadditive set-valued measure, is first defined as a monotone and continuous set function. Then the interconnections between nonadditive set-valued measure and the additive set-valued measure as well as the fuzzy measure are discussed. Finally, an approach to construct a nonadditive compact set-valued measure is presented via Aumann fuzzy integral.
On retrial queueing model with fuzzy parameters
Ke, Jau-Chuan; Huang, Hsin-I.; Lin, Chuen-Horng
2007-01-01
This work constructs the membership functions of the system characteristics of a retrial queueing model with fuzzy customer arrival, retrial and service rates. The α-cut approach is used to transform a fuzzy retrial-queue into a family of conventional crisp retrial queues in this context. By means of the membership functions of the system characteristics, a set of parametric non-linear programs is developed to describe the family of crisp retrial queues. A numerical example is solved successfully to illustrate the validity of the proposed approach. Because the system characteristics are expressed and governed by the membership functions, more information is provided for use by management. By extending this model to the fuzzy environment, fuzzy retrial-queue is represented more accurately and analytic results are more useful for system designers and practitioners.
Development of fuzzy supplier-rating by trapeze fuzzy membership functions with trigonometrical legs
Tamás Portik; Tamás Varga
2011-01-01
In every fields of industry, suppliers create the elements for the final products of OEM (Original Equipment Manufacturer), which means, suppliers ”define” the quality of the final products via the quality of elements. Due to this, the continuous evaluation of supplier performance can be one of the most effcient risk assessment tools to identify weakness in early stages, make the possibility to implement corrective actions in time. The paper offers new development of supplier-rating based on ...
Indarsih, Indrati, Ch. Rini
2016-02-01
In this paper, we define variance of the fuzzy random variables through alpha level. We have a theorem that can be used to know that the variance of fuzzy random variables is a fuzzy number. We have a multi-objective linear programming (MOLP) with fuzzy random of objective function coefficients. We will solve the problem by variance approach. The approach transform the MOLP with fuzzy random of objective function coefficients into MOLP with fuzzy of objective function coefficients. By weighted methods, we have linear programming with fuzzy coefficients and we solve by simplex method for fuzzy linear programming.
Adaptive membership functions for handwritten character recognition by Voronoi-based image zoning.
Pirlo, Giuseppe; Impedovo, Donato
2012-09-01
In the field of handwritten character recognition, image zoning is a widespread technique for feature extraction since it is rightly considered to be able to cope with handwritten pattern variability. As a matter of fact, the problem of zoning design has attracted many researchers who have proposed several image-zoning topologies, according to static and dynamic strategies. Unfortunately, little attention has been paid so far to the role of feature-zone membership functions that define the way in which a feature influences different zones of the zoning method. The result is that the membership functions defined to date follow nonadaptive, global approaches that are unable to model local information on feature distributions. In this paper, a new class of zone-based membership functions with adaptive capabilities is introduced and its effectiveness is shown. The basic idea is to select, for each zone of the zoning method, the membership function best suited to exploit the characteristics of the feature distribution of that zone. In addition, a genetic algorithm is proposed to determine-in a unique process-the most favorable membership functions along with the optimal zoning topology, described by Voronoi tessellation. The experimental tests show the superiority of the new technique with respect to traditional zoning methods.
FUZZY IDENTIFICATION METHOD BASED ON A NEW OBJECTIVE FUNCTION
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A method of fuzzy identification based on a new objective function is proposed. The method could deal with the issue that input variables of a system have an effect on the input space while output variables of the system do not exert an influence on the input space in the proposed objective functions of fuzzy clustering. The method could simultaneously solve the problems about structure identification and parameter estimation; thus it makes the fuzzy model become optimal. Simulation example demonstrates that the method could identify non-linear systems and obviously improve modeling accuracy.
Operator functional state estimation based on EEG-data-driven fuzzy model.
Zhang, Jianhua; Yin, Zhong; Yang, Shaozeng; Wang, Rubin
2016-10-01
This paper proposed a max-min-entropy-based fuzzy partition method for fuzzy model based estimation of human operator functional state (OFS). The optimal number of fuzzy partitions for each I/O variable of fuzzy model is determined by using the entropy criterion. The fuzzy models were constructed by using Wang-Mendel method. The OFS estimation results showed the practical usefulness of the proposed fuzzy modeling approach.
Fuzzy rank functions in the set of all binary systems.
Kim, Hee Sik; Neggers, J; So, Keum Sook
2016-01-01
In this paper, we introduce fuzzy rank functions for groupoids, and we investigate their roles in the semigroup of binary systems by using the notions of right parallelisms and [Formula: see text]-shrinking groupoids.
Tuning of a TS Fuzzy Output Regulator Using the Steepest Descent Approach and ANFIS
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Ricardo Tapia-Herrera
2013-01-01
Full Text Available The exact output regulation problem for Takagi-Sugeno (TS fuzzy models, designed from linear local subsystems, may have a solution if input matrices are the same for every local linear subsystem. Unfortunately, such a condition is difficult to accomplish in general. Therefore, in this work, an adaptive network-based fuzzy inference system (ANFIS is integrated into the fuzzy controller in order to obtain the optimal fuzzy membership functions yielding adequate combination of the local regulators such that the output regulation error in steady-state is reduced, avoiding in this way the aforementioned condition. In comparison with the steepest descent method employed for tuning fuzzy controllers, ANFIS approximates the mappings between local regulators with membership functions which are not necessary known functions as Gaussian bell (gbell, sigmoidal, and triangular membership functions. Due to the structure of the fuzzy controller, Levenberg-Marquardt method is employed during the training of ANFIS.
Fuzzy Clustering Using the Convex Hull as Geometrical Model
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Luca Liparulo
2015-01-01
Full Text Available A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms.
Possibilistic Exponential Fuzzy Clustering
Institute of Scientific and Technical Information of China (English)
Kiatichai Treerattanapitak; Chuleerat Jaruskulchai
2013-01-01
Generally,abnormal points (noise and outliers) cause cluster analysis to produce low accuracy especially in fuzzy clustering.These data not only stay in clusters but also deviate the centroids from their true positions.Traditional fuzzy clustering like Fuzzy C-Means (FCM) always assigns data to all clusters which is not reasonable in some circumstances.By reformulating objective function in exponential equation,the algorithm aggressively selects data into the clusters.However noisy data and outliers cannot be properly handled by clustering process therefore they are forced to be included in a cluster because of a general probabilistic constraint that the sum of the membership degrees across all clusters is one.In order to improve this weakness,possibilistic approach relaxes this condition to improve membership assignment.Nevertheless,possibilistic clustering algorithms generally suffer from coincident clusters because their membership equations ignore the distance to other clusters.Although there are some possibilistic clustering approaches that do not generate coincident clusters,most of them require the right combination of multiple parameters for the algorithms to work.In this paper,we theoretically study Possibilistic Exponential Fuzzy Clustering (PXFCM) that integrates possibilistic approach with exponential fuzzy clustering.PXFCM has only one parameter and not only partitions the data but also filters noisy data or detects them as outliers.The comprehensive experiments show that PXFCM produces high accuracy in both clustering results and outlier detection without generating coincident problems.
Product design on the basis of fuzzy quality function deployment
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
In the implementation of quality function deployment (QFD), the determination of the target values of engineering characteristics is a complex decision process with multiple variables and multiple objectives that should trade off, and optimize all kinds of conflicts and constraints. A fuzzy linear programming model (FLP) is proposed. On the basis of the inherent fuzziness of QFD system, triangular fuzzy numbers are used to represent all the relationships and correlations, and then, the functional relationships between the customer needs and engineering characteristics and the functional correlations among the engineering characteristics are determined with the information in the house of quality (HoQ) fully used. The fuzzy linear programming (FLP) model aims to find the optimal target values of the engineering characteristics to maximize the customer satisfaction. Finally, the proposed method is illustrated by a numerical example.
Dominance-based ranking functions for interval-valued intuitionistic fuzzy sets.
Chen, Liang-Hsuan; Tu, Chien-Cheng
2014-08-01
The ranking of interval-valued intuitionistic fuzzy sets (IvIFSs) is difficult since they include the interval values of membership and nonmembership. This paper proposes ranking functions for IvIFSs based on the dominance concept. The proposed ranking functions consider the degree to which an IvIFS dominates and is not dominated by other IvIFSs. Based on the bivariate framework and the dominance concept, the functions incorporate not only the boundary values of membership and nonmembership, but also the relative relations among IvIFSs in comparisons. The dominance-based ranking functions include bipolar evaluations with a parameter that allows the decision-maker to reflect his actual attitude in allocating the various kinds of dominance. The relationship for two IvIFSs that satisfy the dual couple is defined based on four proposed ranking functions. Importantly, the proposed ranking functions can achieve a full ranking for all IvIFSs. Two examples are used to demonstrate the applicability and distinctiveness of the proposed ranking functions.
A FUZZY LOGIC CONTROLLERFORA TWO-LINK FUNCTIONAL MANIPULATOR
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Sherif Kamel Hussein
2014-12-01
Full Text Available This paper presents a new approach for designing a Fuzzy Logic Controller "FLC"for a dynamically multivariable nonlinear coupling system. The conventional controller with constant gains for different operating points may not be sufficient to guarantee satisfactory performance for Robot manipulator. The Fuzzy Logic Controller utilizes the error and the change of error as fuzzy linguistic inputs to regulate the system performance. The proposed controller have been developed to simulate the dynamic behavior of A Two-Link Functional Manipulator. The new controller uses only the available information of the inputoutput for controlling the position and velocity of the robot axes of the motion of the end effectors
Wang, Li Kui; Zhang, Hua Guang; Liu, Xiao Dong
2016-09-01
This paper deals with the problem of observer design for continuous-time Takagi-Sugeno fuzzy models with unmeasurable premise variables. First, in order to improve the existing results of observer design, a new method is proposed to bound the time derivatives of the membership function. Then, by applying the nonquadratic Lyapunov function and the matrix decoupling technique, the controller gains and observer gains are designed to guarantee that the error system is asymptotically stale. Furthermore, better H ∞ performance can be obtained by solving an optimization problem. All of the results are presented as linear matrices inequalities and three examples are provided to demonstrate the merits of the proposed approach.
双隶属度模糊粗糙支持向量机%Fuzzy rough support vector machine with dual membership
Institute of Scientific and Technical Information of China (English)
韩虎; 党建武
2015-01-01
It is difficult for support vector machine to deal with uncertain information because SVM is not only sensitive to noises and outliers but also the inconsistence between conditional features and decision labels is not taken into account. In order to overcome the problem, two types of membership are introduced into standard support vector machine, one type of membership is computed by the distance between the training samples and their center as fuzzy membership, the other type of membership is computed by the distance between the training samples and the nearest training sample with different class label as rough membership. At last several comparative experiments are made to show the performance and the validity of the proposed approach.%针对支持向量机方法处理不确定信息系统时存在的两个问题：一方面支持向量机训练对噪声样本敏感，另一方面支持向量机训练未考虑信息系统的不一致，利用模糊理论与粗糙集方法分别计算得到两种隶属度：模糊隶属度与粗糙隶属度，并将两种隶属度引入到标准支持向量机中得到一个新的支持向量机模型——双隶属度模糊粗糙支持向量机（DM-FRSVM）。分析该模型对于不确定问题的解决思路并进行对比研究，实验结果表明，在对于含有不确定信息的样本集进行分类时，DM-FRSVM表现出更好的推广性能。
Probabilistic Quadratic Programming Problems with Some Fuzzy Parameters
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S. K. Barik
2012-01-01
making problem by using some specified random variables and fuzzy numbers. In the present paper, randomness is characterized by Weibull random variables and fuzziness is characterized by triangular and trapezoidal fuzzy number. A defuzzification method has been introduced for finding the crisp values of the fuzzy numbers using the proportional probability density function associated with the membership functions of these fuzzy numbers. An equivalent deterministic crisp model has been established in order to solve the proposed model. Finally, a numerical example is presented to illustrate the solution procedure.
Anderson, H C; Lotfi, A; Westphal, L C; Jang, J R
1998-01-01
The above paper claims that under a set of minor restrictions radial basis function networks and fuzzy inference systems are functionally equivalent. The purpose of this letter is to show that this set of restrictions is incomplete and that, when it is completed, the said functional equivalence applies only to a small range of fuzzy inference systems. In addition, a modified set of restrictions is proposed which is applicable for a much wider range of fuzzy inference systems.
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Mansour Saraj
2012-06-01
Full Text Available In this paper we propose a fuzzy goal programming method for obtaining a satisfactory solution to a bi-level multi-objective absolute-value fractional programming (BLMO-A-FP problems. In the proposed approach, the membership functions for the de ned fuzzy goals of all objective functions at the two levels as well as the membership functions for vector of fuzzy goals of the decision variables controlled by upper level decision maker (ULDM are developed in the model formulation of the problem. Then fuzzy goal programming technique is used for achieving highest degree of each of the membership goals by minimizing negative and positive deviational variables. The method of variable change on the under- and over-deviational variables of the membership goals associated with the fuzzy goals of the model is introduced to solve the problem eciently by using linear goal programming methodology. Theoretical results is illustrated with the help of a numerical.
Profit Allocation Scheme among Partners in Virtual Enterprises Based on Fuzzy Shapley Values
Institute of Scientific and Technical Information of China (English)
CHEN Wen; ZHANG Qiang; WANG Ming-zhe
2007-01-01
Fuzzy Shapley values are developed based on classical Shapley values and used to allocate profit among partners in virtual enterprises (VE).Axioms of the classical Shapley value are extended to Shapley values with fuzzy payoffs by using fuzzy sets theory.Fuzzy Shapley function is defined based on these extended axioms.From the viewpoint the allocation for each partner should be a crisp value rather a fuzzy membership function at the end of cooperation,a crisp allocation scheme based on fuzzy Shapley values is proposed.
Energy Technology Data Exchange (ETDEWEB)
Castro, Antonio Orestes de Salvo [PETROBRAS, Rio de Janeiro, RJ (Brazil); Ferreira Filho, Virgilio Jose Martins [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)
2004-07-01
The hydraulic fracture operation is wide used to increase the oil wells production and to reduce formation damage. Reservoir studies and engineer analysis are made to select the wells for this kind of operation. As the reservoir parameters have some diffuses characteristics, Fuzzy Inference Systems (SIF) have been tested for this selection processes in the last few years. This paper compares the performance of a neuro fuzzy system and a genetic fuzzy system used for hydraulic Fracture well selection, with knowledge acquisition from an operational data base to set the SIF membership functions. The training data and the validation data used were the same for both systems. We concluded that, in despite of the genetic fuzzy system would be a younger process, it got better results than the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy system can work with constraints, the membership functions setting kept the consistency of variables linguistic values. (author)
Gayazova, Anna; Abdullaev, Sanjar
2014-05-01
Short-range forecasting of algal blooms in drinking water reservoirs and other waterbodies is an actual element of water treatment system. Particularly, Shershnevskoie reservoir - the source of drinking water for Chelyabinsk city (South Ural region of Russia) - is exposed to interannual, seasonal and short-range fluctuations of blue-green alga Aphanizomenon flos-aquae and other dominant species abundance, which lead to technological problems and economic costs and adversely affect the water treatment quality. Whereas the composition, intensity and the period of blooms affected not only by meteorological seasonal conditions but also by ecological specificity of waterbody, that's important to develop object-oriented forecasting, particularly, search for an optimal number of predictors for such forecasting. Thereby, firstly fuzzy logic and fuzzy artificial neural network patterns for blue-green alga Microcystis aeruginosa (M. aeruginosa) blooms prediction in nearby undrained Smolino lake were developed. These results subsequently served as the base to derive membership functions for Shernevskoie reservoir forecasting patterns. Time series with the total lenght about 138-159 days of dominant species seasonal abundance, water temperature, cloud cover, wind speed, mineralization, phosphate and nitrate concentrations were obtained through field observations held at Lake Smolino (Chelyabinsk) in the warm season of 2009 and 2011 with time resolution of 2-7 days. The cross-correlation analysis of the data revealed the potential predictors of M. aeruginosa abundance quasi-periodic oscillations: green alga Pediastrum duplex (P. duplex) abundance and mineralization for 2009, P. duplex abundance, water temperature and concentration of nitrates for 2011. According to the results of cross-correlation analysis one membership function "P. duplex abundance" and one rule linking M. aeruginosa and P. duplex abundances were set up for database of 2009. Analogically, for database of 2011
Application of Adaptive Fuzzy PID Leveling Controller
Directory of Open Access Journals (Sweden)
Ke Zhang
2013-05-01
Full Text Available Aiming at the levelling precision, speed and stability of suspended access platform, this paper put forward a new adaptive fuzzy PID control levelling algorithm by fuzzy theory. The method is aided design by using the SIMULINK toolbox of MATLAB, and setting the membership function and the fuzzy-PID control rule. The levelling algorithm can real-time adjust the three parameters of PID according to the fuzzy rules due to the current state. It is experimented, which is verified the algorithm have better stability and dynamic performance.
Fuzzy Multiresolution Neural Networks
Ying, Li; Qigang, Shang; Na, Lei
A fuzzy multi-resolution neural network (FMRANN) based on particle swarm algorithm is proposed to approximate arbitrary nonlinear function. The active function of the FMRANN consists of not only the wavelet functions, but also the scaling functions, whose translation parameters and dilation parameters are adjustable. A set of fuzzy rules are involved in the FMRANN. Each rule either corresponding to a subset consists of scaling functions, or corresponding to a sub-wavelet neural network consists of wavelets with same dilation parameters. Incorporating the time-frequency localization and multi-resolution properties of wavelets with the ability of self-learning of fuzzy neural network, the approximation ability of FMRANN can be remarkable improved. A particle swarm algorithm is adopted to learn the translation and dilation parameters of the wavelets and adjusting the shape of membership functions. Simulation examples are presented to validate the effectiveness of FMRANN.
Research and Design of a Fuzzy Neural Expert System
Institute of Scientific and Technical Information of China (English)
王仕军; 王树林
1995-01-01
We have developed a fuzzy neural expert system that has the precision and learning ability of a neural network.Knowledge is acquired from domain experts as fuzzy rules and membership functions.Then,they are converted into a neural network which implements fuzzy inference without rule matching.The neural network is applied to problem-solving and learns from the data obtained during operation to enhance the accuracy.The learning ability of the neural network makes it easy to modify the membership functions defined by domain experts.Also,by modifying the weights of neural networks adaptively,the problem of belief propagation in conventional expert systems can be solved easily.Converting the neural network back into fuzzy rules and membership functions helps explain the inner representation and operation of the neural network.
Outlier rejection fuzzy c-means (ORFCM) algorithm for image segmentation
segmentation, Outlier rejection fuzzy c-means (ORFCM)
2013-01-01
This paper presents a fuzzy clustering-based technique for image segmentation. Many attempts have been put into practice to increase the conventional fuzzy c-means (FCM) performance. In this paper, the sensitivity of the soft membership function of the FCM algorithm to the outlier is considered and the new exponent operator on the Euclidean distance is implemented in the membership function to improve the outlier rejection characteristics of the FCM. The comparative quantitative and qua...
The Application of Fuzzy Logic to Collocation Extraction
Bisht, Raj Kishor
2008-01-01
Collocations are important for many tasks of Natural language processing such as information retrieval, machine translation, computational lexicography etc. So far many statistical methods have been used for collocation extraction. Almost all the methods form a classical crisp set of collocation. We propose a fuzzy logic approach of collocation extraction to form a fuzzy set of collocations in which each word combination has a certain grade of membership for being collocation. Fuzzy logic provides an easy way to express natural language into fuzzy logic rules. Two existing methods; Mutual information and t-test have been utilized for the input of the fuzzy inference system. The resulting membership function could be easily seen and demonstrated. To show the utility of the fuzzy logic some word pairs have been examined as an example. The working data has been based on a corpus of about one million words contained in different novels constituting project Gutenberg available on www.gutenberg.org. The proposed me...
Fuzzy Mathematics for Raw Silk Size Control
Institute of Scientific and Technical Information of China (English)
HU Zheng-yu; YU Hai-feng; GU Ping
2008-01-01
With photographing and experiments,this paper divides the cocoon layers into three categories according to their colors,establishes three-color membership function based on fuzzy mathemtics,constructs fuzzy sets which satisfy the range of size contrd by using the ordinary set and attached fiequency of three color cocoons combination,then achieves the ordinary sets of range of size control by choosing λ-cut.Under these ordinary sets,each end does duality relative level,then sets up relative matrix and overall sequence and finds the membership function to iudge whether the size cmtrol is normal.
Fuzzy set approach to quality function deployment: An investigation
Masud, Abu S. M.
1992-01-01
The final report of the 1992 NASA/ASEE Summer Faculty Fellowship at the Space Exploration Initiative Office (SEIO) in Langley Research Center is presented. Quality Function Deployment (QFD) is a process, focused on facilitating the integration of the customer's voice in the design and development of a product or service. Various input, in the form of judgements and evaluations, are required during the QFD analyses. All the input variables in these analyses are treated as numeric variables. The purpose of the research was to investigate how QFD analyses can be performed when some or all of the input variables are treated as linguistic variables with values expressed as fuzzy numbers. The reason for this consideration is that human judgement, perception, and cognition are often ambiguous and are better represented as fuzzy numbers. Two approaches for using fuzzy sets in QFD have been proposed. In both cases, all the input variables are considered as linguistic variables with values indicated as linguistic expressions. These expressions are then converted to fuzzy numbers. The difference between the two approaches is due to how the QFD computations are performed with these fuzzy numbers. In Approach 1, the fuzzy numbers are first converted to their equivalent crisp scores and then the QFD computations are performed using these crisp scores. As a result, the output of this approach are crisp numbers, similar to those in traditional QFD. In Approach 2, all the QFD computations are performed with the fuzzy numbers and the output are fuzzy numbers also. Both the approaches have been explained with the help of illustrative examples of QFD application. Approach 2 has also been applied in a QFD application exercise in SEIO, involving a 'mini moon rover' design. The mini moon rover is a proposed tele-operated vehicle that will traverse and perform various tasks, including autonomous operations, on the moon surface. The output of the moon rover application exercise is a
Weakly Semi-Preopen and Semi-Preclosed Functions in L-fuzzy Topological Spaces
Ghareeb, A
2010-01-01
A new class of functions called L-fuzzy weakly Semi-Preopen (Semi-Preclosed) functions in L-fuzzy topological spaces are introduced in this paper. Some characterizations of this class and its properties and the relationship with other classes of functions between L-fuzzy topological spaces are also obtained.
Advances in type-2 fuzzy sets and systems theory and applications
Mendel, Jerry; Tahayori, Hooman
2013-01-01
This book explores recent developments in the theoretical foundations and novel applications of general and interval type-2 fuzzy sets and systems, including: algebraic properties of type-2 fuzzy sets, geometric-based definition of type-2 fuzzy set operators, generalizations of the continuous KM algorithm, adaptiveness and novelty of interval type-2 fuzzy logic controllers, relations between conceptual spaces and type-2 fuzzy sets, type-2 fuzzy logic systems versus perceptual computers; modeling human perception of real world concepts with type-2 fuzzy sets, different methods for generating membership functions of interval and general type-2 fuzzy sets, and applications of interval type-2 fuzzy sets to control, machine tooling, image processing and diet. The applications demonstrate the appropriateness of using type-2 fuzzy sets and systems in real world problems that are characterized by different degrees of uncertainty.
Applying fuzzy analytic network process in quality function deployment model
Directory of Open Access Journals (Sweden)
Mohammad Ali Afsharkazemi
2012-08-01
Full Text Available In this paper, we propose an empirical study of QFD implementation when fuzzy numbers are used to handle the uncertainty associated with different components of the proposed model. We implement fuzzy analytical network to find the relative importance of various criteria and using fuzzy numbers we calculate the relative importance of these factors. The proposed model of this paper uses fuzzy matrix and house of quality to study the products development in QFD and also the second phase i.e. part deployment. In most researches, the primary objective is only on CRs to implement the quality function deployment and some other criteria such as production costs, manufacturing costs etc were disregarded. The results of using fuzzy analysis network process based on the QFD model in Daroupat packaging company to develop PVDC show that the most important indexes are being waterproof, resistant pill packages, and production cost. In addition, the PVDC coating is the most important index in terms of company experts’ point of view.
Institute of Scientific and Technical Information of China (English)
FAN Xingzhe; ZHANG Naiyao; LINing
2001-01-01
In this paper, a kind of typical fuzzycontrollers is defined, which have two inputs (e and△c) and one output (△u); triangular, symmetric andfull-overlapped membership functions for input vari-ables; singleton and symmetric membership func-tions for output variable; linear fuzzy control rules;Sum-Product inference method, and weighted meanmethod for defuzzification. For this kind of typicalfuzzy controllers we have analyzed their analyticalstructure, limiting structure and local stability.
Fuzzy Comprehensive Evaluation for Decision Making of Water Saving Irrigation System
Institute of Scientific and Technical Information of China (English)
LuoJin-yao; QiuYuan-feng
2003-01-01
A model of fuzzy comprehensive evaluation for water saving irrigation system (WSIS) decision making is proposed based on establishing an index system affected by six kinds of basic factors including qualitative and quantitative indexes. The object function of WSIS is set up by using the concept of fuzzy membership degree, it is to transform characteristic vector matrix into unify membership matrix and extending the least square method to the least of weighted distance square. The optimum weighted membership degree and the inferior weighted membership degree are used to solve the object function. This method effective solves the problem of classify for fuzzy attributive indexes and the problem of optimum for the set of different attributive indexes. A case study shows that the fuzzy comprehensive evaluation model is reasonable and effective in decision making for water saving irrigation system planning.
Fuzzy Comprehensive Evaluation for Decision Making of Water Saving Irrigation System
Institute of Scientific and Technical Information of China (English)
Luo Jin-yao; Qiu Yuan-feng
2003-01-01
A model of fuzzy comprehensive evaluation for water saving irrigation system (WSIS) decision making is proposed based on establishing an index system affected by six kinds of basic factors including qualitative and quantitative indexes. The object function of WSIS is set up by using the concept of fuzzy membership degree, it is to transform characteristic vector matrix into unify membership matrix and extending the least square method to the least of weighted dis tance square. The optimum weighted membership degree and the inferior weighted membership degree are used to solve the object function. This method effective solves the problem of classify for fuzzy attributive indexes and the problem of optimum for the set of different attributive indexes. A case study shows that the fuzzy comprehensive evaluation model is reasonable and effective in decision making for water saving irrigation system planning.
An Adaptive Fuzzy Control Approach for the Robust Tracking of a MEMS Gyroscope Sensor
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Juntao Fei
2011-11-01
Full Text Available In this paper, a direct adaptive fuzzy control using a supervisory compensator is designed for the robust tracking of a MEMS gyroscope sensor. The parameters of the membership functions are adjusted according to the designed adaptive law for the purpose of tracking a reference trajectory. A fuzzy controller that can approximate the unknown nonlinear function and compensate the system
Moldability Evaluation for Molded Parts Based on Fuzzy Reasoning
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Moldability evaluation for molded parts, which is the basis of concurrent design, is a key design stage in injection molding design. By moldability evaluation the design problems can be found timely and an optimum plastic part design achieved. In this paper, a systematic methodology for moldability evaluation based on fuzzy logic is proposed. Firstly, fuzzy set modeling for six key design attributes of molded parts is carried out respectively. Secondly, on the basis of this, the relationship between fuzzy sets for design attributes and fuzzy sets for moldability is established by fuzzy rules that are based on domain experts' experience and knowledge. At last the integral moldability for molded parts is obtained through fuzzy reasoning. The neural network based fuzzy reasoning approach presented in this paper can improve fuzzy reasoning efficiency greatly, especially for system having a large number of rules and complicated membership functions. An example for moldability evaluation is given to show the feasibility of this proposed methodology.
Fuzzy ta/2 symmetries of straight chain conjugate polyene molecules
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
On the basis of our recent studies on the molecular fuzzy point group symmetry,we further probe into the more complicated planar one-dimensional fuzzy periodic molecules-straight chain conjugate polyene.Except for the fuzzy translation transformation,the space transformation of the fuzzy screw rotation and the glide plane will be referred to.In addition,other fuzzy point symmetry transformation lain in the space transformation is discussed.Usually there is a correlation between the fuzzy symmetry characterization caused by the transition of the point symmetry elements and by certain space symmetry transformation.For the molecular orbital,the irreducible representation component is analyzed besides the membership function of the fuzzy symmetry transformation.Also,we inquire into the relativity between some molecular property and the fuzzy symmetry characterization.
Fuzzy stability of a mixed type functional equation
Directory of Open Access Journals (Sweden)
Jin Sun
2011-01-01
Full Text Available Abstract In this paper, we investigate a fuzzy version of stability for the functional equation f ( x + y + z - f ( x + y - f ( y + z - f ( x + z + f ( x + f ( y + f ( z = 0 in the sense of Mirmostafaee and Moslehian. 1991 Mathematics Subject Classification. Primary 46S40; Secondary 39B52.
Weakly semi-preopen “semi-preclosed” functions in L-double fuzzy topological spaces
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A. Ghareeb
2016-07-01
Full Text Available In this paper, we introduce a new class of functions called L-double fuzzy weakly semi-preopen (semi-preclosed functions in L-double fuzzy topological spaces. Some characterizations of this class and its properties and the relationship with other classes in L-double fuzzy topological spaces are also discussed.
Universal triple I fuzzy reasoning algorithm of function model based on quotient space
Institute of Scientific and Technical Information of China (English)
Lu Qiang; Shen Guanting; and Liu Xiaoping
2012-01-01
Aiming at the deficiencies of analysis capacity from different levels and fuzzy treating method in product function modeling of conceptual design, the theory of quotient space and universal triple I fuzzy reasoning method are introduced, and then the function modeling algorithm based on the universal triple I fuzzy reasoning method is proposed. Firstly, the product function granular model based on the quotient space theory is built, with its function granular representation and computing rules defined at the same time. Secondly, in order to quickly achieve function granular model from function requirement, the function modeling method based on universal triple I fuzzy reasoning is put forward. Within the fuzzy reasoning of universal triple I method, the small-distance-activating method is proposed as the kernel of fuzzy reasoning; how to change function requirements to fuzzy ones, fuzzy computing methods, and strategy of fuzzy reasoning are respectively investigated as well; the function modeling algorithm based on the universal triple I fuzzy reasoning method is achieved. Lastly, the validity of the function granular model and function modeling algorithm is validated. Through our method, the reasonable function granular model can be quickly achieved from function requirements, and the fuzzy character of conceptual design can be well handled, which greatly improves conceptual design.
Automated leukocyte recognition using fuzzy divergence.
Ghosh, Madhumala; Das, Devkumar; Chakraborty, Chandan; Ray, Ajoy K
2010-10-01
This paper aims at introducing an automated approach to leukocyte recognition using fuzzy divergence and modified thresholding techniques. The recognition is done through the segmentation of nuclei where Gamma, Gaussian and Cauchy type of fuzzy membership functions are studied for the image pixels. It is in fact found that Cauchy leads better segmentation as compared to others. In addition, image thresholding is modified for better recognition. Results are studied and discussed.
Fuzzy neural networks for arc welding quality control
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Fuzzy Logic Control (FLC) is a promising control strategy in welding process control due to its ability for solving control problem with uncertainty as well as its independence on the analytical mathematics model. However, in basic FLC, the fuzzy rule relies heavily on the experts' (e.g. advanced welders') experience. In addition to this, the membership function for fuzzy set is non-adaptive, i.e. it remains unchanged as long as they are determined by experience or other means. For welding process, which is time-variable systems and strong disturbance exists in it, fixed membership function may not guarantee the required system performance, and attempts should be made to improve the system performance by adopting adaptive membership function. Therefore, the automatically determination of the fuzzy rule and in-process adaptation of membership function are required for the advanced welding process control. This paper discussed the possibility by using the combination between FLC and neural network (NN) to realize the above propose. The adaptation of membership function as well as the self-organizing of fuzzy rule are realized by the self-learning and competitiveness of the NN. Taking GTAW process welds bead width regulating system as the controlled plant, the proposed algorithm was testified for such a process. Computer simulations showed the improvement of the system characteristics.
Terrorism Event Classification Using Fuzzy Inference Systems
Inyaem, Uraiwan; Meesad, Phayung; Tran, Dat
2010-01-01
Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decision-making model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluat...
Incomplete fuzzy data processing systems using artificial neural network
Patyra, Marek J.
1992-01-01
In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.
Weakly continuous functions on mixed fuzzy topological spaces
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Binod Chandra Tripathy
2014-04-01
Full Text Available The notions of continuity was generalized in the fuzzy setting by Chang (1968. Later on Azad (1981 introduced some weaker form of fuzzy continuity like fuzzy almost continuity, fuzzy semi-continuity and fuzzy weak continuity. These are natural generalization of the corresponding weaker forms of continuity in topological spaces. Recently Arya and Singal (2001a and b introduce another weaker form of fuzzy continuity, namely fuzzy subweakly continuity as a natural generalization of subweak continuity introduced by Rose (1984. In this paper we introduce fuzzy weak continuity in mixed fuzzy topological space.
Intuitionistic fuzzy segmentation of medical images.
Chaira, Tamalika
2010-06-01
This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy set theory to segment blood vessels and also the blood cells in pathological images. This type of segmentation is very important in detecting different types of human diseases, e.g., an increase in the number of vessels may lead to cancer in prostates, mammary, etc. The medical images are not properly illuminated, and segmentation in that case becomes very difficult. A novel image segmentation approach using intuitionistic fuzzy set theory and a new membership function is proposed using restricted equivalence function from automorphisms, for finding the membership values of the pixels of the image. An intuitionistic fuzzy image is constructed using Sugeno type intuitionistic fuzzy generator. Local thresholding is applied to threshold medical images. The results showed a much better performance on poor contrast medical images, where almost all the blood vessels and blood cells are visible properly. There are several fuzzy and intuitionistic fuzzy thresholding methods, but these methods are not related to the medical images. To make a comparison with the proposed method with other thresholding methods, the method is compared with six nonfuzzy, fuzzy, and intuitionistic fuzzy methods.
Modeling of Kefir Production with Fuzzy Logic
Directory of Open Access Journals (Sweden)
Hüseyin Nail Akgül
2014-06-01
Full Text Available The fermentation is ended with pH 4.6 values in industrial production of kefir. In this study, the incubation temperature, the incubation time and inoculums of culture were chose as variable parameters of kefir. In conventional control systems, the value of pH can be found by trial method. In these systems, if the number of input parameters is greater, the method of trial and error creates a system dependent on the person as well as troublesome. Fuzzy logic can be used in such cases. Modeling studies with this fuzzy logic control are examined in two portions. The first part consists of fuzzy rules and membership functions, while the second part consists of clarify. Kefir incubation temperature between 20 and 25°C, the incubation period between 18 to 22 hours and the inoculum ratio of culture between 1-5% are selected for optimum production conditions. Three separate fuzzy sets (triangular membership function are used to blur the incubation temperature, the incubation time and the inoculum ratio of culture. Because the membership function numbers belonging to the the input parameters are 3 units, 3x3x3=27 line rule is obtained by multiplying these numbers. The table of fuzzy rules was obtained using the method of Mamdani. The membership function values were determined by the method of average weight using three trapezoidal area of membership functions created for clarification. The success of the system will be found, comparing the numerical values obtained with pH values that should be. Eventually, to achieve the desired pH value of 4.6 in the production of kefir, with the using of fuzzy logic, the workload of people will be decreased and the productivity of business can be increased. In this case, it can be provided savings in both cost and time.
Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning
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Ya’nan Wang
2016-01-01
Full Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.
Hypotheses testing for fuzzy robust regression parameters
Energy Technology Data Exchange (ETDEWEB)
Kula, Kamile Sanli [Ahi Evran University, Department of Mathematics, 40200 Kirsehir (Turkey)], E-mail: sanli2004@hotmail.com; Apaydin, Aysen [Ankara University, Department of Statistics, 06100 Ankara (Turkey)], E-mail: apaydin@science.ankara.edu.tr
2009-11-30
The classical least squares (LS) method is widely used in regression analysis because computing its estimate is easy and traditional. However, LS estimators are very sensitive to outliers and to other deviations from basic assumptions of normal theory [Huynh H. A comparison of four approaches to robust regression. Psychol Bull 1982;92:505-12; Stephenson D. 2000. Available from: (http://folk.uib.no/ngbnk/kurs/notes/node38.html); Xu R, Li C. Multidimensional least-squares fitting with a fuzzy model. Fuzzy Sets and Systems 2001;119:215-23.]. If there exists outliers in the data set, robust methods are preferred to estimate parameters values. We proposed a fuzzy robust regression method by using fuzzy numbers when x is crisp and Y is a triangular fuzzy number and in case of outliers in the data set, a weight matrix was defined by the membership function of the residuals. In the fuzzy robust regression, fuzzy sets and fuzzy regression analysis was used in ranking of residuals and in estimation of regression parameters, respectively [Sanli K, Apaydin A. Fuzzy robust regression analysis based on the ranking of fuzzy sets. Inernat. J. Uncertainty Fuzziness and Knowledge-Based Syst 2008;16:663-81.]. In this study, standard deviation estimations are obtained for the parameters by the defined weight matrix. Moreover, we propose another point of view in hypotheses testing for parameters.
Performance of Geno-Fuzzy Model on rainfall-runoff predictions in claypan watersheds
Fuzzy logic provides a relatively simple approach to simulate complex hydrological systems while accounting for the uncertainty of environmental variables. The objective of this study was to develop a fuzzy inference system (FIS) with genetic algorithm (GA) optimization for membership functions (MF...
Control of a dc motor using fuzzy logic control algorithm | Usoro ...
African Journals Online (AJOL)
This study sought to establish the impact of a fuzzy logic controller (FLC) and a ... A choice of seven membership functions was designed for the error and change in ... Based on the findings, it was observed that the fuzzy speed controlled DC ...
HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.
Kim, J; Kasabov, N
1999-11-01
This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.
Development of single-chip fuzzy controller based on FFSI in binary
Institute of Scientific and Technical Information of China (English)
张吉礼; 欧进萍; 孙德兴
2003-01-01
Length and concise structure of fuzzy logic reasoning program and its real-time reasoning characteris-tic have their effect on the performance of a digital single-chip fuzzy controller. The control effect of a digitalfuzzy controller based on looking up fuzzy control responding table is only relative to the table and not relative tothe fuzzy control rules in the practical control process. Aiming at above problem and having combined fuzzy log-ic reasoning with digital operational characteristics of a single-chip microcomputer, functioning-fuzzy-subset in-ference (FFSI) in binary, in which triangle membership functions of error and error-in-change are all represen-ted in binary and singleton membership functions of control variable is binary too, has been introduced. The cir-cuit principle plans of a single-chip fuzzy controller have been introduced for development of its hardware, andthe primary program structure, fuzzy logic reasoning subroutine, serial communication subroutine with PC andreliability design of the fuzzy controller are all discussed in detail. The control of indoor temperature by a fuzzycontroller has been conducted using a testing-room thermodynamic system. Research results show that the FFSIin binary can exercise a concise fuzzy control in a single-chip fuzzy controller, and the fuzzy controller is there-fore reliable and possesses a high performance-price ratio.
Extending the functional equivalence of radial basis function networks and fuzzy inference systems.
Hunt, K J; Haas, R; Murray-Smith, R
1996-01-01
We establish the functional equivalence of a generalized class of Gaussian radial basis function (RBFs) networks and the full Takagi-Sugeno model (1983) of fuzzy inference. This generalizes an existing result which applies to the standard Gaussian RBF network and a restricted form of the Takagi-Sugeno fuzzy system. The more general framework allows the removal of some of the restrictive conditions of the previous result.
FUZZY-DISTANCE FUNCTION APPROACH FOR MULTIPLE CRITERIA DECISION MAKING
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Mayank Kumar
2012-06-01
Full Text Available In this paper, a method for decision making using fuzzy integral and distance function is presented. Case studies of multiple-response process with correlated responses are used to illustrate the effective application of the proposed approach. The efficacy of this method is compared with the existing methods of MCDM like TOPSIS and GRA. The proposed method is robust, requires less information and less complex as compared to many existing methods.
Fuzzy Control of Chaotic System with Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
FANG Jian-an; GUO Zhao-xia; SHAO Shi-huang
2002-01-01
A novel approach to control the unpredictable behavior of chaotic systems is presented. The control algorithm is based on fuzzy logic control technique combined with genetic algorithm. The use of fuzzy logic allows for the implementation of human "rule-of-thumb" approach to decision making by employing linguistic variables. An improved Genetic Algorithm (GA) is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule,and to automatically generate fuzzy control actions under each condition. Simulation results show that such an approach for the control of chaotic systems is both effective and robust.
Triangular and Trapezoidal Fuzzy State Estimation with Uncertainty on Measurements
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Mohammad Sadeghi Sarcheshmah
2012-01-01
Full Text Available In this paper, a new method for uncertainty analysis in fuzzy state estimation is proposed. The uncertainty is expressed in measurements. Uncertainties in measurements are modelled with different fuzzy membership functions (triangular and trapezoidal. To find the fuzzy distribution of any state variable, the problem is formulated as a constrained linear programming (LP optimization. The viability of the proposed method would be verified with the ones obtained from the weighted least squares (WLS and the fuzzy state estimation (FSE in the 6-bus system and in the IEEE-14 and 30 bus system.
Image Edge Extraction via Fuzzy Reasoning
Dominquez, Jesus A. (Inventor); Klinko, Steve (Inventor)
2008-01-01
A computer-based technique for detecting edges in gray level digital images employs fuzzy reasoning to analyze whether each pixel in an image is likely on an edge. The image is analyzed on a pixel-by-pixel basis by analyzing gradient levels of pixels in a square window surrounding the pixel being analyzed. An edge path passing through the pixel having the greatest intensity gradient is used as input to a fuzzy membership function, which employs fuzzy singletons and inference rules to assigns a new gray level value to the pixel that is related to the pixel's edginess degree.
Cooperative fuzzy games approach to setting target levels of ECs in quality function deployment.
Yang, Zhihui; Chen, Yizeng; Yin, Yunqiang
2014-01-01
Quality function deployment (QFD) can provide a means of translating customer requirements (CRs) into engineering characteristics (ECs) for each stage of product development and production. The main objective of QFD-based product planning is to determine the target levels of ECs for a new product or service. QFD is a breakthrough tool which can effectively reduce the gap between CRs and a new product/service. Even though there are conflicts among some ECs, the objective of developing new product is to maximize the overall customer satisfaction. Therefore, there may be room for cooperation among ECs. A cooperative game framework combined with fuzzy set theory is developed to determine the target levels of the ECs in QFD. The key to develop the model is the formulation of the bargaining function. In the proposed methodology, the players are viewed as the membership functions of ECs to formulate the bargaining function. The solution for the proposed model is Pareto-optimal. An illustrated example is cited to demonstrate the application and performance of the proposed approach.
Cooperative Fuzzy Games Approach to Setting Target Levels of ECs in Quality Function Deployment
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Zhihui Yang
2014-01-01
Full Text Available Quality function deployment (QFD can provide a means of translating customer requirements (CRs into engineering characteristics (ECs for each stage of product development and production. The main objective of QFD-based product planning is to determine the target levels of ECs for a new product or service. QFD is a breakthrough tool which can effectively reduce the gap between CRs and a new product/service. Even though there are conflicts among some ECs, the objective of developing new product is to maximize the overall customer satisfaction. Therefore, there may be room for cooperation among ECs. A cooperative game framework combined with fuzzy set theory is developed to determine the target levels of the ECs in QFD. The key to develop the model is the formulation of the bargaining function. In the proposed methodology, the players are viewed as the membership functions of ECs to formulate the bargaining function. The solution for the proposed model is Pareto-optimal. An illustrated example is cited to demonstrate the application and performance of the proposed approach.
Construction of Functions by Fuzzy Operators
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József Dombi
2007-12-01
Full Text Available In this paper we present a new approach to compose and decompose functions.This technology is based on pliant concept. We use the proper transformations ofConjunction of Sigmoid function to create an effect. We aggregate the effects to composethe function. This tool is also capable for function decomposition.
Rank-ordered filter for edge enhancement of cellular images using interval type II fuzzy set.
Chaira, Tamalika
2015-10-01
An edge-enhancement technique using an interval type II fuzzy set that uses rank-ordered filter to enhance the edges of cellular images is proposed. When cellular images from any laboratory are digitized, scanned, and stored, some kind of degradation occurs, and directly using a rank-ordered filter may not produce clear edges. These images contain uncertainties, present in edges or boundaries of the image. Fuzzy sets that take into account these uncertainties may be a good tool to process these images. However, a fuzzy set sometimes does not produce better results. We used an interval type II fuzzy set, which considers the uncertainty in a different way. It considers the membership function in the fuzzy set as "fuzzy," so the membership values lie within an interval range. A type II fuzzy set has upper and lower membership levels, and with the two levels, a new membership function is computed using Hamacher t-conorm. A new fuzzy image is formed. A rank-ordered filter is applied to the image to obtain an edge-enhanced image. The proposed method is compared with the existing methods visually and quantitatively using entropic method. Entropy of the proposed method is higher (0.4418) than the morphology method (0.2275), crisp method (0.3599), and Sobel method (0.2669), implying that the proposed method is better.
A Neuro-Fuzzy Approach in the Prediction of Financial Stability and Distress Periods
Giovanis, eleftheios
2008-01-01
The purpose of this paper is to present a neuro-fuzzy approach of financial distress pre-warning model appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) from 2002 through 2008. We present an adaptive neuro-fuzzy system with triangle and Gaussian membership functions. We conclude that neuro-fuzzy model presents almost perfect forecasts for financial distress periods as also...
Application of a Fuzzy Programming Technique to Production Planning in the Textile Industry
Elamvazuthi, I; Vasant, P; Webb, J F
2010-01-01
Many engineering optimization problems can be considered as linear programming problems where all or some of the parameters involved are linguistic in nature. These can only be quantified using fuzzy sets. The aim of this paper is to solve a fuzzy linear programming problem in which the parameters involved are fuzzy quantities with logistic membership functions. To explore the applicability of the method a numerical example is considered to determine the monthly production planning quotas and profit of a home textile group.
A New Fuzzy System Based on Rectangular Pyramid
Jiang, Mingzuo; Yuan, Xuehai; Li, Hongxing; Wang, Jiaxia
2015-01-01
A new fuzzy system is proposed in this paper. The novelty of the proposed system is mainly in the compound of the antecedents, which is based on the proposed rectangular pyramid membership function instead of t-norm. It is proved that the system is capable of approximating any continuous function of two variables to arbitrary degree on a compact domain. Moreover, this paper provides one sufficient condition of approximating function so that the new fuzzy system can approximate any continuous function of two variables with bounded partial derivatives. Finally, simulation examples are given to show how the proposed fuzzy system can be effectively used for function approximation. PMID:25874253
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吴杰康; 祝宇楠; 韦善革
2011-01-01
梯级水电站的多目标优化调度要求在保证发电量最大同时减少用水量和弃水量,提高水能利用率,为此提出了采用改进隶属度函数的梯级水电站模糊多目标优化调度模型,以梯级水电站总发电量最大、总弃水量最小及调度期末蓄水量最大为目标建立了多目标优化调度模型.传统求解多目标问题的模糊算法普遍采用半升或半降直线型隶属度函数,文中以定义域上连续可微的Sigmoid函数及反Sigmoid函数作为新的隶属度函数,确定了Sigmoid隶属度函数各参数的计算方法.改进隶属度函数的应用要求目标函数可导的非线性规划法更加适于求解该多目标模型.算例结果验证了该模型的可行性.%It is demanded for multi-objective optimal dispatching for cascaded hydropower stations to ensure as high electric energy production as possible and to reduce water consumption and water to be abandoned to improve waterpower utilization. For this reason, an optimized fuzzy multi-objective dispatching model adopting improved membership function is proposed, in which the maximum gross power generation, the maximum total abandoned water and the maximum water storage at the end of the dispatching period are taken as objective functions, for cascaded hydropower stations.In the solution of multi-objective problem by traditional fuzzy algorithms, the increasing or decreasing linear membership functions are commonly used. In this paper, the continuously differentiable Sigmoid function and inverse Sigmoid function are taken as new membership functions, then a method to calculate parameters of Sigmoid membership functions is determined. Because of applying improved membership functions, those nonlinear programming methods are suitable to solve this multi-objective dispatching model. The feasibility of the proposed model is verified by results of calculation example.
The Gaia-ESO Survey: membership and Initial Mass Function of the Gamma Velorum cluster
Prisinzano, L; Micela, G; Jeffries, R D; Franciosini, E; Sacco, G G; Frasca, A; Klutsch, A; Lanzafame, A; Alfaro, E J; Biazzo, K; Bonito, R; Bragaglia, A; Caramazza, M; Vallenari, A; Carraro, G; Costado, M T; Flaccomio, E; Jofre', P; Lardo, C; Monaco, L; Morbidelli, L; Mowlavi, N; Pancino, E; Randich, S; Zaggia, S
2016-01-01
Understanding the properties of young open clusters, such as the Initial Mass Function (IMF), star formation history and dynamic evolution, is crucial to obtain reliable theoretical predictions of the mechanisms involved in the star formation process. We want to obtain a list, as complete as possible, of confirmed members of the young open cluster Gamma Velorum, with the aim of deriving general cluster properties such as the IMF. We used all available spectroscopic membership indicators within the Gaia-ESO public archive together with literature photometry and X-ray data and, for each method, we derived the most complete list of candidate cluster members. Then, we considered photometry, gravity and radial velocities as necessary conditions to select a subsample of candidates whose membership was confirmed by using the lithium and H$\\alpha$ lines and X-rays as youth indicators. We found 242 confirmed and 4 possible cluster members for which we derived masses using very recent stellar evolutionary models. The c...
Multi-factor high-order intuitionistic fuzzy time series forecasting model
Institute of Scientific and Technical Information of China (English)
Yanan Wang; Yingjie Lei; Yang Lei; Xiaoshi Fan
2016-01-01
Fuzzy sets theory cannot describe the neutrality degree of data, which has largely limited the objectivity of fuzzy time series in uncertain data forecasting. With this regard, a multi-factor high-order intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to get unequal intervals, and a more objective technique for ascertaining member-ship and non-membership functions of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on multidimen-sional intuitionistic fuzzy modus ponens inference are established. Final y, contrast experiments on the daily mean temperature of Beijing are carried out, which show that the novel model has a clear advantage of improving the forecast accuracy.
Tuning of a neuro-fuzzy controller by genetic algorithm.
Seng, T L; Bin Khalid, M; Yusof, R
1999-01-01
Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.
Fuzzy modeling of friction by bacterial and least square optimization
Jastrzebski, Marcin
2006-03-01
In this paper a new method of tuning parameters of Sugeno fuzzy models is presented. Because modeled phenomenon is discontinuous, new type of consequent function was introduced. Described algorithm (BA+LSQ) combines bacterial algorithm (BA) for tuning parameters of membership functions and least square method (LSQ) for parameters of consequent functions.
Intelligent PI Fuzzy Control of An Electro-Hydraulic Manipulator
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Ayman A. Aly
2012-06-01
Full Text Available The development of a fuzzy-logic controller for a class of industrial hydraulic manipulator is described. The main element of the controller is a PI-type fuzzy control technique which utilizes a simple set of membership functions and rules to meet the basic control requirements of such robots. Using the triangle shaped membership function, the position of the servocylinder was successfully controlled. When the system parameter is altered, the control algorithm is shown to be robust and more faster compared to the traditional PID controller. The robustness and tracking ability of the controller were demonstrated through simulations.
Research on Modeling with Adaptive Neuro-Fuzzy Inference System%自适应神经模糊推理系统建模研究
Institute of Scientific and Technical Information of China (English)
鲁斌; 何华灿
2003-01-01
With rapid development of the fuzzy control application field, the existing system for fuzzy inferring modeling cannot more and more suit the requirements of fuzzy control. About how to apply the theories of fuzzy control to practice rapidly and conveniently, this paper presents a reasonable and practical method, which supports all sorts of fuzzy inferring system of MAMDANI and SUGENO to be modeled not only by tuning references of membership functions, but also by tuning fuzzy inferring structure. The modeling instance shows that it's practical and effective.
FICA:fuzzy imperialist competitive algorithm
Institute of Scientific and Technical Information of China (English)
Saeid ARISH; Ali AMIRI; Khadije NOORI
2014-01-01
Despite the success of the imperialist competitive algorithm (ICA) in solving optimization problems, it still suffers from frequently falling into local minima and low convergence speed. In this paper, a fuzzy version of this algorithm is proposed to address these issues. In contrast to the standard version of ICA, in the proposed algorithm, powerful countries are chosen as imperialists in each step;according to a fuzzy membership function, other countries become colonies of all the empires. In ab-sorption policy, based on the fuzzy membership function, colonies move toward the resulting vector of all imperialists. In this algorithm, no empire will be eliminated;instead, during the execution of the algorithm, empires move toward one point. Other steps of the algorithm are similar to the standard ICA. In experiments, the proposed algorithm has been used to solve the real world optimization problems presented for IEEE-CEC 2011 evolutionary algorithm competition. Results of experiments confirm the performance of the algorithm.
Fuzzy efficiency optimization of AC induction motors
Jani, Yashvant; Sousa, Gilberto; Turner, Wayne; Spiegel, Ron; Chappell, Jeff
1993-01-01
This paper describes the early states of work to implement a fuzzy logic controller to optimize the efficiency of AC induction motor/adjustable speed drive (ASD) systems running at less than optimal speed and torque conditions. In this paper, the process by which the membership functions of the controller were tuned is discussed and a controller which operates on frequency as well as voltage is proposed. The membership functions for this dual-variable controller are sketched. Additional topics include an approach for fuzzy logic to motor current control which can be used with vector-controlled drives. Incorporation of a fuzzy controller as an application-specific integrated circuit (ASIC) microchip is planned.
Cheng, Jun; Park, Ju H; Wang, Hailing
2016-11-01
This paper addresses the problem of event-triggered H∞ control for a class of T-S fuzzy nonlinear systems. An improved event-triggered scheme (ETS) characterized by discrete sampling is proposed, where the time-derivative of the membership function is not required. To get conservative conditions, the deviation bound of asynchronous normalized membership functions is considered. By utilizing the non-quadratic fuzzy line-integral Lyapunov functions and a free-matrix-based integral inequality, novel criteria for stabilization analysis of T-S fuzzy nonlinear systems are established. Finally, a truck-trailer system is provided to show the effectiveness of the proposed theories.
Fuzzy-based Navigation and Control of a Non-Holonomic Mobile Robot
Rashid, Razif; Begam, Mumtaj; Arrofiq, M
2010-01-01
In recent years, the use of non-analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation. In this paper, a theoretical model of a fuzzy based controller for an autonomous mobile robot is developed. The paper begins with the mathematical model of the robot that involves the kinematic model. Then, the fuzzy logic controller is developed and discussed in detail. The proposed method is successfully tested in simulations, and it compares the effectiveness of three different set of membership of functions. It is shown that fuzzy logic controller with input membership of three provides better performance compared with five and seven membership functions.
Robust Visual Tracking via Fuzzy Kernel Representation
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Zhiqiang Wen
2013-05-01
Full Text Available A robust visual kernel tracking approach is presented for solving the problem of existing background pixels in object model. At first, after definition of fuzzy set on image is given, a fuzzy factor is embedded into object model to form the fuzzy kernel representation. Secondly, a fuzzy membership functions are generated by center-surround approach and log likelihood ratio of feature distributions. Thirdly, details about fuzzy kernel tracking algorithm is provided. After that, methods of parameter selection and performance evaluation for tracking algorithm are proposed. At last, a mass of experimental results are done to show our method can reduce the influence of the incomplete representation of object model via integrating both color features and background features.
Statistical mechanics of fuzzy random polymer networks
Institute of Scientific and Technical Information of China (English)
陈晓红
1995-01-01
A statistical mechanics framework of fuzzy random polymer networks is established based on the theories of fuzzy systems. The entanglement effect is manifested quantitatively by introducing an entanglement tensor and membership function and the amorphous structure is treated as the fuzzy random network made up of macromolecular coils entangled randomly. A random tetrahedral entangled-crosslinked cell is chosen as an average representative unit of the fuzzy random polymer network structure. By making use of the theory of fuzzy probability and statistical mechanics, the expression for the free energy of deformation is given, which fits well with the experimental data on rubber elasticity under various deformation modes. Both classical statistical theory and Mooney-Rivlin equation can be taken as its special cases.
Several Types of Totally Continuous Functions in Double Fuzzy Topological Spaces
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Fatimah M. Mohammed
2014-01-01
Full Text Available We introduce the notions of totally continuous functions, totally semicontinuous functions, and semitotally continuous functions in double fuzzy topological spaces. Their characterizations and the relationship with other already known kinds of functions are introduced and discussed.
A Game Theoretic Sensor Resource Allocation Using Fuzzy Logic
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Stephen C. Stubberud
2013-01-01
Full Text Available A sensor resource management system that employs fuzzy logic to provide the utility functions to a game theoretic approach is developed. The application looks at a virtual fence problem where several unattended ground sensors are placed in remote locations to act as virtual sentries. The goal of the approach is to maximize the battery life while tracking targets of interest. This research also considers the incorporation of uncertainty into the fuzzy membership functions. Both type-2 fuzzy logic and the use of conditional fuzzy membership function are employed. The type-2 fuzzy logic is employed in the case of acoustical sensor tracking accuracy degradation, while the condition-based membership functions are used to adapt to different conditions, such as environmental conditions and sensor performance degradation, over time. The resource management process uses fuzzy logic to determine which of the sensor systems on a sensor pod is used to provide initial classification of the target and which sensor or sensors are to be used in tracking and better classifying the target if it is determined to be of value to the mission. The three different approaches are compared to determine when the best times for the more complex approaches are warranted.
Single Machine Scheduling Problem with Fuzzy Due Dates and Fuzzy Precedence%模糊交货期和模糊优先下的单机调度问题
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谢源; 谢剑英; 黄芹华
2005-01-01
A single machine scheduling problem involving fuzzy due dates and fuzzy precedence constraints is investigated. The fuzzy precedence reflects the satisfaction level with respect to precedence between two jobs. A membership function is associated with each job Ji, which describes the degree of satisfaction with respect to completion time of Ji. For the bi-criteria scheduling problem, an O ( n3 ) algorithm is proposed for finding nondominated solutions.
A Fixed Point Approach to the Fuzzy Stability of a Mixed Typ e Functional Equation
Institute of Scientific and Technical Information of China (English)
Cheng Li-hua; Zhang Jun-min
2016-01-01
Through the paper, a general solution of a mixed type functional equation in fuzzy Banach space is obtained and by using the fixed point method a generalized Hyers-Ulam-Rassias stability of the mixed type functional equation in fuzzy Banach space is proved.
Fuzzy Neuron: Method and Hardware Realization
Krasowski, Michael J.; Prokop, Norman F.
2014-01-01
This innovation represents a method by which single-to-multi-input, single-to-many-output system transfer functions can be estimated from input/output data sets. This innovation can be run in the background while a system is operating under other means (e.g., through human operator effort), or may be utilized offline using data sets created from observations of the estimated system. It utilizes a set of fuzzy membership functions spanning the input space for each input variable. Linear combiners associated with combinations of input membership functions are used to create the output(s) of the estimator. Coefficients are adjusted online through the use of learning algorithms.
Institute of Scientific and Technical Information of China (English)
ZHANG Long-ting; HE Zhe-ming; GUO Hui-xin
2003-01-01
The design target with definite purpose character of product quality was described in a real fuzzy number ( named fury target for short in this paper), and its membership functions in common use were given. According to the fury probability theory and the robust design principle, the robust design rule based on fuzzy probability (named fuzzy robust design rule for short) was put forward and its validity and practicability were analyzed and tested with a design example. The theoretical analysis and the design examples make clear that, while the fuzzy robust design rule was used, the fine design effect can be obtained and the fury robust design rule can be very suitable for the choice of the membership function of the fuzzy target; so it has a particular advantage.
Institute of Scientific and Technical Information of China (English)
1998-01-01
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Design of the Fuzzy Control Systems Based on Genetic Algorithm for Intelligent Robots
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Gyula Mester
2014-07-01
Full Text Available This paper gives the structure optimization of fuzzy control systems based on genetic algorithm in the MATLAB environment. The genetic algorithm is a powerful tool for structure optimization of the fuzzy controllers, therefore, in this paper, integration and synthesis of fuzzy logic and genetic algorithm has been proposed. The genetic algorithms are applied for fuzzy rules set, scaling factors and membership functions optimization. The fuzzy control structure initial consist of the 3 membership functions and 9 rules and after the optimization it is enough for the 4 DOF SCARA Robot control to compensate for structured and unstructured uncertainty. Fuzzy controller with the generalized bell membership functions can provide better dynamic performance of the robot then with the triangular membership functions. The proposed joint-space controller is computationally simple and had adaptability to a sudden change in the dynamics of the robot. Results of the computer simulation applied to the 4 DOF SCARA Robot show the validity of the proposed method.
Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data
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Peter Hofmann
2016-06-01
Full Text Available The classes in fuzzy classification schemes are defined as fuzzy sets, partitioning the feature space through fuzzy rules, defined by fuzzy membership functions. Applying fuzzy classification schemes in remote sensing allows each pixel or segment to be an incomplete member of more than one class simultaneously, i.e., one that does not fully meet all of the classification criteria for any one of the classes and is member of more than one class simultaneously. This can lead to fuzzy, ambiguous and uncertain class assignation, which is unacceptable for many applications, indicating the need for a reliable defuzzification method. Defuzzification in remote sensing has to date, been performed by “crisp-assigning” each fuzzy-classified pixel or segment to the class for which it best fulfills the fuzzy classification rules, regardless of its classification fuzziness, uncertainty or ambiguity (maximum method. The defuzzification of an uncertain or ambiguous fuzzy classification leads to a more or less reliable crisp classification. In this paper the most common parameters for expressing classification uncertainty, fuzziness and ambiguity are analysed and discussed in terms of their ability to express the reliability of a crisp classification. This is done by means of a typical practical example from Object Based Image Analysis (OBIA.
A hybrid fuzzy MCDM approach to maintenance Quality Function Deployment
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Davy George Valavi
2015-01-01
Full Text Available Maintenance Quality Function Deployment (MQFD is a model, which enhances the synergic power of Quality Function Deployment (QFD and Total Productive Maintenance (TPM. One of the crucial and important steps during the implementation of MQFD is the determination of the importance or weightages of the critical factors (CF and sub factors (SF. The CFs and SFs have to be compared precisely for the successful implementation of MQFD. The crisp pair-wise comparison in the conventional Analytical Hierarchy Process (AHP may be insufficient to determine the degree of weightage of CFs and SFs where vagueness and uncetainties are associated. In this paper, a modification of AHP based MQFD by incorporating fuzzy operations is proposed, which can improve the accuracy of determination of the weightages. A case study showing the applicability of this method is illustrated in this paper.
Predictive functional control based on fuzzy T-S model for HVAC systems temperature control
Institute of Scientific and Technical Information of China (English)
Hongli L(U); Lei JIA; Shulan KONG; Zhaosheng ZHANG
2007-01-01
In heating,ventilating and air-conditioning(HVAC)systems,there exist severe nonlinearity,time-varying nature,disturbances and uncertainties.A new predictive functional control based on Takagi-Sugeno(T-S)fuzzy model was proposed to control HVAC systems.The T-S fuzzy model of stabilized controlled process was obtained using the least squares method,then on the basis of global linear predictive model from T-S fuzzy model,the process was controlled by the predictive functional controller.Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model.Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness.Compared with the conventional PID controller,this control strategy has the advantages of less overshoot and shorter setting time,etc.
Functional equivalence between radial basis function networks and fuzzy inference systems.
Jang, J R; Sun, C T
1993-01-01
It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe that two models stemming from different origins turn out to be functionally equivalent.
A Simplified Output Regulator for a Class of Takagi-Sugeno Fuzzy Models
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Tonatiuh Hernández-Cortés
2015-01-01
Full Text Available This paper is devoted to solve the regulation problem on the basis of local regulators, which are combined using “new” membership functions. As a result, the exact tracking of references is achieved. The design of linear local regulators is suggested in this paper, but now adequate membership functions are computed in order to ensure the proper combination of the local regulators in the interpolation regions. These membership functions, which are given as mathematical expressions, solve the fuzzy regulation problem in a relative simple way. The form of the new membership functions is systematically derived for a class of Takagi-Sugeno (T-S fuzzy systems. Some numerical examples are used to illustrate the viability of the proposed approach.
Optimal operating policy for a controllable queueing model with a fuzzy environment
Institute of Scientific and Technical Information of China (English)
Chuen-homg LIN; Jau-chuan KE
2009-01-01
We construct the membership functions of the fuzzy objective values of a controllable queueing model, in which cost elements, arrival rate and service rate are all fuzzy numbers. Based on Zadeh's extension principle, a set of parametric nonlinear programs is developed to find the upper and lower bounds of the minimal average total cost per unit time at the possibility level. The membership functions of the minimal average total cost are further constructed using different values of the possibility level. A numerical example is solved successfully to illustrate the validity of the proposed approach. Because the object value is ex-pressed and governed by the membership functions, the optimization problem in a fuzzy environment for the controllable queueing models is represented more accurately and analytical results are more useful for system designers and practitioners.
A NEW METHOD FOR CONSTRUCTING CONFIDENCE INTERVAL FOR CPM BASED ON FUZZY DATA
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Bahram Sadeghpour Gildeh
2011-06-01
Full Text Available A measurement control system ensures that measuring equipment and measurement processes are fit for their intended use and its importance in achieving product quality objectives. In most real life applications, the observations are fuzzy. In some cases specification limits (SLs are not precise numbers and they are expressed in fuzzy terms, s o that the classical capability indices could not be applied. In this paper we obtain 100(1 - α% fuzzy confidence interval for C pm fuzzy process capability index, where instead of precise quality we have two membership functions for specification limits.
Determining a human cardiac pacemaker using fuzzy logic
Varnavsky, A. N.; Antonenco, A. V.
2017-01-01
The paper presents a possibility of estimating a human cardiac pacemaker using combined application of nonlinear integral transformation and fuzzy logic, which allows carrying out the analysis in the real-time mode. The system of fuzzy logical conclusion is proposed, membership functions and rules of fuzzy products are defined. It was shown that the ratio of the value of a truth degree of the winning rule condition to the value of a truth degree of any other rule condition is at least 3.
Marginal linearization method in modeling on fuzzy control systems
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Marginal linearization method in modeling on fuzzy control systems is proposed, which is to deal with the nonlinear model with variable coefficients. The method can turn a nonlinear model with variable coefficients into a linear model with variable coefficients in the way that the membership functions of the fuzzy sets in fuzzy partitions of the universes are changed from triangle waves into rectangle waves. However, the linearization models are incomplete in their forms because of their lacking some items. For solving this problem, joint approximation by using linear models is introduced. The simulation results show that marginal linearization models are of higher approximation precision than their original nonlinear models.
Constraint-Based Fuzzy Models for an Environment with Heterogeneous Information-Granules
Institute of Scientific and Technical Information of China (English)
K. Robert Lai; Yi-Yuan Chiang
2006-01-01
A novel framework for fuzzy modeling and model-based control design is described. Based on the theory of fuzzy constraint processing, the fuzzy model can be viewed as a generalized Takagi-Sugeno (TS) fuzzy model with fuzzy functional consequences. It uses multivariate antecedent membership functions obtained by granular-prototype fuzzy clustering methods and consequent fuzzy equations obtained by fuzzy regression techniques. Constrained optimization is used to estimate the consequent parameters, where the constraints are based on control-relevant a priori knowledge about the modeled process. The fuzzy-constraint-based approach provides the following features. 1) The knowledge base of a constraint-based fuzzy model can incorporate information with various types of fuzzy predicates. Consequently, it is easy to provide a fusion of different types of knowledge. The knowledge can be from data-driven approaches and/or from controlrelevant physical models. 2) A corresponding inference mechanism for the proposed model can deal with heterogeneous information granules. 3) Both numerical and linguistic inputs can be accepted for predicting new outputs.The proposed techniques are demonstrated by means of two examples: a nonlinear function-fitting problem and the well-known Box-Jenkins gas furnace process. The first example shows that the proposed model uses fewer fuzzy predicates achieving similar results with the traditional rule-based approach, while the second shows the performance can be significantly improved when the control-relevant constraints are considered.
Optimal Power Flow Using Adaptive Fuzzy Logic Controllers
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Abdullah M. Abusorrah
2013-01-01
Full Text Available This paper presents an approach for optimum reactive power dispatch through the power network with flexible AC transmission systems (FACTSs devices, using adaptive fuzzy logic controller (AFLC driven by adaptive fuzzy sets (AFSs. The membership functions of AFLC are optimized based on 2nd-order fuzzy set specifications. The operation of FACTS devices (particularly, static VAR compensator (SVC and the setting of their control parameters (QSVC are optimized dynamically based on the proposed AFLC to enhance the power system stability in addition to their main function of power flow control. The proposed AFLC is compared with a static fuzzy logic controller (SFLC, driven by a fixed fuzzy set (FFS. Simulation studies were carried out and validated on the standard IEEE 30-bus test system.
Fuzzy Clustering with Novel Separable Criterion
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Fuzzy clustering has been used widely in pattern recognition, image processing, and data analysis. An improved fuzzy clustering algorithm was developed based on the conventional fuzzy c-means (FCM) to obtain better quality clustering results. The update equations for the membership and the cluster center are derived from the alternating optimization algorithm. Two fuzzy scattering matrices in the objective function assure the compactness between data points and cluster centers, and also strengthen the separation between cluster centers in terms of a novel separable criterion. The clustering algorithm properties are shown to be an improvement over the FCM method's properties. Numerical simulations show that the clustering algorithm gives more accurate clustering results than the FCM method.
Properties of Measure-based Fuzzy Logic
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Measure-based fuzzy logic, which is constructed on the basis of eight axioms, is a seemingly powerful fuzzy logic. It possesses several remarkable properties. (1) It is an extended Boolean logic, satisfying all the properties of Boolean algebra, including the law of excluded middle and the law of contradiction. (2) It is conditional. Conditional membership functions play an important role in this logic. (3) The negation operator is not independently defined with the conjunction and disjunction operators, but on the contrary, it is derived from them. (4) Zadehs fuzzy logic is included in it as a particular case. (5) It gives more hints to the relationship between fuzzy logic and probability logic.
Fuzzy 2-partition entropy threshold selection based on Big Bang–Big Crunch Optimization algorithm
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Baljit Singh Khehra
2015-03-01
Full Text Available The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA-based, Biogeography-based Optimization (BBO-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.
Interval TYPE-2 Fuzzy Based Neural Network for High Resolution Remote Sensing Image Segmentation
Wang, Chunyan; Xu, Aigong; Li, Chao; Zhao, Xuemei
2016-06-01
Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).
Adaptive neural-based fuzzy modeling for biological systems.
Wu, Shinq-Jen; Wu, Cheng-Tao; Chang, Jyh-Yeong
2013-04-01
The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems.
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
For structural system with random basic variables as well as fuzzy basic variables,uncertain propagation from two kinds of basic variables to the response of the structure is investigated.A novel algorithm for obtaining membership function of fuzzy reliability is presented with saddlepoint approximation(SA)based line sampling method.In the presented method,the value domain of the fuzzy basic variables under the given membership level is firstly obtained according to their membership functions.In the value domain of the fuzzy basic variables corresponding to the given membership level,bounds of reliability of the structure response satisfying safety requirement are obtained by employing the SA based line sampling method in the reduced space of the random variables.In this way the uncertainty of the basic variables is propagated to the safety measurement of the structure,and the fuzzy membership function of the reliability is obtained.Compared to the direct Monte Carlo method for propagating the uncertainties of the fuzzy and random basic variables,the presented method can considerably improve computational efficiency with acceptable precision.The presented method has wider applicability compared to the transformation method,because it doesn’t limit the distribution of the variable and the explicit expression of performance function, and no approximation is made for the performance function during the computing process.Additionally,the presented method can easily treat the performance function with cross items of the fuzzy variable and the random variable,which isn’t suitably approximated by the existing transformation methods.Several examples are provided to illustrate the advantages of the presented method.
Genetically Generated Double-Level Fuzzy Controller with a Fuzzy Adjustment Strategy
DEFF Research Database (Denmark)
Achiche, Sofiane; Wang, Wei; Fan, Zhun;
2007-01-01
This paper describes the use of a genetic algorithm (GA) in tuning a double-level modular fuzzy logic controller (DLMFLC), which can expand its control working zone to a larger spectrum than a single-level FLC. The first-level FLCs are tuned by a GA so that the input parameters of their membership...... functions and fuzzy rules are optimized according to their individual working zones. The second-level FLC is then used to adjust contributions of the first-level FLCs to the final output signal of the whole controller, i.e., DLMFLC, so that it can function in a wider spectrum covering all individual working...
Stability of Various Functional Equations in Non-Archimedean Intuitionistic Fuzzy Normed Spaces
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Syed Abdul Mohiuddine
2012-01-01
Full Text Available We define and study the concept of non-Archimedean intuitionistic fuzzy normed space by using the idea of t-norm and t-conorm. Furthermore, by using the non-Archimedean intuitionistic fuzzy normed space, we investigate the stability of various functional equations. That is, we determine some stability results concerning the Cauchy, Jensen and its Pexiderized functional equations in the framework of non-Archimedean IFN spaces.
MASALAH PROGRAMA LINIER FUZZY DENGAN FUNGSI KEANGGOTAAN LINIER
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Nyoman Sutapa
2000-01-01
Full Text Available In practice, the certainess assumption for parameters in linear programming are difficult to pullfiled. The uncertainties are sometimes coming from subjective and intuitive policies. To solve and accommodate these problems, will be approximated by fuzzy set theory. In this article, modeling of linear programming with fuzzy set will be discussed, followed by two cases with membership function are trapezoidal and triangular. Abstract in Bahasa Indonesia : Asumsi kepastian nilai-nilai parameter, dalam pengambilan keputusan yang dimodelkan dengan programa linier, dalam praktek sering sulit dipenuhi. Ketidakpastian yang muncul kadang diakibatkan oleh suatu kebijakan yang intuitif dan subjektif. Untuk memecahkan dan mengakomodasi ketidakpastian seperti tersebut, akan didekati dengan teori himpunan fuzzy. Dalam makalah ini, pemodelan programa linier dengan teori himpunan fuzzy tersebut, akan didiskusikan dengan dua kasus, masing-masing dengan menggunakan fungsi keanggotaan linier, yaitu trapezoida dan triangular. Kata kunci: programa linier, himpunan fuzzy.
A Generalized Fuzzy Integer Programming Approach for Environmental Management under Uncertainty
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Y. R. Fan
2014-01-01
Full Text Available In this study, a generalized fuzzy integer programming (GFIP method is developed for planning waste allocation and facility expansion under uncertainty. The developed method can (i deal with uncertainties expressed as fuzzy sets with known membership functions regardless of the shapes (linear or nonlinear of these membership functions, (ii allow uncertainties to be directly communicated into the optimization process and the resulting solutions, and (iii reflect dynamics in terms of waste-flow allocation and facility-capacity expansion. A stepwise interactive algorithm (SIA is proposed to solve the GFIP problem and generate solutions expressed as fuzzy sets. The procedures of the SIA method include (i discretizing the membership function grade of fuzzy parameters into a set of α-cut levels; (ii converting the GFIP problem into an inexact mixed-integer linear programming (IMILP problem under each α-cut level; (iii solving the IMILP problem through an interactive algorithm; and (iv approximating the membership function for decision variables through statistical regression methods. The developed GFIP method is applied to a municipal solid waste (MSW management problem to facilitate decision making on waste flow allocation and waste-treatment facilities expansion. The results, which are expressed as discrete or continuous fuzzy sets, can help identify desired alternatives for managing MSW under uncertainty.
Color Image Enhancement Based on Maximum Fuzzy Entropy
Institute of Scientific and Technical Information of China (English)
QU Yi; XU Li-hong; KANG Qi
2004-01-01
A color image enhancement approach based on maximum fuzzy entropy and genetic algorithm is proposed in this paper. It enhances color images by stretching the contrast of S and I components respectively in the HSI color representation. The image is transformed from the property domain to the fuzzy domain with S-function. To preserve as much information as possible in the fuzzy the domain, the fuzzy entropy function is used as objective function in a genetic algorithm to optimize three parameters of the S-function. The Sigmoid function is applied to intensify the membership values and the results are transformed back to the property domain to produce the enhanced image. Experiments show the effectiveness of the approach.
Improvement of Fuzzy Image Contrast Enhancement Using Simulated Ergodic Fuzzy Markov Chains
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Behrouz Fathi-Vajargah
2014-01-01
Full Text Available This paper presents a novel fuzzy enhancement technique using simulated ergodic fuzzy Markov chains for low contrast brain magnetic resonance imaging (MRI. The fuzzy image contrast enhancement is proposed by weighted fuzzy expected value. The membership values are then modified to enhance the image using ergodic fuzzy Markov chains. The qualitative performance of the proposed method is compared to another method in which ergodic fuzzy Markov chains are not considered. The proposed method produces better quality image.
Institute of Scientific and Technical Information of China (English)
刘民; 李法朝; 吴澄
2003-01-01
Measuring the difference between fuzzy numbers is often needed in many fuzzy optimizationproblems such as manufacturing system production line scheduling with uncertainty environments. In thispaper, based on the distance function of plane R2 and the level importance function, we establish theUID-metric and LPID-metric of measuring the difference between fuzzy numbers, and discuss the basicproperties of UID-metric and LPID-metric, and prove that fuzzy number spaces are metric spaces aboutUID-metric and LPID-metric if and only if the level importance function /(λ) ≠ 0 almost everywhere on [0,1]. Further, we discuss the convergence, separability and completeness of UID-metric and LPID-metricbased on the norms of plane R2. Finally, we analyze the characteristics of UID-metric and LPID-metric bysome application examples.
Energy Technology Data Exchange (ETDEWEB)
Choi, W.K.; Akizuki, K. (Waseda Univ., Tokyo (Japan)); Lee, H.H. (Fukuoka Inst. of Tech., Fukuoka (Japan))
1991-05-20
The target of voice recognition is to recognize continuous speech which is effective for speech recognition of unspecified persons. As a new matching method, the variations of feature parameters of speakers are represented as fuzzy variables to express the variation by membership functions. It is a new pattern matching method of fuzzy inference using feature parameters, fuzzy relation and synthesis of each formant, and the fuzzy rule. It is a recognition method for the inference of best formant which matches the fact by providing each characteristic quantity and fuzzy rule for composite calculation. For consonant recognition, pitch, logarithmic energies, zero crossing rates, etc. are used which represent features of each formant. KOSRES 2, recognition system for continuous Korean speech, was structured using this method which was subjected to recognition experiments on continuous Korean speech, and the recognition method by fuzzy inference is found to be effective for speech recognition of unspecified persons. 8 refs., 9 figs., 3 tabs.
A Novel Multicriteria Group Decision Making Approach With Intuitionistic Fuzzy SIR Method
Chai, Junyi
2011-01-01
The superiority and inferiority ranking (SIR) method is a generation of the well-known PROMETHEE method, which can be more efficient to deal with multi-criterion decision making (MCDM) problem. Intuitionistic fuzzy sets (IFSs), as an important extension of fuzzy sets (IFs), include both membership functions and non-membership functions and can be used to, more precisely describe uncertain information. In real world, decision situations are usually under uncertain environment and involve multiple individuals who have their own points of view on handing of decision problems. In order to solve uncertainty group MCDM problem, we propose a novel intuitionistic fuzzy SIR method in this paper. This approach uses intuitionistic fuzzy aggregation operators and SIR ranking methods to handle uncertain information; integrate individual opinions into group opinions; make decisions on multiple-criterion; and finally structure a specific decision map. The proposed approach is illustrated in a simulation of group decision ma...
Power transformer fault diagnosis model based on rough set theory with fuzzy representation
Institute of Scientific and Technical Information of China (English)
Li Minghua; Dong Ming; Yan Zhang
2007-01-01
Objective Due to the incompleteness and complexity of fault diagnosis for power transformers, a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented. Fuzzy set theory is used both for representation of incipient faults' indications and producing a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules that model indicative regions in the granulated feature space. The fuzzy membership functions corresponding to the indicative regions, modelled by rules, are stored as cases. Results Diagnostic conclusions are made using a similarity measure based on these membership functions. Each case involves only a reduced number of relevant features making this scheme suitable for fault diagnosis. Conclusion Superiority of this method in terms of classification accuracy and case generation is demonstrated.
Totally semi-continuous and semi totally-continuous functions in double fuzzy topological spaces
Mahmood Mohammed, Fatimah; Md Noorani, Mohd Salmi; Salleh, Abdul Razak
2013-04-01
The aim of this paper is to introduce new classes of functions called totally-continuous functions and its variants totally semi-continuous functions and semi totally-continuous functions in double fuzzy topological spaces. Their characterizations, examples and relationship with other notions of continuous functions in this space are provided.
Directory of Open Access Journals (Sweden)
Ching-Hung Lee
2011-01-01
Full Text Available This paper proposes a new type fuzzy neural systems, denoted IT2RFNS-A (interval type-2 recurrent fuzzy neural system with asymmetric membership function, for nonlinear systems identification and control. To enhance the performance and approximation ability, the triangular asymmetric fuzzy membership function (AFMF and TSK-type consequent part are adopted for IT2RFNS-A. The gradient information of the IT2RFNS-A is not easy to obtain due to the asymmetric membership functions and interval valued sets. The corresponding stable learning is derived by simultaneous perturbation stochastic approximation (SPSA algorithm which guarantees the convergence and stability of the closed-loop systems. Simulation and comparison results for the chaotic system identification and the control of Chua's chaotic circuit are shown to illustrate the feasibility and effectiveness of the proposed method.
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Shruti Jain
2015-06-01
Full Text Available In this paper a well defined method for the design of JNK pathway for epidermal growth factor/ insulin using fuzzy system using operational transconductance amplifier was discussed. Fuzzy system includes fuzzification of the input variables, application of the fuzzy operator (AND or OR in the antecedent, implication from the antecedent to the consequent, aggregation of the consequents across the rules, and defuzzfication. Fuzzy system with various electrical parameters for different voltages of OTA with different membership function was found. Results with 3V were the best.
Jung, Hye-Young; Leem, Sangseob; Lee, Sungyoung; Park, Taesung
2016-12-01
Gene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification. We regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure. Two simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD. We propose a novel Fuzzy MDR method to detect gene-gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at https://statgen.snu.ac.kr/software/FuzzyMDR. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
A physical analogy to fuzzy clustering
DEFF Research Database (Denmark)
Jantzen, Jan
2004-01-01
This tutorial paper provides an interpretation of the membership assignment in the fuzzy clustering algorithm fuzzy c-means. The membership of a data point to several clusters is shown to be analogous to the gravitational forces between bodies of mass. This provides an alternative way to explain...
A physical analogy to fuzzy clustering
DEFF Research Database (Denmark)
Jantzen, Jan
2004-01-01
This tutorial paper provides an interpretation of the membership assignment in the fuzzy clustering algorithm fuzzy c-means. The membership of a data point to several clusters is shown to be analogous to the gravitational forces between bodies of mass. This provides an alternative way to explain ...
DEFF Research Database (Denmark)
Åkerstrøm Andersen, Niels; Pors, Justine Grønbæk
2014-01-01
This article studies the implications of current attempts by organizations to adapt to a world of constant change by introducing the notion of playful organizational membership. To this end we conduct a brief semantic history of organizational play and argue that when organizations play, employees...... are expected to engage in playful exploration of alternative selves. Drawing on Niklas Luhmann's theory of time and decision-making and Gregory Bateson's theory of play, the article analyses three empirical examples of how games play with conceptions of time. We explore how games represent an organizational...... desire to reach out - not just to the future - but to futures beyond the future presently imaginable. The article concludes that playful membership is membership through which employees are expected to develop a surplus of potential identities and continuously cross boundaries between real and virtual...
Analysis and synthesis for interval type-2 fuzzy-model-based systems
Li, Hongyi; Lam, Hak-Keung; Gao, Yabin
2016-01-01
This book develops a set of reference methods capable of modeling uncertainties existing in membership functions, and analyzing and synthesizing the interval type-2 fuzzy systems with desired performances. It also provides numerous simulation results for various examples, which fill certain gaps in this area of research and may serve as benchmark solutions for the readers. Interval type-2 T-S fuzzy models provide a convenient and flexible method for analysis and synthesis of complex nonlinear systems with uncertainties.
FSILP: fuzzy-stochastic-interval linear programming for supporting municipal solid waste management.
Li, Pu; Chen, Bing
2011-04-01
Although many studies on municipal solid waste management (MSW management) were conducted under uncertain conditions of fuzzy, stochastic, and interval coexistence, the solution to the conventional linear programming problems of integrating fuzzy method with the other two was inefficient. In this study, a fuzzy-stochastic-interval linear programming (FSILP) method is developed by integrating Nguyen's method with conventional linear programming for supporting municipal solid waste management. The Nguyen's method was used to convert the fuzzy and fuzzy-stochastic linear programming problems into the conventional linear programs, by measuring the attainment values of fuzzy numbers and/or fuzzy random variables, as well as superiority and inferiority between triangular fuzzy numbers/triangular fuzzy-stochastic variables. The developed method can effectively tackle uncertainties described in terms of probability density functions, fuzzy membership functions, and discrete intervals. Moreover, the method can also improve upon the conventional interval fuzzy programming and two-stage stochastic programming approaches, with advantageous capabilities that are easily achieved with fewer constraints and significantly reduces consumption time. The developed model was applied to a case study of municipal solid waste management system in a city. The results indicated that reasonable solutions had been generated. The solution can help quantify the relationship between the change of system cost and the uncertainties, which could support further analysis of tradeoffs between the waste management cost and the system failure risk.
Directory of Open Access Journals (Sweden)
Aihong Ren
2016-01-01
Full Text Available This paper is concerned with a class of fully fuzzy bilevel linear programming problems where all the coefficients and decision variables of both objective functions and the constraints are fuzzy numbers. A new approach based on deviation degree measures and a ranking function method is proposed to solve these problems. We first introduce concepts of the feasible region and the fuzzy optimal solution of a fully fuzzy bilevel linear programming problem. In order to obtain a fuzzy optimal solution of the problem, we apply deviation degree measures to deal with the fuzzy constraints and use a ranking function method of fuzzy numbers to rank the upper and lower level fuzzy objective functions. Then the fully fuzzy bilevel linear programming problem can be transformed into a deterministic bilevel programming problem. Considering the overall balance between improving objective function values and decreasing allowed deviation degrees, the computational procedure for finding a fuzzy optimal solution is proposed. Finally, a numerical example is provided to illustrate the proposed approach. The results indicate that the proposed approach gives a better optimal solution in comparison with the existing method.
Mueen, Zeina; Ramli, Razamin; Zaibidi, Nerda Zura
2016-08-01
In this paper, we propose a procedure to find different performance measurements under crisp value terms for new single fuzzy queue FM/F(H1,H2)/1 with two classes, where arrival rate and service rates are all fuzzy numbers which are represented by triangular and trapezoidal fuzzy numbers. The basic idea is to obtain exact crisp values from the fuzzy value, which is more realistic in the practical queueing system. This is done by adopting left and right ranking method to remove the fuzziness before computing the performance measurements using conventional queueing theory. The main advantage of this approach is its simplicity in application, giving exact real data around fuzzy values. This approach can also be used in all types of queueing systems by taking two types of symmetrical linear membership functions. Numerical illustration is solved in this article to obtain two groups of crisp values in the queueing system under consideration.
A Simple Fuzzy Logic Approach for Induction Motors Stator Condition Monitoring
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M. Zeraoulia
2005-03-01
Full Text Available Many researches dealt with the problem of induction motors fault detection and diagnosis. The major difficulty is the lack of an accurate model that describes a fault motor. Moreover, experienced engineers are often required to interpret measurement data that are frequently inconclusive. A fuzzy logic approach may help to diagnose induction motor faults. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. Therefore, this paper applies fuzzy logic to induction motors fault detection and diagnosis. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. The induction motor condition is diagnosed using a compositional rule of fuzzy inference.
Modeling Multisource-heterogeneous Information Based on Random Set and Fuzzy Set Theory
Institute of Scientific and Technical Information of China (English)
WEN Cheng-lin; XU Xiao-bin
2006-01-01
This paper presents a new idea, named as modeling multisensor-heterogeneous information, to incorporate the fuzzy logic methodologies with mulitsensor-multitarget system under the framework of random set theory. Firstly, based on strong random set and weak random set, the unified form to describe both data (unambiguous information) and fuzzy evidence (uncertain information) is introduced. Secondly, according to signatures of fuzzy evidence, two Bayesian-markov nonlinear measurement models are proposed to fuse effectively data and fuzzy evidence. Thirdly, by use of "the models-based signature-matching scheme", the operation of the statistics of fuzzy evidence defined as random set can be translated into that of the membership functions of relative point state variables. These works are the basis to construct qualitative measurement models and to fuse data and fuzzy evidence.
Uncertainty in Interval Type-2 Fuzzy Systems
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Sadegh Aminifar
2013-01-01
Full Text Available This paper studies uncertainty and its effect on system response displacement. The paper also describes how IT2MFs (interval type-2 membership functions differentiate from T1MFs (type-1 membership functions by adding uncertainty. The effect of uncertainty is modeled clearly by introducing a technique that describes how uncertainty causes membership degree reduction and changing the fuzzy word meanings in fuzzy logic controllers (FLCs. Several criteria are discussed for the measurement of the imbalance rate of internal uncertainty and its effect on system behavior. Uncertainty removal is introduced to observe the effect of uncertainty on the output. The theorem of uncertainty avoidance is presented for describing the role of uncertainty in interval type-2 fuzzy systems (IT2FSs. Another objective of this paper is to derive a novel uncertainty measure for IT2MFs with lower complexity and clearer presentation. Finally, for proving the affectivity of novel interpretation of uncertainty in IT2FSs, several investigations are done.
Distributed Fuzzy CFAR Detection for Weibull Clutter
Zaimbashi, Amir; Taban, Mohammad Reza; Nayebi, Mohammad Mehdi
In Distributed detection systems, restricting the output of the local decision to one bit certainly implies a substantial information loss. In this paper, we consider the fuzzy detection, which uses a function called membership function for mapping the observation space of each local detector to a value between 0 and 1, indicating the degree of assurance about presence or absence of a signal. In this case, we examine the problem of distributed Maximum Likelihood (ML) and Order Statistic (OS) constant false alarm rate (CFAR) detections using fuzzy fusion rules such as “Algebraic Product” (AP), “Algebraic Sum” (AS), “Union” (Un) and “Intersection” (IS) in the fusion centre. For the Weibull clutter, the expression of the membership function based on the ML or OS CFAR processors in the local detectors is also obtained. For comparison, we consider a binary distributed detector, which uses the Maximum Likelihood and Algebraic Product (MLAP) or Order Statistic and Algebraic Product (OSAP) CFAR processors as the local detectors. In homogenous and non homogenous situations, multiple targets or clutter edge, the performances of the fuzzy and binary distributed detectors are analyzed and compared. The simulation results indicate the superior and robust performance of the distributed systems using fuzzy detection in the homogenous and non homogenous situations.
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Yi-hua Zhong
2013-01-01
Full Text Available Recently, various methods have been developed for solving linear programming problems with fuzzy number, such as simplex method and dual simplex method. But their computational complexities are exponential, which is not satisfactory for solving large-scale fuzzy linear programming problems, especially in the engineering field. A new method which can solve large-scale fuzzy number linear programming problems is presented in this paper, which is named a revised interior point method. Its idea is similar to that of interior point method used for solving linear programming problems in crisp environment before, but its feasible direction and step size are chosen by using trapezoidal fuzzy numbers, linear ranking function, fuzzy vector, and their operations, and its end condition is involved in linear ranking function. Their correctness and rationality are proved. Moreover, choice of the initial interior point and some factors influencing the results of this method are also discussed and analyzed. The result of algorithm analysis and example study that shows proper safety factor parameter, accuracy parameter, and initial interior point of this method may reduce iterations and they can be selected easily according to the actual needs. Finally, the method proposed in this paper is an alternative method for solving fuzzy number linear programming problems.
On generalized fuzzy strongly semiclosed sets in fuzzy topological spaces
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Oya Bedre Ozbakir
2002-01-01
semiclosed, generalized fuzzy almost-strongly semiclosed, generalized fuzzy strongly closed, and generalized fuzzy almost-strongly closed sets. In the light of these definitions, we also define some generalizations of fuzzy continuous functions and discuss the relations between these new classes of functions and other fuzzy continuous functions.
Risk analysis with a fuzzy-logic approach of a complex installation
Peikert, Tim; Garbe, Heyno; Potthast, Stefan
2016-09-01
This paper introduces a procedural method based on fuzzy logic to analyze systematic the risk of an electronic system in an intentional electromagnetic environment (IEME). The method analyzes the susceptibility of a complex electronic installation with respect to intentional electromagnetic interference (IEMI). It combines the advantages of well-known techniques as fault tree analysis (FTA), electromagnetic topology (EMT) and Bayesian networks (BN) and extends the techniques with an approach to handle uncertainty. This approach uses fuzzy sets, membership functions and fuzzy logic to handle the uncertainty with probability functions and linguistic terms. The linguistic terms add to the risk analysis the knowledge from experts of the investigated system or environment.
Indirect adaptive fuzzy control for a class of nonlinear discrete-time systems
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
An indirect adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this method, two fuzzy logic systems are used to approximate the unknown functions, and the parameters of membership functions in fuzzy logic systems are adjusted according to adaptive laws for the purpose of controlling the plant to track a reference trajectory. It is proved that the scheme can not only guarantee the boundedness of the input and output of the closed-loop system, but also make the tracking error converge to a small neighborhood of the origin. Simulation results indicate the effectiveness of this scheme.
Determination of interrill soil erodibility coefficient based on Fuzzy and Fuzzy-Genetic Systems
Directory of Open Access Journals (Sweden)
Habib Palizvan Zand
2017-02-01
Full Text Available Introduction: Although the fuzzy logic science has been used successfully in various sudies of hydrology and soil erosion, but in literature review no article was found about its performance for estimating of interrill erodibility. On the other hand, studies indicate that genetic algorithm techniques can be used in fuzzy models and finding the appropriate membership functions for linguistic variables and fuzzy rules. So this study was conducted to develop the fuzzy and fuzzy–genetics models and investigation of their performance in the estimation of soil interrill erodibility factor (Ki. Materials and Methods: For this reason 36 soil samples with different physical and chemical properties were collected from west of Azerbaijan province . soilsamples were also taken from the Ap or A horizon of each soil profile. The samples were air-dried , sieved and Some soil characteristics such as soil texture, organic matter (OM, cation exchange capacity (CEC, sodium adsorption ratio (SAR, EC and pH were determined by the standard laboratory methods. Aggregates size distributions (ASD were determined by the wet-sieving method and fractal dimension of soil aggregates (Dn was also calculated. In order to determination of soil interrill erodibility, the flume experiment performed by packing soil a depth of 0.09-m in 0.5 × 1.0 m. soil was saturated from the base and adjusted to 9% slope and was subjected to at least 90 min rainfall . Rainfall intensity treatments were 20, 37 and 47 mm h-1. During each rainfall event, runoff was collected manually in different time intervals, being less than 60 s at the beginning, up to 15 min near the end of the test. At the end of the experiment, the volumes of runoff samples and the mass of sediment load at each time interval were measured. Finally interrill erodibility values were calculated using Kinnell (11 Equation. Then by statistical analyses Dn and sand percent of the soils were selected as input variables and Ki as
Institute of Scientific and Technical Information of China (English)
熊开封; 张华
2015-01-01
为优化模糊神经网络的实时性、学习速度、收敛性、稳定性，在移动机器人局部路径规划中构建了基于实际隶属函数T-S（Takagi-Sugeno）模型的改进型模糊神经网络。对外部环境信息用多传感器（超声波、摄像头）采集并优化，将机器人横纵坐标及行进方向作为输入、机器人下一步行进方向及速度作为输出，以便机器人实现局部路径规划；结合动态环境下机器人路径规划的实际，综合考虑二维直角坐标体系下机器人、障碍物的位置、速度及运动方向等实时信息，推导出一种新的具有实际含义的隶属函数作为避碰隶属函数，并通过对比隐含层节点数对网络相对误差的影响来确定隶属函数层节点数，构建五层T-S型模糊神经网络；在此基础上应用改进型误差反传学习算法，通过matlab模拟实验仿真验证及对比分析，表明了改进型网络在优化网络实时性、学习速度、收敛性、稳定性方面有良好的性能。%To optimize the fuzzy neural network real time,learning speed, convergence and stability, mobile robot local path planning improved fuzzy neural network model based on the actual membership function t-s type(Takagi-Sugeno)is presented.This method is to use multiple sensors(ultrasonic, camera) to collect the external environment information and optimize them, the robot coordinate transverse, longitudinal axis coordinates and direction were used as input variables, the robot move to the next step and direction speed were used as the output variable, this helps to robot local path planning implementation.This method is to make the gaussian function deformed as membership function,and by comparing the relative error of the node number of implicit layer of network to determine the membership function layer node number,to construct the five layers of the T-S type fuzzy neural network.On the basis of the improved error back propagation
2014-01-01
The purpose of this paper is to create an interval estimation of the fuzzy system reliability for the repairable multistate series–parallel system (RMSS). Two-sided fuzzy confidence interval for the fuzzy system reliability is constructed. The performance of fuzzy confidence interval is considered based on the coverage probability and the expected length. In order to obtain the fuzzy system reliability, the fuzzy sets theory is applied to the system reliability problem when dealing with uncertainties in the RMSS. The fuzzy number with a triangular membership function is used for constructing the fuzzy failure rate and the fuzzy repair rate in the fuzzy reliability for the RMSS. The result shows that the good interval estimator for the fuzzy confidence interval is the obtained coverage probabilities the expected confidence coefficient with the narrowest expected length. The model presented herein is an effective estimation method when the sample size is n ≥ 100. In addition, the optimal α-cut for the narrowest lower expected length and the narrowest upper expected length are considered. PMID:24987728
A New Fuzzy Set Theory Satisfying All Classical Set Formulas
Institute of Scientific and Technical Information of China (English)
Qing-Shi Gao; Xiao-Yu Gao; Yue Hu
2009-01-01
A new fuzzy set theory, C-fuzzy set theory, is introduced in this paper. It is a particular case of the classical set theory and satisfies all formulas of the classical set theory. To add a limitation to C-fuzzy set system, in which all fuzzy sets must be "non-uniform inclusive" to each other, then it forms a family of sub-systems, the Z-fuzzy set family. It can be proved that the Z0-fuzzy set system, one of Z-fuzzy set systems, is equivalent to Zadeh's fuzzy set system. Analysis shows that 1) Zadeh's fuzzy set system defines the relations A = B and A ∈B between two fuzzy sets A and B as "Vu e U,(u A E (u)=μB(U))" and "Au ∈ U, (μA(U) ≤μB(μ))" respectively is inappropriate, because it makes all fuzzy sets be "non-uniformly inclusive"; 2) it is also inappropriate to define two fuzzy sets' union and intersection operations as the max and rain of their grades of membership, because this prevents fuzzy set's ability to correctly reflect different kinds of fuzzy phenomenon in the natural world. Then it has to work around the problem by invent unnatural functions that are hard to understand, such as augmenting max and min for union and intersection to min{a + b, 1} and max{a + b - 1, 0}, but these functions are incorrect on inclusive case. If both pairs of definitions are used together, not only are they unnatural, but also they are still unable to cover all possible set relationships in the natural world; and 3) it is incorrect to define the set complement as 1 -μA(μ), because it can be proved that set complement cannot exist in Zadeh's fuzzy set, and it causes confusion in logic and thinking. And it is seriously mistaken to believe that logics of fuzzy sets necessarily go against classical and normal thinking, logic, and conception. The C-fuzzy set theory proposed in this paper overcomes all of the above errors and shortcomings, and more reasonably reflects fuzzy phenomenon in the natural world. It satisfies all relations, formulas, and operations of the
Fuzzy Expert System For The Selection Of Tourist Hotels
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GOPAL SINGH
2015-08-01
Full Text Available In the present work a simple and very effective mathematical model is designed for tourist hotels of LEVEL 2. Location of hotels building structure of hotels quality of hotels feedback of hotels and advertisement of hotels are as input factors. Trapezoidal membership function and triangular membership function are used for fuzzification process and defuzzification is done by COG technique. The fuzzy logic has been utilized in several different approaches to modeling the selection of tourist hotels process. This model addressed the hotel of LEVEL2 and this model concludes that the hotel is LEVEL 2 with degree of precision 52.15 .
QUADRATIC BI-LEVEL PROGRAMMING PROBLEM BASED ON FUZZY GOAL PROGRAMMING APPROACH
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Partha Pratim Dey
2011-11-01
Full Text Available This paper presents fuzzy goal programming approach to quadratic bi-level programming problem. Inthe model formulation of the problem, we construct the quadratic membership functions by determiningindividual best solutions of the quadratic objective functions subject to the system constraints. Thequadratic membership functions are then transformed into equivalent linear membership functions byfirst order Taylor series approximation at the individual best solution point. Since the objectives of upperand lower level decision makers are potentially conflicting in nature, a possible relaxation of each leveldecisions are considered by providing preference bounds on the decision variables for avoiding decisiondeadlock. Then fuzzy goal programming approach is used for achieving highest degree of each of themembership goals by minimizing deviational variables. Numerical examples are provided in order todemonstrate the efficiency of the proposed approach.
Medical application of fuzzy logic: fuzzy patient state in arterial hypertension analysis
Blinowska, Aleksandra; Duckstein, Lucien
1993-12-01
A few existing applications of fuzzy logic in medicine are briefly described and some potential applications are reviewed. The problem of classification of patient states and medical decision making is discussed more in detail and illustrated by the example of a fuzzy rule based model developed to elicit, analyze and reproduce the opinions of multiple medical experts in the case of arterial hypertension. The goal was to reproduce the average coded answers using an adequate fuzzy procedure, here a fuzzy rule. State categories and the initial set of experimental parameters were defined according to medical practice. The fuzzy set membership functions were then assessed for each parameter in each category and a small subset of representative and pertinent parameters selected for each question. The data were split into two sets of 50 patient files each, the calibration set and the validation set. Two evaluation criteria were used: the sum of squared deviations and the sum of deviations. Fuzzy rules were then sought that reproduced the target, which was the average coded answer. Only one fuzzy rule `and' appeared to be necessary to describe the patient state in a continuous way and to approach the target as closely as the majority of experts.
Directory of Open Access Journals (Sweden)
Novianto Dwi Prasongko
2016-01-01
Full Text Available Internet Service Provider (ISP is a company or business organization that provides access to intenet and services related for individual consumer or companies. There are many ISP in Indonesia recently, and they have almost the same product to offered. This problem makes internet service provider selection become a major issue. Decision support system can be used to recommend the best ISP company based on need. The aim of this research is to used Quality Function Deployment with Fuzzy TOPSIS sequentially to select the best ISP company as needed, and implemented in decision support system for internet service provider selection. Quality Function Deployment and Fuzzy TOPSIS methods used to evaluate, and then recommend the ISP company by ranked. Quality Function Deployment method used to find out customers requirements about internet network, the weighting of the criteria and the assessment of each ISP company. Fuzzy TOPSIS used to rank ISP company. These two methods produce consistent ratings when sensitivity analysis is performed for fuzzy and crisp value. These two methods make decision support system result can be trusted. Keywords : Quality Function Deployment; Fuzzy TOPSIS; Sensitivity Analysis
Fuzzy set applications in engineering optimization: Multilevel fuzzy optimization
Diaz, Alejandro R.
1989-01-01
A formulation for multilevel optimization with fuzzy objective functions is presented. With few exceptions, formulations for fuzzy optimization have dealt with a one-level problem in which the objective is the membership function of a fuzzy set formed by the fuzzy intersection of other sets. In the problem examined here, the goal set G is defined in a more general way, using an aggregation operator H that allows arbitrary combinations of set operations (union, intersection, addition) on the individual sets Gi. This is a straightforward extension of the standard form, but one that makes possible the modeling of interesting evaluation strategies. A second, more important departure from the standard form will be the construction of a multilevel problem analogous to the design decomposition problem in optimization. This arrangement facilitates the simulation of a system design process in which different components of the system are designed by different teams, and different levels of design detail become relevant at different time stages in the process: global design features early, local features later in the process.
CONSIDERING NEIGHBORHOOD INFORMATION IN IMAGE FUZZY CLUSTERING
Institute of Scientific and Technical Information of China (English)
Huang Ning; Zhu Minhui; Zhang Shourong
2002-01-01
Fuzzy C-means clustering algorithm is a classical non-supervised classification method.For image classification, fuzzy C-means clustering algorithm makes decisions on a pixel-by-pixel basis and does not take advantage of spatial information, regardless of the pixels' correlation. In this letter, a novel fuzzy C-means clustering algorithm is introduced, which is based on image's neighborhood system. During classification procedure, the novel algorithm regards all pixels'fuzzy membership as a random field. The neighboring pixels' fuzzy membership information is used for the algorithm's iteration procedure. As a result, the algorithm gives a more smooth classification result and cuts down the computation time.
Poverty Lines Based on Fuzzy Sets Theory and Its Application to Malaysian Data
Abdullah, Lazim
2011-01-01
Defining the poverty line has been acknowledged as being highly variable by the majority of published literature. Despite long discussions and successes, poverty line has a number of problems due to its arbitrary nature. This paper proposes three measurements of poverty lines using membership functions based on fuzzy set theory. The three…
Poverty Lines Based on Fuzzy Sets Theory and Its Application to Malaysian Data
Abdullah, Lazim
2011-01-01
Defining the poverty line has been acknowledged as being highly variable by the majority of published literature. Despite long discussions and successes, poverty line has a number of problems due to its arbitrary nature. This paper proposes three measurements of poverty lines using membership functions based on fuzzy set theory. The three…
Institute of Scientific and Technical Information of China (English)
荀志远; 王少华; 肖骏一
2013-01-01
This research is conducted for considering the physical depreciation more than functional depreciation as determining residential depreciation degree in China. Through investigation and analysis,the paper proposes the residential functional depreciation index system which includes security function,living function and environmental function three first indexes,fourteen second indexes. It applied the triangular fuzzy number to establish residential functional depreciation evaluation set quantization table and fuzzy comprehensive evaluation model to determine the functional depreciation degree and get a triangular fuzzy number. To analyze the result,it uses the triangular fuzzy number to transform the goal and each evaluation layer of triangular fuzzy number to membership function and the fuzzy integral theory to calculate overlap size of the goal fuzzy integral with each evaluation layer fuzzy integral in the x-axis by comparing the overlap size to determine the residential functional depreciation degree. An example is proved this method feasible.% 针对我国住宅折旧度确定时考虑实体折旧较多，考虑功能折旧较少问题，通过调查分析提出了住宅的功能折旧评价指标体系，该体系包括安全功能、居住功能和环境功能3个一级指标，14个二级指标。利用三角模糊数方法建立了住宅功能折旧评语集量化表，运用模糊综合评价模型对住宅功能折旧程度进行评价，评价结果为目标三角模糊数。运用三角模糊数理论确定该目标住宅的功能折旧程度。通过实例分析证明该方法具有可行性。
Design and Simulation of Dynamic Voltage Restorer based on Fuzzy Controller Optimized by ANFIS
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Brahim Ferdi
2014-03-01
Full Text Available The fuzzy logic controller (FLC appears to be the unique solution when the process is too complex for analysis by conventional techniques or when the available information data are interpreted qualitatively, inexactly or with uncertainty. In literature, the proposed FLC in general consists of two inputs (error and derivative of error and one output. The number of membership functions is chosen in most cases to be five or seven regardless of the approach used for the design. In this paper, we propose Adaptive Neuro-Fuzzy Inference System (ANFIS approach to optimize the two inputs one output FLC with seven membership functions to one input one output FLC with three membership functions without compromising accuracy. The study is applied to control a Dynamic Voltage Restorer (DVR in voltage sag/swell mitigation. The results of simulation using MATLAB/SIMULINK show that the performance of the optimal FLC generated by ANFIS is comparable with the initial given FLC.
Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation.
Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi
2016-12-16
Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection
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Dalton Meitei Thounaojam
2016-01-01
Full Text Available This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.
A fuzzy approach to the generation expansion planning problem in a multi-objective environment
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Abass Samir A.
2007-01-01
Full Text Available In many power system problems, the use of optimization techniques has proved inductive to reducing the costs and losses of the system. A fuzzy multi-objective decision is used for solving power system problems. One of the most important issues in the field of power system engineering is the generation expansion planning problem. In this paper, we use the concepts of membership functions to define a fuzzy decision model for generating an optimal solution for this problem. Solutions obtained by the fuzzy decision theory are always efficient and constitute the best compromise. .
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; Roy, Sudipta
2016-01-01
This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.
Research and Implementation of Automatic Fuzzy Garage Parking System Based on FPGA
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Wang Kaiyu
2016-01-01
Full Text Available Because of many common scenes of reverse parking in real life, this paper presents a fuzzy controller which accommodates front and back adjustment of vehicle’s body attitude, and based on chaotic-genetic arithmetic to optimize the membership function of this controller, and get a vertical parking fuzzy controller whose simulation result is good .The paper makes the hardware-software embedded design for system based on Field-Programmable Gate Array (FPGA, and set up a 1:10 verification platform of smart car to verify the fuzzy garage parking system with real car. Verification results show that, the system can complete the parking task very well.
Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control
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Allaoua Boumediene
2008-01-01
Full Text Available This paper presents the application of Fuzzy Logic for DC motor speed control using Particle Swarm Optimization (PSO. Firstly, the controller designed according to Fuzzy Logic rules is such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC the designed FLC-PSO speed controller obtains better dynamic behavior and superior performance of the DC motor, as well as perfect speed tracking with no overshoot.
Simulation of worms transmission in computer network based on SIRS fuzzy epidemic model
Darti, I.; Suryanto, A.; Yustianingsih, M.
2015-03-01
In this paper we study numerically the behavior of worms transmission in a computer network. The model of worms transmission is derived by modifying a SIRS epidemic model. In this case, we consider that the transmission rate, recovery rate and rate of susceptible after recovery follows fuzzy membership functions, rather than constants. To study the transmission of worms in a computer network, we solve the model using the fourth order Runge-Kutta method. Our numerical results show that the fuzzy transmission rate and fuzzy recovery rate may lead to a changing of basic reproduction number which therefore also changes the stability properties of equilibrium points.
Chen, Liang; Tokuda, N
2002-01-01
By exploiting the Fourier series expansion, we have developed a new constructive method of automatically generating a multivariable fuzzy inference system from any given sample set with the resulting multivariable function being constructed within any specified precision to the original sample set. The given sample sets are first decomposed into a cluster of simpler sample sets such that a single input fuzzy system is constructed readily for a sample set extracted directly from the cluster independent of the other variables. Once the relevant fuzzy rules and membership functions are constructed for each of the variables completely independent of the other variables, the resulting decomposed fuzzy rules and membership functions are integrated back into the fuzzy system appropriate for the original sample set requiring only a moderate cost of computation in the required decomposition and composition processes. After proving two basic theorems which we need to ensure the validity of the decomposition and composition processes of the system construction, we have demonstrated a constructive algorithm of a multivariable fuzzy system. Exploiting an implicit error bound analysis available at each of the construction steps, the present Fourier method is capable of implementing a more stable fuzzy system than the power series expansion method of ParNeuFuz and PolyNeuFuz, covering and implementing a wider range of more robust applications.
Fuzzy Logic Connectivity in Semiconductor Defect Clustering
Energy Technology Data Exchange (ETDEWEB)
Gleason, S.S.; Kamowski, T.P.; Tobin, K.W.
1999-01-24
In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re-evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer (SSA) software, a joint development project between Oak Ridge National Lab (ORNL) and SEMATECH.
Fuzzy Logic Connectivity in Semiconductor Defect Clustering
Energy Technology Data Exchange (ETDEWEB)
Gleason, S.S.; Kamowski, T.P.; Tobin, K.W.
1999-01-24
In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re-evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer (SSA) software, a joint development project between Oak Ridge National Lab (ORNL) and SEMATECH.
Train repathing in emergencies based on fuzzy linear programming.
Meng, Xuelei; Cui, Bingmou
2014-01-01
Train pathing is a typical problem which is to assign the train trips on the sets of rail segments, such as rail tracks and links. This paper focuses on the train pathing problem, determining the paths of the train trips in emergencies. We analyze the influencing factors of train pathing, such as transferring cost, running cost, and social adverse effect cost. With the overall consideration of the segment and station capability constraints, we build the fuzzy linear programming model to solve the train pathing problem. We design the fuzzy membership function to describe the fuzzy coefficients. Furthermore, the contraction-expansion factors are introduced to contract or expand the value ranges of the fuzzy coefficients, coping with the uncertainty of the value range of the fuzzy coefficients. We propose a method based on triangular fuzzy coefficient and transfer the train pathing (fuzzy linear programming model) to a determinate linear model to solve the fuzzy linear programming problem. An emergency is supposed based on the real data of the Beijing-Shanghai Railway. The model in this paper was solved and the computation results prove the availability of the model and efficiency of the algorithm.
Directory of Open Access Journals (Sweden)
Renna Magdalena
2012-09-01
Full Text Available Supplier selection is an important part of supply chain management process by which firms identify, evaluate, and establish contracts with suppliers. Deciding the right supplier can be a complex task. As such, various criteria must be taken into account to choose the best supplier. This study focused on the supply in the packaging division of a food industry in Denpasar-Bali. A combination of Taguchi Loss Function and fuzzy-AHP (Analytical Hierarchy Process Fuzzy Linear Programming was used to determine the best supplier. In this analysis, several suppliers’ criteria were considered, namely quality, delivery, completeness, quality loss and environmental management. By maximizing the suppliers’ performances based on each criterion and aggregating the suppliers’ performances based on the overall criteria, the best supplier was determined. Keywords: supplier selection, taguchi loss function, AHP, fuzzy linear programming,environment
Genetic Fuzzy Prediction of Mass Perception in Non-Functional 3D Shapes
DEFF Research Database (Denmark)
Achiche, Sofiane
2010-01-01
and it is argued that human attributes originate from three different levels of the brain: the visceral level; the behavioral level and the reflective level. This paper focuses upon the visceral level of reaction by automatically building a link between geometric properties of non-functional 3D shapes...... and their perception by observers. The link between geometry and human perception is created using a genetic learning algorithm combined with a fuzzy logic decision support system. Human evaluations of the non-functional 3D shapes against two contrary perception adjectives (massive versus lightweight) are used...... by the author and five (5) genetically generated. The fuzzy models were constructed using different sets of inputs of quantitative geometric properties. Combination of the different inputs resulted in different sets of fuzzy rules that can eventually be used as design guidelines for designers. The results...
DEFF Research Database (Denmark)
Anker, Thomas Boysen; Kappel, Klemens; Eadie, Douglas
2012-01-01
This article clarifies the commonplace assumption that brands make promises by developing definitions of brand promise delivery. Distinguishing between clear and fuzzy brand promises, we develop definitions of what it is for a brand to deliver on fuzzy functional, symbolic, and experiential...
Application of an iterative method and an evolutionary algorithm in fuzzy optimization
Directory of Open Access Journals (Sweden)
Ricardo Coelho Silva
2012-08-01
Full Text Available This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain.
Stability Analysis of Continuous-Time Fuzzy Large-Scale System
Institute of Scientific and Technical Information of China (English)
曾怡达; 张友刚; 肖建
2003-01-01
A continuous-time fuzzy large-scale system F consists of some interconnected Takagi-Sugeno fuzzy subsystems. Two sufficient conditions for the asymptotic stability of this system (namely, theorem 1 and theorem 2) are derived via a multiple Lyapunov function approach. In theorem 1, the information of membership functions of fuzzy rules should be known in order to analyze the stability of F. But in general this information is not easy to be acquired for their time-varying property. So theorem 2 is provided to judge the asymptotic stability of F, based on which there is no need to know the information of membership functions in stability analysis. Finally, a numerical example is given to show the utility of the method proposed in this paper.
Application Research of Fuzzy Theory in PE Teaching Evaluation
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Chen Ying
2013-09-01
Full Text Available Comprehensive evaluation of PE teaching is always one the of difficulties of teaching management for universities; This paper, on the basis of analyzing the fuzzy features of teaching evaluation, puts forward fuzzy evaluation model of PE teaching. The model first discards the defects of traditional evaluation methods which always neglect the specific characteristics of PE teaching, instead, takes teaching objectives and results as orientation, designs new comprehensive evaluation indicators for PE teaching; Second, analytic hierarchy process and multivariate fuzzy evaluation method are used to build the evaluation model for PE teaching through building membership function and comprehensive evaluation matrix of fuzzy comprehensive evaluation; Finally, the model is realized by the data from three universities to carry out comprehensive evaluation on PE course and the experimental results indicate that the presented model has satisfied application results in evaluation accuracy and time consumption compared with traditional methods.
Study on the Fuzzy COntrol Strategy of Automobile with CVT
Institute of Scientific and Technical Information of China (English)
HuJianjun; QINDatong; 等
2002-01-01
In order to study the dynamic characteristics of automobile with a CVT system, a bond graph analysis model of continuously variable transmission is established.On the base of the simulation state space equations that are established with bond graph theory,a fuzzy control strategy with an expert system of starting process has been introduced.Considering uncertain system parameters and exterior resistance disturbing,the effect of the profile of membership function and the defuzzification algorthm on the capacity of the fuzzy controller has been studied.The result of simulation proves that the proposed fuzzy controller is effective and feasible,Such controller has been employed in the actual control and has proved practicable.The study lays a foundation for design of the fuzzy controller for automobile with a CVT system.
Adaptive Fuzzy-Lyapunov Controller Using Biologically Inspired Swarm Intelligence
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Alejandro Carrasco Elizalde
2008-01-01
Full Text Available The collective behaviour of swarms produces smarter actions than those achieved by a single individual. Colonies of ants, flocks of birds and fish schools are examples of swarms interacting with their environment to achieve a common goal. This cooperative biological intelligence is the inspiration for an adaptive fuzzy controller developed in this paper. Swarm intelligence is used to adjust the parameters of the membership functions used in the adaptive fuzzy controller. The rules of the controller are designed using a computing-with-words approach called Fuzzy-Lyapunov synthesis to improve the stability and robustness of an adaptive fuzzy controller. Computing-with-words provides a powerful tool to manipulate numbers and symbols, like words in a natural language.
Attribute Control Chart Construction Based on Fuzzy Score Number
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Shiwang Hou
2016-11-01
Full Text Available There is much uncertainty and fuzziness in product quality attributes or quality parameters of a manufacturing process, so the traditional quality control chart can be difficult to apply. This paper proposes a fuzzy control chart. The plotted data was obtained by transforming expert scores into fuzzy numbers. Two types of nonconformity judgment rules—necessity and possibility measurement rules—are proposed. Through graphical analysis, the nonconformity judging method (i.e., assessing directly based on the shape feature of a fuzzy control chart is proposed. For four different widely used membership functions, control levels were analyzed and compared by observing gaps between the upper and lower control limits. The result of the case study validates the feasibility and reliability of the proposed approach.
Automatic control of biomass gasifiers using fuzzy inference systems
Energy Technology Data Exchange (ETDEWEB)
Sagues, C. [Universidad de Zaragoza (Spain). Dpto. de Informatica e Ingenieria de Sistemas; Garcia-Bacaicoa, P.; Serrano, S. [Universidad de Zaragoza (Spain). Dpto. de Ingenieria Quimica y Medio Ambiente
2007-03-15
A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated. (author)
Automatic control of biomass gasifiers using fuzzy inference systems.
Sagüés, C; García-Bacaicoa, P; Serrano, S
2007-03-01
A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated.
Contrast enhancement of fingerprint images using intuitionistic type II fuzzy set
Directory of Open Access Journals (Sweden)
Devarasan Ezhilmaran
2015-04-01
Full Text Available A novel contrast image enhancement of fingerprint images using intuitionistic type II fuzzy set theory is recommended in this work. The method of Hamacher T co-norm(S norm which generates a new membership function with the help of upper and lower membership function of type II fuzzy set. The finger print identification is one of the very few techniques employed in forensic science to aid criminal investigations in daily life, providing access control in financial security;-, visa related services, as well as others. Mostly fingerprint images are poorly illuminated and hardly visible, so it is necessary to enhance the input images. The enhancement is useful for authentication and matching. The fingerprint enhancement is vital for identifying and authenticating people by matching their fingerprints with the stored one in the database. The proposed enhancement of the intuitionistic type II fuzzy set theory results showed that it is more effective, especially, very useful for forensic science operations. The experimental results were compared with non-fuzzy, fuzzy, intuitionistic fuzzy and type II fuzzy methods in which the proposed method offered better results with good quality, less noise and low blur features.
Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms
Siddique, Nazmul
2014-01-01
Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller. The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...
Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms.
Liu, B D; Chen, C Y; Tsao, J Y
2001-01-01
In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design.
Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai
2016-01-01
Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).
Directory of Open Access Journals (Sweden)
Eka Prasetyono
2015-09-01
Full Text Available Bidirectional DC-DC converter is needed in the energy storage system. The converter topology used in this paper was a non-isolated bidirectional DC-DC buck-boost converter. This converter worked in two ways, which the charging mode stored energy into battery when load current was less than nominal main DC current (set point and discharging mode transferred energy from battery to the load when its current exceeded set point value. Both of these modes worked automatically according to the load current. The charging and discharging currents were controlled by fuzzy logic controller which was implemented on microcontroller ARM Cortex-M4F STM32F407VG. This paper compares two types of fuzzy membership function (triangular and sigmoid in controlling bidirectional DC-DC converter. The results showed that fuzzy logic controller with triangle membership function and sigmoid as control bidirectional DC-DC converter had no significant different response, both had an average error for charging and discharging process under 4% with ripple current on the main DC bus around 0.5%. The computing time of program for fuzzy logic controller with triangular membership functions had 19.01% faster than sigmoid, and fuzzy logic computation time on a microcontroller with hardware floating point was 60% faster than software floating point.
Institute of Scientific and Technical Information of China (English)
刘浪; 陈忠强; 王李管
2015-01-01
Rock bursts are spontaneous, violent fracture of rock that can occur in deep mines, and the likelihood of rock bursts occurring increases as depth of the mine increases. Rock bursts are also affected by the compressive strength, tensile strength, tangential strength, elastic energy index, etc. of rock, and the relationship between these factors and rock bursts in deep mines is difficult to analyze from quantitative point. Typical rock burst instances as a sample set were collected, and membership function was introduced to process the discrete values of these factors with the discrete factors as condition attributes and rock burst situations as decision attributes. Dominance-based rough set theory was used to generate preference rules of rock burst, and eventually rock burst laws analysis in deep mines with preference relation was taken. The results show that this model for rock burst laws analysis in deep mines is more reasonable and feasible, and the prediction results are more scientific.
Anaesthesia monitoring using fuzzy logic.
Baig, Mirza Mansoor; Gholamhosseini, Hamid; Kouzani, Abbas; Harrison, Michael J
2011-10-01
Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration. The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of off-line tests. The training and testing data set are selected randomly from 30 sets of patients' data. The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures. The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.
Estimating the crowding level with a neuro-fuzzy classifier
Boninsegna, Massimo; Coianiz, Tarcisio; Trentin, Edmondo
1997-07-01
This paper introduces a neuro-fuzzy system for the estimation of the crowding level in a scene. Monitoring the number of people present in a given indoor environment is a requirement in a variety of surveillance applications. In the present work, crowding has to be estimated from the image processing of visual scenes collected via a TV camera. A suitable preprocessing of the images, along with an ad hoc feature extraction process, is discussed. Estimation of the crowding level in the feature space is described in terms of a fuzzy decision rule, which relies on the membership of input patterns to a set of partially overlapping crowding classes, comprehensive of doubt classifications and outliers. A society of neural networks, either multilayer perceptrons or hyper radial basis functions, is trained to model individual class-membership functions. Integration of the neural nets within the fuzzy decision rule results in an overall neuro-fuzzy classifier. Important topics concerning the generalization ability, the robustness, the adaptivity and the performance evaluation of the system are explored. Experiments with real-world data were accomplished, comparing the present approach with statistical pattern recognition techniques, namely linear discriminant analysis and nearest neighbor. Experimental results validate the neuro-fuzzy approach to a large extent. The system is currently working successfully as a part of a monitoring system in the Dinegro underground station in Genoa, Italy.
Fuzzy neural network based on a Sigmoid chaotic neuron
Institute of Scientific and Technical Information of China (English)
Zhang Yi; Wang Xing-Yuan
2012-01-01
The theories of intelligent information processing are urgently needed for the rapid development of modem science.In this paper,a novel fuzzy chaotic neural network,which is the combination of fuzzy logic system,artificial neuralnetwork system,and chaotic system,is proposed.We design its model structure which is based on the Sigmoid map,derive its mathematical model,and analyse its chaotic characteristics.Finally the relationship between the accuracy of map and the membership function is illustrated by simulation.
EFFICIENT SUBSPACE CLUSTERING FOR HIGHER DIMENSIONAL DATA USING FUZZY ENTROPY
Institute of Scientific and Technical Information of China (English)
C.PALANISAMY; S.SELVAN
2009-01-01
In this paper we propose a novel method for identifying relevant subspaces using fuzzy entropy and perform clustering. This measure discriminates the real distribution better by using membership functions for measuring class match degrees. Hence the fuzzy entropy reflects more information in the actual disbution of patterns in the subspaces. We use a heuristic procedure based on the silhouette criterion to find the number of clusters. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets. Empirical results have shown its favorable performance in comparison with several other clustering algorithms.
Body Fuzzy Pattern Recognition in Woman Basic Block Design
Institute of Scientific and Technical Information of China (English)
谢红; 张渭源
2003-01-01
Basic block is the foundation of clothing construction design because it is the media between body and clothes and the fitness of clothes should be based on the accuracy of basic block. That needs us to recognize body not to record it. This paper reports the Algorithm of woman body fuzzy pattern recognition. It is organized in three sections:(i) extracting woman body feature; (ii) establishing membership functions of feature indexes;(iii) presenting an Algorithm for woman body fuzzy pattern recognition by example.
Fuzzy φψ-continuous Functions between L-topological Spaces (I)
Institute of Scientific and Technical Information of China (English)
Bayaz Daraby
2009-01-01
In the present paper, φψ-continuous function on L-topological spaces and productive operation are defined. By means of this operation,we study fuzzy φψ-continuity from L-product spaces into L-product spaces and also from L-topological spaces into L-product spaces.
Moment Method Based on Fuzzy Reliability Sensitivity Analysis for a Degradable Structural System
Institute of Scientific and Technical Information of China (English)
Song Jun; Lu Zhenzhou
2008-01-01
For a degradable structural system with fuzzy failure region, a moment method based on fuzzy reliability sensitivity algorithm is presented. According to the value assignment of porformance function, the integral region for calculating the fuzzy failure probability is first split into a series of subregions in which the membership function values of the performance function within the fuzzy failure region can be approximated by a set of constants. The fuzzy failure probability is then transformed into a sum of products oftbe random failure probabilities and the approximate constants of the membership function in the subregions. Furthermore, the fuzzy reliability sensitivity analysis is transformed into a series of random reliability sensitivity analysis, and the random reliability sensitivity can be obtained by the constructed moment method. The primary advantages of the presented method include higher efficiency for implicit performance function with low and medium dimensionality and wide applicability to multiple failure modes and nonnormal basic random variables. The limitation is that the required computation effort grows exponentially with the increase of dimensionality of the basic random vari-able; hence, it is not suitable for high dimensionality problem. Compared with the available methods, the presented one is pretty com-petitive in the case that the dimensionality is lower than 10. The presented examples are used to verify the advantages and indicate the limitations.
Kosko, Bart
1991-01-01
Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.
Countable Fuzzy Topological Space and Countable Fuzzy Topological Vector Space
Directory of Open Access Journals (Sweden)
Apu Kumar Saha
2015-06-01
Full Text Available This paper deals with countable fuzzy topological spaces, a generalization of the notion of fuzzy topological spaces. A collection of fuzzy sets F on a universe X forms a countable fuzzy topology if in the definition of a fuzzy topology, the condition of arbitrary supremum is relaxed to countable supremum. In this generalized fuzzy structure, the continuity of fuzzy functions and some other related properties are studied. Also the class of countable fuzzy topological vector spaces as a generalization of the class of fuzzy topological vector spaces has been introduced and investigated.
Assessment of the Degree of Consistency of the System of Fuzzy Rules
Directory of Open Access Journals (Sweden)
Pospelova Lyudmila Yakovlevna
2013-12-01
Full Text Available The article analyses recent achievements and publications and shows that difficulties of explaining the nature of fuzziness and equivocation arise in socio-economic models that use the traditional paradigm of classical rationalism (computational, agent and econometric models. The accumulated collective experience of development of optimal models confirms prospectiveness of application of the fuzzy set approach in modelling the society. The article justifies the necessity of study of the nature of inconsistency in fuzzy knowledge bases both on the generalised ontology level and on pragmatic functional level of the logical inference. The article offers the method of search for logical and conceptual contradictions in the form of a combination of the abduction and modus ponens. It discusses the key issue of the proposed method: what properties should have the membership function of the secondary fuzzy set, which describes in fuzzy inference models such a resulting state of the object of management, which combines empirically incompatible properties with high probability. The degree of membership of the object of management in several incompatible classes with respect to the fuzzy output variable is the degree of fuzziness of the “Intersection of all results of the fuzzy inference of the set, applied at some input of rules, is an empty set” statement. The article describes an algorithm of assessment of the degree of consistency. It provides an example of the step-by-step detection of contradictions in statistical fuzzy knowledge bases at the pragmatic functional level of the logical output. The obtained results of testing in the form of sets of incompatible facts, output chains, sets of non-crossing intervals and computed degrees of inconsistency allow experts timely elimination of inadmissible contradictions and, at the same time, increase of quality of recommendations and assessment of fuzzy expert systems.
Directory of Open Access Journals (Sweden)
Nour-Eddine El Harchaoui
2013-01-01
Full Text Available The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM to initialize the parameters of possibilistic c-means (PCM, in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.
Fuzzy-Rule-Based Approach for Modeling Sensory Acceptabitity of Food Products
Directory of Open Access Journals (Sweden)
Olusegun Folorunso
2009-04-01
Full Text Available The prediction of product acceptability is often an additive effect of individual fuzzy impressions developed by a consumer on certain underlying attributes characteristic of the product. In this paper, we present the development of a data-driven fuzzy-rule-based approach for predicting the overall sensory acceptability of food products, in this case composite cassava-wheat bread. The model was formulated using the Takagi-Sugeno and Kang (TSK fuzzy modeling approach. Experiments with the model derived from sampled data were simulated on Windows 2000XP running on Intel 2Gh environment. The fuzzy membership function for the sensory scores is implemented in MATLAB 6.0 using the fuzzy logic toolkit, and weights of each linguistic attribute were obtained using a Correlation Coefficient formula. The results obtained are compared to those of human judgments. Overall assessments suggest that, if implemented, this approach will facilitate a better acceptability of cassava bread as well as nutritionally improved food.
An Interval-Valued Intuitionistic Fuzzy TOPSIS Method Based on an Improved Score Function
Directory of Open Access Journals (Sweden)
Zhi-yong Bai
2013-01-01
Full Text Available This paper proposes an improved score function for the effective ranking order of interval-valued intuitionistic fuzzy sets (IVIFSs and an interval-valued intuitionistic fuzzy TOPSIS method based on the score function to solve multicriteria decision-making problems in which all the preference information provided by decision-makers is expressed as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by IVIFS value and the information about criterion weights is known. We apply the proposed score function to calculate the separation measures of each alternative from the positive and negative ideal solutions to determine the relative closeness coefficients. According to the values of the closeness coefficients, the alternatives can be ranked and the most desirable one(s can be selected in the decision-making process. Finally, two illustrative examples for multicriteria fuzzy decision-making problems of alternatives are used as a demonstration of the applications and the effectiveness of the proposed decision-making method.
Institute of Scientific and Technical Information of China (English)
朱坚民; 雷静桃; 翟东婷; 黄之文
2012-01-01
Aiming at the problem that existing fruit recognition methods either need a lot of fruit samples to train or only use a single feature to do identification, which leads to low recognition rate, a feature parameter extraction method of fruit color and shape based on fruit image processing is proposed, and an automatic recognition method of spherical fruit based on grey relational analysis and fuzzy membership degree matching is proposed. The method extracts the fruit image color and shape features in the region of interest ( ROI) , establishes the fruit color feature reference database and shape feature membership functions, calculates the grey weighted relational grade of the color features between the fruit to be identified and reference fruits, and calculates the fuzzy membership degree of shape feature parameter of the fruit to be identified relative to the reference fruits. The method synthesizes the total matching degree of the fruit to be identified relative to reference fruits according to the principle that all features are equal weight, and realizes the recognition of the fruits to be identified based on the total matching degree. Mass experiment results show that the proposed method is simple and effective; it doesn't need large fruit samples for learning and training, and the average recognition accuracy reaches above 99% .%针对现有水果识别方法需大量水果样本学习或仅对单一特征进行识别而导致的识别率较低的问题,提出一种基于水果图像处理的水果颜色和形状特征参数的提取方法、基于灰色关联分析和模糊隶属度匹配的球形水果自动识别方法.该方法通过提取水果图像关注区域(region of interest,ROI)的颜色和形状特征,建立参比水果的颜色特征参比数据库和形状特征隶属度函数,计算待识别水果与参比水果颜色特征的灰色加权关联度,求取待识别水果对于参比水果形状特征参数的模糊隶属度,按各特征量等
循环荷载作用下基于隶属度函数的边界面模型%Bounding surface model based on membership function under cyclic loading
Institute of Scientific and Technical Information of China (English)
王喜刚; 扶名福; 胡小荣
2015-01-01
A membership function was imported into the bounding surface model to solve accumulated and continu-ous plastic strain under cyclic loading.A new three-dimensional cone was constructed based on the two-dimensional critical soil model figure.The relationship between the membership function and the loading surface was established based on the new three-dimensional cone.The membership function was used to modify the plastic flow criteria, so that the maximum stress of model was partially memorized;the damage parameters were adopted to deduce a fuzzy bounding surface model.A dynamic triaxial experiment was adopted to determine the parameters of the model.The numerical prediction matches the experiment results, proving the model is reasonable.%为了反映土体在循环荷载作用下塑性应变的累积特性和连续性，将隶属度函数引入到边界面模型中，在二维临界土力学模型图基础上，构造了一个新三维空间锥体，利用该锥体建立了加载面与隶属度函数的一一对应关系。根据隶属度函数修正了塑性流动法则，使得模型对最大预应力具有部分记忆功能，并引入损伤参数，推导得到了模糊边界面模型，通过动三轴实验确定了该模型的参数。利用有限元的二次开发功能，将该模型引入到有限元中，得到了模型的数值结果，通过与动三轴实验结果对比，发现两者吻合较好，证明了模型的合理性。
Fuzzy neural order robust of the non-linear systems
Madour, F.; Benmahammed, K.
2008-06-01
This article introduces a controller at structure of a network multi-layer neurons specified by the fuzzy reasoning of Takagi-Sugeno (TS) order one [1], the weights of the network represent the standard deviations of the membership function. This controller is applied to the ordering of a reversed pendulum. Changes in the entries and the exit, as of the environment changes of operation are introduced in order to test the robustness of the designed controller.
Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems
Indian Academy of Sciences (India)
Abdurahman Kadir; Xing-Yuan Wang; Yu-Zhang Zhao
2011-06-01
In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identiﬁer (FNNI) is the principal controller. The FNNI is used for online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a speciﬁc example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.
Directory of Open Access Journals (Sweden)
Yanbing Ju
2014-01-01
Full Text Available This paper studies the multiattribute decision making (MADM problems in which the attribute values take the form of dual hesitant fuzzy triangular linguistic elements and the weights of attributes take the form of real numbers. Firstly, to solve the situation where the membership degree and the nonmembership degree of an element to a triangular linguistic variable, the concept, operational laws, score function, and accuracy function of dual hesitant fuzzy triangular linguistic elements (DHFTLEs are defined. Then, some dual hesitant fuzzy triangular linguistic geometric aggregation operators are developed for aggregating the DHFTLEs, including dual hesitant fuzzy triangular linguistic weighted geometric (DHFTLWG operator, dual hesitant fuzzy triangular linguistic ordered weighted geometric (DHFTLOWG operator, dual hesitant fuzzy triangular linguistic hybrid geometric (DHFTLHG operator, generalized dual hesitant fuzzy triangular linguistic weighted geometric (GDHFTLWG operator, and generalized dual hesitant fuzzy triangular linguistic ordered weighted geometric (GDHFTLOWG operator. Furthermore, some desirable properties of these operators are investigated in detail. Based on the proposed operators, an approach to MADM with dual hesitant fuzzy triangular linguistic information is proposed. Finally, a numerical example for investment alternative selection is given to illustrate the application of the proposed method.
On the fusion of tuning parameters of fuzzy rules and neural network
Mamuda, Mamman; Sathasivam, Saratha
2017-08-01
Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.
An improved method for determining the membership of fuzzy SVM%一种改进的模糊支持向量机隶属度确定方法
Institute of Scientific and Technical Information of China (English)
刘成忠; 韩虎; 黄高宝
2011-01-01
为了克服支持向量机方法对于噪声或异常样本敏感的问题,本文研究基于粗糙集理论的粗糙单类支持向量机,提出一种改进的模糊支持向量机隶属度确定方法.该算法首先利用粗糙集思想构造一个最小粗糙球,分别得到对应粗糙球的上近似、下近似与边界区域,然后依据样本在超球中的位置对分布在下近似、边界域和粗糙球以外的样本,分别采用三种不同的方式计算其各自的隶属度.最后通过对比实验表明,与传统支持向量机方法相比,本文提出的改进方法使模糊支持向量机具有更好的抗噪性能及分类能力.%In order to overcome the problem that support vector machine is sensitive to noises and outliers, rough one-class support vector machine based on rough set is researched and an improved method for determining the membership of fuzzy support vector machine is proposed in the paper.At first, the smallest rough sphere which encloses the data points is constructed.And then, those data points are divided into upper approximation region, low approximation region and boundary region by the distance between data points and the center of the rough sphere.At last, several comparative experiments using synthetic and real life data set show the performance and the validity of the method.
Energy Technology Data Exchange (ETDEWEB)
Velez D, D
2000-07-01
This work presents a fuzzy logic controller design for neutron power control, from its source to its full power level, applied to a nuclear reactor model. First, we present the basic definitions on fuzzy sets as generalized definitions of the crisp (non fuzzy) set theory. Likewise, we define the basic operations on fuzzy sets (complement, union, and intersection), and the operations on fuzzy relations such as projection and cylindrical extension operations. Furthermore, some concepts of the fuzzy control theory, such as the main modules of the typical fuzzy controller structure and its internal variables, are defined. After the knowledge base is obtained by simulation of the reactor behavior, where the controlled system is modeled by a simple nonlinear reactor model, this model is used to infer a set of fuzzy rules for the reactor response to different insertions of reactivity. The reduction of the response time, using fuzzy rule based controllers on this reactor, is possible by adjusting the output membership functions, by selecting fuzzy rule sets, or by increasing the number of crisp inputs to the fuzzy controller. System characteristics, such as number of rules, response times, and safety parameter values, were considered in the evaluation of each controller merits. Different fuzzy controllers are designed to attain the desired power level, to maintain a constant level for long periods of time, and to keep the reactor away from a shutdown condition. The basic differences among the controllers are the number of crisp inputs and the novel implementation of a crisp power level-based selection of different sets of output membership functions. Simulation results highlight, mainly: (1) A decrease of the response variations at low power level, and (2) a decrease in the time required to attain the desired neutron power. Finally, we present a comparative study of different fuzzy control algorithms applied to a nuclear model. (Author)
Fuzzy unit commitment solution - A novel twofold simulated annealing approach
Energy Technology Data Exchange (ETDEWEB)
Saber, Ahmed Yousuf; Senjyu, Tomonobu; Yona, Atsushi; Urasaki, Naomitsu [Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho Nakagami, Okinawa 903-0213 (Japan); Funabashi, Toshihisa [Meidensha Corporation, Riverside Building 36-2, Tokyo 103-8515 (Japan)
2007-10-15
The authors propose a twofold simulated annealing (twofold-SA) method for the optimization of fuzzy unit commitment formulation in this paper. In the proposed method, simulated annealing (SA) and fuzzy logic are combined to obtain SA acceptance probabilities from fuzzy membership degrees. Fuzzy load is calculated from error statistics and an initial solution is generated by a priority list method. The initial solution is decomposed into hourly-schedules and each hourly-schedule is modified by decomposed-SA using a bit flipping operator. Fuzzy membership degrees are the selection attributes of the decomposed-SA. A new solution consists of these hourly-schedules of entire scheduling period after repair, as unit-wise constraints may not be fulfilled at the time of an individual hourly-schedule modification. This helps to detect and modify promising schedules of appropriate hours. In coupling-SA, this new solution is accepted for the next iteration if its cost is less than that of current solution. However, a higher cost new solution is accepted with the temperature dependent total cost membership function. Computation time of the proposed method is also improved by the imprecise tolerance of the fuzzy model. Besides, excess units with the system dependent probability distribution help to handle constraints efficiently and imprecise economic load dispatch (ELD) calculations are modified to save the execution time. The proposed method is tested using standard reported data sets. Numerical results show an improvement in solution cost and time compared to the results obtained from other existing methods. (author)
Membership Satisfaction and the Cost of Membership
DEFF Research Database (Denmark)
Eskildsen, Jacob Kjær; Kristensen, Kai
2011-01-01
in membership satisfaction. Furthermore the cost of administration per member and membership satisfaction is found to be able to explain differences in membership loyalty when the 29 unemployment insurance funds are compared. Finally administration costs per member are found to be dependent on the number...
Functional Based Adaptive and Fuzzy Sliding Controller for Non-Autonomous Active Suspension System
Huang, Shiuh-Jer; Chen, Hung-Yi
In this paper, an adaptive sliding controller is developed for controlling a vehicle active suspension system. The functional approximation technique is employed to substitute the unknown non-autonomous functions of the suspension system and release the model-based requirement of sliding mode control algorithm. In order to improve the control performance and reduce the implementation problem, a fuzzy strategy with online learning ability is added to compensate the functional approximation error. The update laws of the functional approximation coefficients and the fuzzy tuning parameters are derived from the Lyapunov theorem to guarantee the system stability. The proposed controller is implemented on a quarter-car hydraulic actuating active suspension system test-rig. The experimental results show that the proposed controller suppresses the oscillation amplitude of the suspension system effectively.
Evaluation of E-Commerce Website Functionality Using a Mamdani Fuzzy System
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L. Al-Qaisi
2015-10-01
Full Text Available The majority of leader companies are running their businesses using online E-commerce websites. These E-commerce websites are becoming significant revenue drivers and major retailers. Hence, it is critical to evaluate the functionality of these websites which are expected to support growing business needs. The evaluation of the functionality of E-commerce websites is not a straightforward process due to the many constraints and standards that should be considered. Fuzzy logic is a powerful technique used in modeling impreciseness and uncertainties. This paper proposes a Mamdani fuzzy system that evaluates the functionality of E-commerce websites over different parameters: accuracy, flexibility, client support, and availability of product information. Experimental results provide positive relations between accuracy and flexibility on the functionality of E-commerce websites.
SISTEM PENGEMBANGAN KENDALI FUZZY LOGIC BERBASIS MIKROKONTROLER KELUARGA MCS51 (PetraFuz
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Thiang Thiang
1999-01-01
Full Text Available This paper presents a Fuzzy Logic Development Tool called PetraFuz which has been developed at Control System Laboratory, Electrical Engineering Department, Petra Christian University. The system consists of a hardware target based on MCS51 microcontroller and a software support running under PC Windows. The system is targeted for developing fuzzy logic based systems. It supports fuzzy logic design, evaluation, assembly language generator and downloading process to the target hardware to perform on-line fuzzy process. Process action and fuzzy parameters could be transferred to PC monitor via RS-232 serial communication, this on-line process parameters is used for fuzzy tuning, i.e. fuzzy if-then rules and fuzzy membership functions. The PetraFuz tool helps very much for Fuzzy system developments, it could reduce development time significantly. The tool could spur the development of fuzzy systems based on microcontroller systems such as fuzzy control systems, fuzzy information processing, etc. Abstract in Bahasa Indonesia : Makalah ini menyajikan sebuah sistem pengembangan kendali fuzzy logic (PetraFuz, Petra Fuzzy Development System yang dikembangkan oleh laboratorium Sistem Kontrol, Jurusan Teknik Elektro, Universitas Kristen Petra Surabaya. Sistem ini terdiri dari perangkat keras sistem mikrokontroler MCS51 dan perangkat lunak pendukung yang berjalan pada PC. Sistem PetraFuz digunakan untuk mengembangkan sistem berbasis fuzzy logic utamanya pada bidang kendali. Kemampuan sistem meliputi pengembangan pada fase perancangan kendali, evaluasi kendali, pembentukan program bahasa assembly MCS51 dan proses downloading program menuju target sistem mikrokontroler MCS51 untuk dieksekusi melakukan kendali pada plant yang nyata. Aksi kendali dapat diakuisi oleh program PC melalui komunikasi serial RS232 sehingga respon kendali dapat digambarkan pada layar monitor untuk dilakukan analisis lebih lanjut yang diperlukan pada proses tuning if-then fuzzy rules
Directory of Open Access Journals (Sweden)
Emer Bernal
2017-01-01
Full Text Available In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.
Directory of Open Access Journals (Sweden)
Henry O Sarmiento
2013-01-01
Full Text Available Este artículo presenta una metodología para predecir estados funcionales en procesos complejos a partir de la estimación de grados de pertenencia difusos. La propuesta integra una medida estática como es el resultado de un clasificador difuso entrenado con los datos históricos del proceso y un algoritmo de estimación basado en la teoría de Markov para eventos discretos. La propuesta, que puede ser integrada a un sistema de monitoreo de sistemas complejos, comprende dos etapas: una etapa de entrenamiento fuera de línea para definir el clasificador difuso y el estimador; y una etapa en línea donde se realizan la clasificación de la situación actual del proceso y la estimación del estado funcional para el siguiente tiempo de muestreo. La propuesta desarrollada para la estimación de estados funcionales permite utilizar cualquier método de agrupamiento difuso que suministre la información base que requiere la metodología. La metodología fue probada con éxito en un sistema de monitoreo para una línea de transmisión de energía y en el monitoreo de un sistema de caldera.This paper presents a methodology to predict functional states in complex processes from the estimation of fuzzy membership degrees. The proposal integrates a static measure, such as the result of a fuzzy classifier trained with historical process data, and an estimation algorithm based on Markov theory for discrete events. The proposal, which can be integrated to the monitoring of complex systems, provides two stages: an off-line training stage to define the fuzzy classifier and the estimator; and an online stage where the classification of the current process situation and the estimation of the next functional state are performed. The proposal for the estimation of functional states allows using any fuzzy clustering method that provides the information required by the methodology. The proposed methodology was successfully tested on a monitoring system for a power
A Novel Approach to Modeling of Hydrogeologic Systems Using Fuzzy Differential Equations
Faybishenko, B. A.
2003-12-01
The many simultaneously occurring processes in unsaturated-saturated heterogeneous soils and fractured rocks can cause field observations to become imprecise and incomplete. Consequently, the results of predictions using deterministic and stochastic mathematical models are often uncertain, vague or "fuzzy." One of the alternative approaches to modeling hydrogeologic systems is the application of a fuzzy-systems approach, which is already widely used in such fields as engineering, physics, chemistry, and biology. After presenting a hydrogeologic system as a fuzzy system, the author presents a fuzzy form of Darcy's equation. Based on this equation, second-order fuzzy partial differential equations of the elliptic type (analogous to the Laplace equation) and the parabolic type (analogous to the Richards equation) are derived. These equations are then approximated as fuzzy-difference equations and solved using the basic principles of fuzzy arithmetic. The solutions for the fuzzy-difference equations take the form of fuzzy membership functions for each observation point (node). The author gives examples of the solutions of these equations for flow in unsaturated and saturated media and then compares them with those obtained using deterministic and stochastic methods.
A novel Neuro-fuzzy classification technique for data mining
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Soumadip Ghosh
2014-11-01
Full Text Available In our study, we proposed a novel Neuro-fuzzy classification technique for data mining. The inputs to the Neuro-fuzzy classification system were fuzzified by applying generalized bell-shaped membership function. The proposed method utilized a fuzzification matrix in which the input patterns were associated with a degree of membership to different classes. Based on the value of degree of membership a pattern would be attributed to a specific category or class. We applied our method to ten benchmark data sets from the UCI machine learning repository for classification. Our objective was to analyze the proposed method and, therefore compare its performance with two powerful supervised classification algorithms Radial Basis Function Neural Network (RBFNN and Adaptive Neuro-fuzzy Inference System (ANFIS. We assessed the performance of these classification methods in terms of different performance measures such as accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and f-measure. In every aspect the proposed method proved to be superior to RBFNN and ANFIS algorithms.
Fuzzy optimization of pneumatic half-floating slide ways
Institute of Scientific and Technical Information of China (English)
李宇鹏; 高殿荣; 单彦霞; 张海青
2008-01-01
Dynamic modeling was carried on by combining the dynamic of machinery with composite triology, and the critical condition in which the ways would not produce composite-friction self-excited vibration was obtained. The movement regularity and characteristic of the airflow in exhaust gas slit were analyzed, and the relationship between pressure lost and geometry parameters of exhaust gas slit was obtained. A dynamic model and a mathematical model were established for pneumatic half-floating slide ways by combining the dynamics of machinery with hydrokinetics. The objective function for the optimization of slide ways was established based on the fuzzy optimization theory. The membership function of fuzzy constraint was deduced, the fuzzy constraint limit was established by amplification coefficient method, and the optimal value was resolved by the multilevel fuzzy comprehensive evaluation method. By combining the internal penalty function method with the variable metric method, the fuzzy optimization design program of ways was designed based on the Matlab platform. The validation was carried on by an example, and ideal results of fuzzy optimization design of slide ways were obtained.
HYBRID SYSTEM BASED FUZZY-PID CONTROL SCHEMES FOR UNPREDICTABLE PROCESS
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M.K. Tan
2011-07-01
Full Text Available In general, the primary aim of polymerization industry is to enhance the process operation in order to obtain high quality and purity product. However, a sudden and large amount of heat will be released rapidly during the mixing process of two reactants, i.e. phenol and formalin due to its exothermic behavior. The unpredictable heat will cause deviation of process temperature and hence affect the quality of the product. Therefore, it is vital to control the process temperature during the polymerization. In the modern industry, fuzzy logic is commonly used to auto-tune PID controller to control the process temperature. However, this method needs an experienced operator to fine tune the fuzzy membership function and universe of discourse via trial and error approach. Hence, the setting of fuzzy inference system might not be accurate due to the human errors. Besides that, control of the process can be challenging due to the rapid changes in the plant parameters which will increase the process complexity. This paper proposes an optimization scheme using hybrid of Q-learning (QL and genetic algorithm (GA to optimize the fuzzy membership function in order to allow the conventional fuzzy-PID controller to control the process temperature more effectively. The performances of the proposed optimization scheme are compared with the existing fuzzy-PID scheme. The results show that the proposed optimization scheme is able to control the process temperature more effectively even if disturbance is introduced.
MOTION MODELLINGUSINGCONCEPTS OF FUZZY ARTIFICIAL POTENTIAL FIELDS
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O. Motlagh
2010-12-01
Full Text Available Artificial potential fields (APF are well established for reactive navigation of mobile robots. This paper describes a fast and robust fuzzy-APF on an ActivMedia AmigoBot. Obstacle-related information is fuzzified by using sensory fusion, which results in a shorter runtime. In addition, the membership functions of obstacle direction and range have been merged into one function, obtaining a smaller block of rules. The system is tested in virtual environments with non-concave obstacles. Then, the paper describes a new approach to motion modelling where the motion of intelligent travellers is modelled by consecutive path segments. In previous work, the authors described a reliable motion modelling technique using causal inference of fuzzy cognitive maps (FCM which has been efficiently modified for the purpose of this contribution. Results and analysis are given to demonstrate the efficiency and accuracy of the proposed motion modelling algorithm.
FUZZY DECISION MAKING MODEL FOR BYZANTINE AGREEMENT
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S. MURUGAN
2014-04-01
Full Text Available Byzantine fault tolerance is of high importance in the distributed computing environment where malicious attacks and software errors are common. A Byzantine process sends arbitrary messages to every other process. An effective fuzzy decision making approach is proposed to eliminate the Byzantine behaviour of the services in the distributed environment. It is proposed to derive a fuzzy decision set in which the alternatives are ranked with grade of membership and based on that an appropriate decision can be arrived on the messages sent by the different services. A balanced decision is to be taken from the messages received across the services. To accomplish this, Hurwicz criterion is used to balance the optimistic and pessimistic views of the decision makers on different services. Grades of membership for the services are assessed using the non-functional Quality of Service parameters and have been estimated using fuzzy entropy measure which logically ranks the participant services. This approach for decision making is tested by varying the number of processes, varying the number of faulty services, varying the message values sent to different services and considering the variation in the views of the decision makers about the services. The experimental result shows that the decision reached is an enhanced one and in case of conflict, the proposed approach provides a concrete result, whereas decision taken using the Lamport’s algorithm is an arbitrary one.
Optimal Distinctiveness Signals Membership Trust.
Leonardelli, Geoffrey J; Loyd, Denise Lewin
2016-07-01
According to optimal distinctiveness theory, sufficiently small minority groups are associated with greater membership trust, even among members otherwise unknown, because the groups are seen as optimally distinctive. This article elaborates on the prediction's motivational and cognitive processes and tests whether sufficiently small minorities (defined by relative size; for example, 20%) are associated with greater membership trust relative to mere minorities (45%), and whether such trust is a function of optimal distinctiveness. Two experiments, examining observers' perceptions of minority and majority groups and using minimal groups and (in Experiment 2) a trust game, revealed greater membership trust in minorities than majorities. In Experiment 2, participants also preferred joining minorities over more powerful majorities. Both effects occurred only when minorities were 20% rather than 45%. In both studies, perceptions of optimal distinctiveness mediated effects. Discussion focuses on the value of relative size and optimal distinctiveness, and when membership trust manifests.
Decision making with fuzzy probability assessments and fuzzy payoff
Institute of Scientific and Technical Information of China (English)
Song Yexin; Yin Di; Chen Mianyun
2005-01-01
A novel method for decision making with fuzzy probability assessments and fuzzy payoff is presented. The consistency of the fuzzy probability assessment is considered. A fuzzy aggregate algorithm is used to indicate the fuzzy expected payoff of alternatives. The level sets of each fuzzy expected payoff are then obtained by solving linear programming models. Based on a defuzzification function associated with the level sets of fuzzy number and a numerical integration formula (Newton-Cotes formula), an effective approach to rank the fuzzy expected payoff of alternatives is also developed to determine the best alternative. Finally, a numerical example is provided to illustrate the proposed method.
NARX prediction of some rare chaotic flows: Recurrent fuzzy functions approach
Energy Technology Data Exchange (ETDEWEB)
Goudarzi, Sobhan [Biomedical Engineering Department, Amirkabir University of Technology, Tehran 15875-4413 (Iran, Islamic Republic of); Jafari, Sajad, E-mail: sajadjafari@aut.ac.ir [Biomedical Engineering Department, Amirkabir University of Technology, Tehran 15875-4413 (Iran, Islamic Republic of); Moradi, Mohammad Hassan [Biomedical Engineering Department, Amirkabir University of Technology, Tehran 15875-4413 (Iran, Islamic Republic of); Sprott, J.C. [Department of Physics, University of Wisconsin–Madison, Madison, WI 53706 (United States)
2016-02-15
The nonlinear and dynamic accommodating capability of time domain models makes them a useful representation of chaotic time series for analysis, modeling and prediction. This paper is devoted to the modeling and prediction of chaotic time series with hidden attractors using a nonlinear autoregressive model with exogenous inputs (NARX) based on a novel recurrent fuzzy functions (RFFs) approach. Case studies of recently introduced chaotic systems with hidden attractors plus classical chaotic systems demonstrate that the proposed modeling methodology exhibits better prediction performance from different viewpoints (short term and long term) compared to some other existing methods. - Highlights: • A new method is proposed for prediction of chaotic time series. • This method is based on novel recurrent fuzzy functions (RFFs) approach. • Some rare chaotic flows are used as test systems. • The new method shows proper performance in short-term prediction. • It also shows proper performance in prediction of attractor's topology.
Polynomial Function and Fuzzy Inference for Evaluating the Project Performance under Uncertainty
Directory of Open Access Journals (Sweden)
A.S. Abdel Azeem
2014-12-01
Full Text Available The objectives of this paper are two folds. The first one is to improve the time forecasting produced from the well known Earned Value Management (EVM, using the polynomial function. The time prediction observed from the polynomial model, which is compared against that observed from the most common method for time forecasting (critical path method, is a more accurate (mean absolute percentage of error is less than 2% than that observed from the conventional deterministic forecasting methods (CDFMs. The second is to evaluate and forecast the overall project performance under uncertainty using the fuzzy inference. As the uncertainty is inherent in real life projects, the polynomial function and fuzzy inference model (PFFI can assist the project managers, to estimate the future status of the project in a more robust and reliable way. Two examples are used to illustrate how the new method can be implemented in reality.
Integrasi Taguchi Loss Function dengan Fuzzy Analytical Hierarchy Process dalam Pemilih Pemasok
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Ahmad S. Indrapriyatna
2011-01-01
Full Text Available One important issue in the line production is the selection of the company's best supplier. Various criteria should be considered for determining the best supplier. Answering to that challenge, we apply Taguchi loss function- Analytical Hierarchy Process Fuzzy-Linear Programming (Taguchi loss function-Fuzzy AHP to find out the best supplier. Moreover, we also consider multiple criteria, i.e., goods’ completeness, quality, delivery, and quality loss in that analysis. By maximizing the suppliers’ performances based on each criterion and aggregated the suppliers’ performances based on the overall criteria, we selected the best one. Applying this method for selecting the best pressure gauge’s supplier in PT. Coca Cola Bottling Indonesia Central Sumatera (PT. CCBICS, we found out that among three suppliers, the second supplier is the best one.
Energy Technology Data Exchange (ETDEWEB)
Martin-del-Campo, C.; Francois, J.L.; Barragan, A.M. [Universidad Nacional Autonoma de Mexico - Facultad de Ingenieria (Mexico); Palomera, M.A. [Universidad Nacional Autonoma de Mexico - Instituto de Investigaciones en Matematicas Aplicadas y Sistema, Mexico, D. F. (Mexico)
2005-07-01
In this paper we develop a methodology based on the use of the Fuzzy Logic technique to build multi-objective functions to be used in optimization processes applied to in-core nuclear fuel management. As an example, we selected the problem of determining optimal radial fuel enrichment and gadolinia distributions in a typical 'Boiling Water Reactor (BWR)' fuel lattice. The methodology is based on the use of the mathematical capability of Fuzzy Logic to model nonlinear functions of arbitrary complexity. The utility of Fuzzy Logic is to map an input space into an output space, and the primary mechanism for doing this is a list of if-then statements called rules. The rules refer to variables and adjectives that describe those variables and, the Fuzzy Logic technique interprets the values in the input vectors and, based on the set of rules assigns values to the output vector. The methodology was developed for the radial optimization of a BWR lattice where the optimization algorithm employed is Tabu Search. The global objective is to find the optimal distribution of enrichments and burnable poison concentrations in a 10*10 BWR lattice. In order to do that, a fuzzy control inference system was developed using the Fuzzy Logic Toolbox of Matlab and it has been linked to the Tabu Search optimization process. Results show that Tabu Search combined with Fuzzy Logic performs very well, obtaining lattices with optimal fuel utilization. (authors)
Lim, Joon S
2009-03-01
Fuzzy neural networks (FNNs) have been successfully applied to generate predictive rules for medical or diagnostic data. This brief presents an approach to detect premature ventricular contractions (PVCs) using the neural network with weighted fuzzy membership functions (NEWFMs). The NEWFM classifies normal and PVC beats by the trained bounded sum of weighted fuzzy membership functions (BSWFMs) using wavelet transformed coefficients from the MIT-BIH PVC database. The eight generalized coefficients, locally related to the time signal, are extracted by the nonoverlap area distribution measurement method. The eight generalized coefficients are used for the three PVC data sets with reliable accuracy rates of 99.80%, 99.21%, and 98.78%, respectively, which means that the selected features are less dependent on the data sets. It is shown that the locations of the eight features are not only around the QRS complex that represents ventricular depolarization in the electrocardiogram (ECG) containing a Q wave, an R wave, and an S wave, but also the QR segment from the Q wave to the R wave has more discriminate information than the RS segment from the R wave to the S wave. The BSWFMs of the eight features trained by NEWFM are shown visually, which makes the features explicitly interpretable. Since each BSWFM combines multiple weighted fuzzy membership functions into one using the bounded sum, the eight small-sized BSWFMs can realize real-time PVC detection in a mobile environment.
Affinity functions: recognizing essential parameters in fuzzy connectedness based image segmentation
Ciesielski, Krzysztof C.; Udupa, Jayaram K.
2009-02-01
Fuzzy connectedness (FC) constitutes an important class of image segmentation schemas. Although affinity functions represent the core aspect (main variability parameter) of FC algorithms, they have not been studied systematically in the literature. In this paper, we present a thorough study to fill this gap. Our analysis is based on the notion of equivalent affinities: if any two equivalent affinities are used in the same FC schema to produce two versions of the algorithm, then these algorithms are equivalent in the sense that they lead to identical segmentations. We give a complete characterization of the affinity equivalence and show that many natural definitions of affinity functions and their parameters used in the literature are redundant in the sense that different definitions and values of such parameters lead to equivalent affinities. We also show that two main affinity types - homogeneity based and object feature based - are equivalent, respectively, to the difference quotient of the intensity function and Rosenfeld's degree of connectivity. In addition, we demonstrate that any segmentation obtained via relative fuzzy connectedness (RFC) algorithm can be viewed as segmentation obtained via absolute fuzzy connectedness (AFC) algorithm with an automatic and adaptive threshold detection. We finish with an analysis of possible ways of combining different component affinities that result in non equivalent affinities.
Two-Point Fuzzy Ostrowski Type Inequalities
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Muhammad Amer Latif
2013-08-01
Full Text Available Two-point fuzzy Ostrowski type inequalities are proved for fuzzy Hölder and fuzzy differentiable functions. The two-point fuzzy Ostrowski type inequality for M-lipshitzian mappings is also obtained. It is proved that only the two-point fuzzy Ostrowski type inequality for M-lipshitzian mappings is sharp and as a consequence generalize the two-point fuzzy Ostrowski type inequalities obtained for fuzzy differentiable functions.
Designing Integrated Fuzzy Guidance Law for Aerodynamic Homing Missiles Using Genetic Algorithms
Omar, Hanafy M.
The Fuzzy logic controller (FLC) is well-known for robustness to parameter variations and ability to reject noise. However, its design requires definition of many parameters. This work proposes a systematic and simple procedure to develop an integrated fuzzy-based guidance law which consists of three FLC. Each is activated in a region of the interception. Another fuzzy-based switching system is introduced to allow smooth transition between these controllers. The parameters of all the fuzzy controllers, which include the distribution of the membership functions and the rules, are obtained simply by observing the function of each controller. Furthermore, these parameters are tuned by genetic algorithms by solving an optimization problem to minimize the interception time, missile acceleration commands, and miss distance. The simulation results show that the proposed procedure can generate a guidance law with satisfactory performance.
Rodrigo, M A; Seco, A; Ferrer, J; Penya-roja, J M; Valverde, J L
1999-01-01
In this paper, several tuning algorithms, specifically ITAE, IMC and Cohen and Coon, were applied in order to tune an activated sludge aeration PID controller. Performance results of these controllers were compared by simulation with those obtained by using a nonlinear fuzzy PID controller. In order to design this controller, a trial and error procedure was used to determine, as a function of error at current time and at a previous time, sets of parameters (including controller gain, integral time and derivative time) which achieve satisfactory response of a PID controller actuating over the aeration process. Once these sets of data were obtained, neural networks were used to obtain fuzzy membership functions and fuzzy rules of the fuzzy PID controller.
A fuzzy neural network for intelligent data processing
Xie, Wei; Chu, Feng; Wang, Lipo; Lim, Eng Thiam
2005-03-01
In this paper, we describe an incrementally generated fuzzy neural network (FNN) for intelligent data processing. This FNN combines the features of initial fuzzy model self-generation, fast input selection, partition validation, parameter optimization and rule-base simplification. A small FNN is created from scratch -- there is no need to specify the initial network architecture, initial membership functions, or initial weights. Fuzzy IF-THEN rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy; irrelevant inputs are detected and deleted, and membership functions and network weights are trained with a gradient descent algorithm, i.e., error backpropagation. Experimental studies on synthesized data sets demonstrate that the proposed Fuzzy Neural Network is able to achieve accuracy comparable to or higher than both a feedforward crisp neural network, i.e., NeuroRule, and a decision tree, i.e., C4.5, with more compact rule bases for most of the data sets used in our experiments. The FNN has achieved outstanding results for cancer classification based on microarray data. The excellent classification result for Small Round Blue Cell Tumors (SRBCTs) data set is shown. Compared with other published methods, we have used a much fewer number of genes for perfect classification, which will help researchers directly focus their attention on some specific genes and may lead to discovery of deep reasons of the development of cancers and discovery of drugs.
Mezey, Paul G
2014-09-16
Conspectus Just as complete molecules have no boundaries and have "fuzzy" electron density clouds approaching zero density exponentially at large distances from the nearest nucleus, a physically justified choice for electron density fragments exhibits similar behavior. Whereas fuzzy electron densities, just as any fuzzy object, such as a thicker cloud on a foggy day, do not lend themselves to easy visualization, one may partially overcome this by using isocontours. Whereas a faithful representation of the complete fuzzy density would need infinitely many such isocontours, nevertheless, by choosing a selected few, one can still obtain a limited pictorial representation. Clearly, such images are of limited value, and one better relies on more complete mathematical representations, using, for example, density matrices of fuzzy fragment densities. A fuzzy density fragmentation can be obtained in an exactly additive way, using the output from any of the common quantum chemical computational techniques, such as Hartree-Fock, MP2, and various density functional approaches. Such "fuzzy" electron density fragments properly represented have proven to be useful in a rather wide range of applications, for example, (a) using them as additive building blocks leading to efficient linear scaling macromolecular quantum chemistry computational techniques, (b) the study of quantum chemical functional groups, (c) using approximate fuzzy fragment information as allowed by the holographic electron density theorem, (d) the study of correlations between local shape and activity, including through-bond and through-space components of interactions between parts of molecules and relations between local molecular shape and substituent effects, (e) using them as tools of density matrix extrapolation in conformational changes, (f) physically valid averaging and statistical distribution of several local electron densities of common stoichiometry, useful in electron density databank mining, for
Runtime Verification of Pacemaker Functionality Using Hierarchical Fuzzy Colored Petri-nets.
Majma, Negar; Babamir, Seyed Morteza; Monadjemi, Amirhassan
2017-02-01
Today, implanted medical devices are increasingly used for many patients and in case of diverse health problems. However, several runtime problems and errors are reported by the relevant organizations, even resulting in patient death. One of those devices is the pacemaker. The pacemaker is a device helping the patient to regulate the heartbeat by connecting to the cardiac vessels. This device is directed by its software, so any failure in this software causes a serious malfunction. Therefore, this study aims to a better way to monitor the device's software behavior to decrease the failure risk. Accordingly, we supervise the runtime function and status of the software. The software verification means examining limitations and needs of the system users by the system running software. In this paper, a method to verify the pacemaker software, based on the fuzzy function of the device, is presented. So, the function limitations of the device are identified and presented as fuzzy rules and then the device is verified based on the hierarchical Fuzzy Colored Petri-net (FCPN), which is formed considering the software limits. Regarding the experiences of using: 1) Fuzzy Petri-nets (FPN) to verify insulin pumps, 2) Colored Petri-nets (CPN) to verify the pacemaker and 3) To verify the pacemaker by a software agent with Petri-network based knowledge, which we gained during the previous studies, the runtime behavior of the pacemaker software is examined by HFCPN, in this paper. This is considered a developing step compared to the earlier work. HFCPN in this paper, compared to the FPN and CPN used in our previous studies reduces the complexity. By presenting the Petri-net (PN) in a hierarchical form, the verification runtime, decreased as 90.61% compared to the verification runtime in the earlier work. Since we need an inference engine in the runtime verification, we used the HFCPN to enhance the performance of the inference engine.
Bosch, David; Ledo, Juanjo; Queralt, Pilar
2013-07-01
Fuzzy logic has been used for lithology prediction with remarkable success. Several techniques such as fuzzy clustering or linguistic reasoning have proven to be useful for lithofacies determination. In this paper, a fuzzy inference methodology has been implemented as a MATLAB routine and applied for the first time to well log data from the German Continental Deep Drilling Program (KTB). The training of the fuzzy inference system is based on the analysis of the multi-class Matthews correlation coefficient computed for the classification matrix. For this particular data set, we have found that the best suited membership function type is the piecewise linear interpolation of the normalized histograms; that the best combination operator for obtaining the final lithology degrees of membership is the fuzzy gamma operator; and that all the available properties are relevant in the classification process. Results show that this fuzzy logic-based method is suited for rapidly and reasonably suggesting a lithology column from well log data, neatly identifying the main units and in some cases refining the classification, which can lead to a better interpretation. We have tested the trained system with synthetic data generated from property value distributions of the training data set to find that the differences in data distributions between both wells are significant enough to misdirect the inference process. However, a cross-validation analysis has revealed that, even with differences between data distributions and missing lithologies in the training data set, this fuzzy logic inference system is able to output a coherent classification.
Water quality evaluation based on improved fuzzy matter-element method
Institute of Scientific and Technical Information of China (English)
Dongjun Liu; Zhihong Zou
2012-01-01
For natural water,method of water quality evaluation based on improved fuzzy matter-element evaluation method is presented.Two important parts are improved,the weights determining and fuzzy membership functions.The coefficient of variation of each indicator is used to determine the weight instead of traditional calculating superscales method.On the other hand,fuzzy matter-elements are constructed,and normal membership degrees are used instead of traditional trapezoidal ones.The composite fuzzy matter-elements with associated coefficient are constructed through associated transformation.The levels of natural water quality are determined according to the principle of maximum correlation.The improved fuzzy matter-element evaluation method is applied to evaluate water quality of the Luokou mainstream estuary at the first ten weeks in 2011 with the coefficient of variation method determining the weights.Water quality of Luokou mainstream estuary is dropping from level Ⅰ to level Ⅱ.The results of the improved evaluation method are basically the same as the official water quality.The variation coefficient method can reduce the workload,and overcome the adverse effects from abnormal values,compared with the traditional calculating superscales method.The results of improved fuzzy matterelement evaluation method are more credible than the ones of the traditional evaluation method.The improved evaluation method can use information of monitoring data more scientifically and comprehensively,and broaden a new evaluation method for water quality assessment.
Membership Satisfaction and the Cost of Membership
DEFF Research Database (Denmark)
Eskildsen, Jacob Kjær; Kristensen, Kai
2011-01-01
This article suggests a framework for measuring membership satisfaction based on a literature study. The framework is tested on data from more than 8800 members from 29 different Danish unemployment insurance funds. The framework fits the data well and is able to explain 83% of the variation...... in membership satisfaction. Furthermore the cost of administration per member and membership satisfaction is found to be able to explain differences in membership loyalty when the 29 unemployment insurance funds are compared. Finally administration costs per member are found to be dependent on the number...
Fuzzy Evaluation of Thermal Comfort in Naturally Ventilated Residential Buildings in China
Institute of Scientific and Technical Information of China (English)
WANG Yi; NIU Tian-cai; LIU Jia-ping; XIAO Yong-qiang
2009-01-01
In order to assess the differences between the human body thermal sensation in naturally ventilat-ed space and that in air-conditioned space,the fuzzy evaluation model was adopted in the research of thermal sensation in naturally ventilated space.Based on the questionnaires and field measurements,the membership functions were presented by the statistic of the covering frequency to the fuzzy subset.Dry-bulb temperature was taken as the only independent variable for membership functions.The maximum values of membership grades are all at 0.5 or so, which is a distinction character on thermal comfort of naturally ventihted space.By the cal-culating resultS of membership grades value to different fuzzy evaluation subsets,the Predicted Mean Vote (PMV)was obtained.Furthermore,energy coefficient(Ea) was introduced to calculate the energy consump-tion,and the prediction methods of residential building energy consumption were also discussed.Finally,the importance of evaluation model of thermal sense is shown through the energy consumption prediction in a specificresidential building.
Mao, Shengyong; Zhang, Mengling; Liu, Junhua; Zhu, Weiyun
2015-11-03
The bacterial community composition and function in the gastrointestinal tracts (GITs) of dairy cattle is very important, since it can influence milk production and host health. However, our understanding of bacterial communities in the GITs of dairy cattle is still very limited. This study analysed bacterial communities in ten distinct GIT sites (the digesta and mucosa of the rumen, reticulum, omasum, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum) in six dairy cattle. The study observed 542 genera belonging to 23 phyla distributed throughout the cattle GITs, with the Firmicutes, Bacteroidetes and Proteobacteria predominating. In addition, data revealed significant spatial heterogeneity in composition, diversity and species abundance distributions of GIT microbiota. Furthermore, the study inferred significant differences in the predicted metagenomic profiles among GIT regions. In particular, the relative abundances of the genes involved in carbohydrate metabolism were overrepresented in the digesta samples of forestomaches, and the genes related to amino acid metabolism were mainly enriched in the mucosal samples. In general, this study provides the first deep insights into the composition of GIT microbiota in dairy cattle, and it may serve as a foundation for future studies in this area.
A Cooperative Network Intrusion detection Based on Fuzzy SVMs
Directory of Open Access Journals (Sweden)
Shaohua Teng
2010-04-01
Full Text Available There is a large number of noise in the data obtained from network, which deteriorates intrusion detection performance. To delete the noise data, data preprocessing is done before the construction of hyperplane in support vector machine (SVM. By introducing fuzzy theory into SVM, a new method is proposed for network intrusion detection. Because the attack behavior is different for different network protocol, a different fuzzy membership function is formatted, such that for each class of protocol there is a SVM. To implement this approach, a fuzzy SVM-based cooperative network intrusion detection system with multi-agent architecture is presented. It is composed of three types of agents corresponding to TCP, UDP, and ICMP protocols, respectively. Simulation experiments are done by using KDD CUP 1999 data set, results show that the training time is significantly shortened, storage space requirement is reduced, and classification accuracy is improved.
Design and performance comparison of fuzzy logic based tracking controllers
Lea, Robert N.; Jani, Yashvant
1992-01-01
Several camera tracking controllers based on fuzzy logic principles have been designed and tested in software simulation in the software technology branch at the Johnson Space Center. The fuzzy logic based controllers utilize range measurement and pixel positions from the image as input parameters and provide pan and tilt gimble rate commands as output. Two designs of the rulebase and tuning process applied to the membership functions are discussed in light of optimizing performance. Seven test cases have been designed to test the performance of the controllers for proximity operations where approaches like v-bar, fly-around and station keeping are performed. The controllers are compared in terms of responsiveness, and ability to maintain the object in the field-of-view of the camera. Advantages of the fuzzy logic approach with respect to the conventional approach have been discussed in terms of simplicity and robustness.
Fuzzy Adaptive PI Controller for DTFC in Electric Vehicle
Directory of Open Access Journals (Sweden)
Medjdoub khessam
2014-12-01
Full Text Available This paper presents a technique to control the electric vehicle (EV speed and torque at any curve. Our propulsion model consist of two permanent magnet synchronous (PMSM motors. The fuzzy adaptive PI controller is used to adjust the different static error constants, as per the speed error. The suggested based on the direct torque fuzzy control (DTFC. A Mamdani type fuzzy direct torque controller is first developed and then rules are modified using stator current membership functions. The computations are ensured by the electronic differential, this driving process permit to steer each driving wheels at any curve separately.Modeling and simulation are carried out using the Matlab/Simulink tool to investigate the performance of the proposed system.
Generating Interpretable Fuzzy Systems for Classification Problems
Directory of Open Access Journals (Sweden)
Juan A. Contreras-Montes
2009-12-01
Full Text Available This paper presents a new method to generate interpretable fuzzy systems from training data to deal with classification problems. The antecedent partition uses triangular sets with 0.5 interpolations avoiding the presence of complex overlapping that happens in another method. Singleton consequents are generated form the projection of the modal values of each triangular membership function into the output space. Least square method is used to adjust the consequents. The proposed method gets a higher average classification accuracy rate than the existing methods with a reduced number of rules andparameters and without sacrificing the fuzzy system interpretability. The proposed approach is applied to two classical classification problems: Iris data and the Wisconsin Breast Cancer classification problem.
A COMPARISON OF ALTERNATIVE CRITERIA FOR DEFINING FUZZY BOUNDARIES ON FUZZY CATEGORICAL MAPS
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
This paper provides a brief introduction to the methods for generating fuzzy categorical maps from remotely sensed images (in graphical and digital forms). This is followed by a description of the slicing process for deriving fuzzy boundaries from fuzzy categorical maps, which can be based on the maximum fuzzy membership values, confusion index, or measure of entropy. Results from an empirical test preformed in an Edinburgh suburb show that fuzzy boundaries of land cover can be derived from aerial photographs and satellite images by using the three criteria with small differences, and that slicing based on the maximum fuzzy membership values is the easiest and most straightforward solution. This, in turn, implies the suitability of maintaining both a crisp classification and its underlying certainty map for deriving fuzzy boundaries at different thresholds, which is a flexible and compact management of categorical map data and their uncertainty.
Navigating a Mobile Robot Across Terrain Using Fuzzy Logic
Seraji, Homayoun; Howard, Ayanna; Bon, Bruce
2003-01-01
A strategy for autonomous navigation of a robotic vehicle across hazardous terrain involves the use of a measure of traversability of terrain within a fuzzy-logic conceptual framework. This navigation strategy requires no a priori information about the environment. Fuzzy logic was selected as a basic element of this strategy because it provides a formal methodology for representing and implementing a human driver s heuristic knowledge and operational experience. Within a fuzzy-logic framework, the attributes of human reasoning and decision- making can be formulated by simple IF (antecedent), THEN (consequent) rules coupled with easily understandable and natural linguistic representations. The linguistic values in the rule antecedents convey the imprecision associated with measurements taken by sensors onboard a mobile robot, while the linguistic values in the rule consequents represent the vagueness inherent in the reasoning processes to generate the control actions. The operational strategies of the human expert driver can be transferred, via fuzzy logic, to a robot-navigation strategy in the form of a set of simple conditional statements composed of linguistic variables. These linguistic variables are defined by fuzzy sets in accordance with user-defined membership functions. The main advantages of a fuzzy navigation strategy lie in the ability to extract heuristic rules from human experience and to obviate the need for an analytical model of the robot navigation process.
Learning fuzzy logic control system
Lung, Leung Kam
1994-01-01
The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the
Coelho, Antonio Augusto Rodrigues
2016-01-01
This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723
Sensor-based navigation of a mobile robot using automatically constructed fuzzy rules
Energy Technology Data Exchange (ETDEWEB)
Watanabe, Y.; Pin, F.G.
1993-10-01
A system for automatic generation of fuzzy rules is proposed which is based on a new approach, called ``Fuzzy Behaviorist,`` and on its associated formalism for rule base development in behavior-based robot control systems. The automated generator of fuzzy rules automatically constructs the set of rules and the associated membership functions that implement reasoning schemes that have been expressed in qualitative terms. The system also checks for completeness of the rule base and independence and/or redundancy of the rules to ensure that the requirements of the formalism are satisfied. Examples of the automatic generation of fuzzy rules for cases involving suppression and/or inhibition of fuzzy behaviors are given and discussed. Experimental results obtained with the automated fuzzy rule generator applied to the domain of sensor-based navigation in a priori unknown environments using one of our autonomous test-bed robots are then presented and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using our proposed ``Fuzzy Behaviorist`` approach.
Automatic generation of fuzzy rules for the sensor-based navigation of a mobile robot
Energy Technology Data Exchange (ETDEWEB)
Pin, F.G.; Watanabe, Y.
1994-10-01
A system for automatic generation of fuzzy rules is proposed which is based on a new approach, called {open_quotes}Fuzzy Behaviorist,{close_quotes} and on its associated formalism for rule base development in behavior-based robot control systems. The automated generator of fuzzy rules automatically constructs the set of rules and the associated membership functions that implement reasoning schemes that have been expressed in qualitative terms. The system also checks for completeness of the rule base and independence and/or redundancy of the rules to ensure that the requirements of the formalism are satisfied. Examples of the automatic generation of fuzzy rules for cases involving suppression and/or inhibition of fuzzy behaviors are given and discussed. Experimental results obtained with the automated fuzzy rule generator applied to the domain of sensor-based navigation in a priori unknown environments using one of our autonomous test-bed robots are then presented and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using our proposed {open_quotes}Fuzzy Behaviorist{close_quotes} approach.
RANDOM VARIABLE WITH FUZZY PROBABILITY
Institute of Scientific and Technical Information of China (English)
吕恩琳; 钟佑明
2003-01-01
Mathematic description about the second kind fuzzy random variable namely the random variable with crisp event-fuzzy probability was studied. Based on the interval probability and using the fuzzy resolution theorem, the feasible condition about a probability fuzzy number set was given, go a step further the definition arid characters of random variable with fuzzy probability ( RVFP ) and the fuzzy distribution function and fuzzy probability distribution sequence of the RVFP were put forward. The fuzzy probability resolution theorem with the closing operation of fuzzy probability was given and proved. The definition and characters of mathematical expectation and variance of the RVFP were studied also. All mathematic description about the RVFP has the closing operation for fuzzy probability, as a result, the foundation of perfecting fuzzy probability operation method is laid.
An Improvement of Empirical Risk Functional in Neuro-Fuzzy Classifier
Directory of Open Access Journals (Sweden)
Elham Zamani
2013-09-01
Full Text Available This paper suggests a new method to improve of Empirical Risk Functional . Empirical Risk Functional acts as cost function for training neuro-fuzzy classifiers. Empirical risk minimization seeks the function that best fits the training data and it is equivalent to maximum likelihood estimation. The name of this cost function is Approximate Differentiable Empirical Risk Functional (ADERF.This function enables us to use a differentiable approximation of the misclassification rate so that the Empirical Risk Minimization Principle formulated in Statistical Learning Theory can be applied. Also there is a learning algorithm based on ADERF. With our new method,more component of output vector of fuzzy classifier map to 1.By evaluating the effects of the proposed method, we can see the convergence speed of the learning algorithm and the classification accuracy are improved,and causes improved ADERF. The effects of improved ADERF, was illustrated. Experimental results on a number of benchmark classification tasks and comparison between approaches are provided
Directory of Open Access Journals (Sweden)
Mohammed Shoeb Mohiuddin
2014-09-01
Full Text Available It is often difficult to develop an accurate mathematical model of DC motor due to unknown load variation, unknown and unavoidable parameter variations or nonlinearities due to saturation temperature variations and system disturbances. Fuzzy logic application can handle such nonlinearities so that the controller design is fundamentally robust which is not possible in conventional controllers. The knowledge base of a fuzzy logic controller (FLC encapsulates expert knowledge and consists of the Data base (membership functions and Rule-Base of the controller. Optimization of both these knowledge base components is critical to the performance of the controller and has traditionally been achieved through a process of trial and error. Such an approach is convenient for FLCs having low numbers of input variables however for greater numbers of inputs, more formal methods of knowledge base optimization are required. In this work, we study the challenging task of controlling the speed of DC motor. The feasibility of such controller design is evaluated by simulation in the MATLAB/Simulink environment. In this study Conventional Proportional Integral Derivative controller, Fuzzy logic controller using a chopper circuit and Fuzzy tuned PID controller are analyzed and compared. Simulation software like MATLAB with Simulink has been used for modeling and simulation purpose. The performance comparison of conventional controller with Fuzzy logic controller using chopper circuit and Fuzzy tuned PID controller has been done in terms of several performance measures Such as Settling time, Rise time and Overshoot.
A fuzzy Bayesian approach to flood frequency estimation with imprecise historical information
Salinas, José Luis; Kiss, Andrea; Viglione, Alberto; Viertl, Reinhard; Blöschl, Günter
2016-09-01
This paper presents a novel framework that links imprecision (through a fuzzy approach) and stochastic uncertainty (through a Bayesian approach) in estimating flood probabilities from historical flood information and systematic flood discharge data. The method exploits the linguistic characteristics of historical source material to construct membership functions, which may be wider or narrower, depending on the vagueness of the statements. The membership functions are either included in the prior distribution or the likelihood function to obtain a fuzzy version of the flood frequency curve. The viability of the approach is demonstrated by three case studies that differ in terms of their hydromorphological conditions (from an Alpine river with bedrock profile to a flat lowland river with extensive flood plains) and historical source material (including narratives, town and county meeting protocols, flood marks and damage accounts). The case studies are presented in order of increasing fuzziness (the Rhine at Basel, Switzerland; the Werra at Meiningen, Germany; and the Tisza at Szeged, Hungary). Incorporating imprecise historical information is found to reduce the range between the 5% and 95% Bayesian credibility bounds of the 100 year floods by 45% and 61% for the Rhine and Werra case studies, respectively. The strengths and limitations of the framework are discussed relative to alternative (non-fuzzy) methods. The fuzzy Bayesian inference framework provides a flexible methodology that fits the imprecise nature of linguistic information on historical floods as available in historical written documentation.
Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions
Khoury, Mehdi; Liu, Honghai
This research introduces and builds on the concept of Fuzzy Gaussian Inference (FGI) (Khoury and Liu in Proceedings of UKCI, 2008 and IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS 2009), 2009) as a novel way to build Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions. This method is now combined with a Genetic Programming Fuzzy rule-based system in order to classify boxing moves from natural human Motion Capture data. In this experiment, FGI alone is able to recognise seven different boxing stances simultaneously with an accuracy superior to a GMM-based classifier. Results seem to indicate that adding an evolutionary Fuzzy Inference Engine on top of FGI improves the accuracy of the classifier in a consistent way.
Fuzzy logic feedback control for fed-batch enzymatic hydrolysis of lignocellulosic biomass.
Tai, Chao; Voltan, Diego S; Keshwani, Deepak R; Meyer, George E; Kuhar, Pankaj S
2016-06-01
A fuzzy logic feedback control system was developed for process monitoring and feeding control in fed-batch enzymatic hydrolysis of a lignocellulosic biomass, dilute acid-pretreated corn stover. Digested glucose from hydrolysis reaction was assigned as input while doser feeding time and speed of pretreated biomass were responses from fuzzy logic control system. Membership functions for these three variables and rule-base were created based on batch hydrolysis data. The system response was first tested in LabVIEW environment then the performance was evaluated through real-time hydrolysis reaction. The feeding operations were determined timely by fuzzy logic control system and efficient responses were shown to plateau phases during hydrolysis. Feeding of proper amount of cellulose and maintaining solids content was well balanced. Fuzzy logic proved to be a robust and effective online feeding control tool for fed-batch enzymatic hydrolysis.
A high performance, ad-hoc, fuzzy query processing system for relational databases
Mansfield, William H., Jr.; Fleischman, Robert M.
1992-01-01
Database queries involving imprecise or fuzzy predicates are currently an evolving area of academic and industrial research. Such queries place severe stress on the indexing and I/O subsystems of conventional database environments since they involve the search of large numbers of records. The Datacycle architecture and research prototype is a database environment that uses filtering technology to perform an efficient, exhaustive search of an entire database. It has recently been modified to include fuzzy predicates in its query processing. The approach obviates the need for complex index structures, provides unlimited query throughput, permits the use of ad-hoc fuzzy membership functions, and provides a deterministic response time largely independent of query complexity and load. This paper describes the Datacycle prototype implementation of fuzzy queries and some recent performance results.
Fuzzy-Expert Diagnostics for Detecting and Locating Internal Faults in Three Phase Induction Motors
Institute of Scientific and Technical Information of China (English)
DONG Mingchui; CHEANG Takson; SEKAR Booma Devi; CHAN Sileong
2008-01-01
Internal faults in three phase induction motors can result in serious performance degradation and eventual system failures if not properly detected and treated in time. Artificial intelligence techniques, the core of soft-computing, have numerous advantages over conventional fault diagnostic approaches; therefore, a soft-computing system was developed to detect and diagnose electric motor faults. The fault diagnostic system for three-phase induction motors samples the fault symptoms and then uses a fuzzy-expert forward inference model to identify the fault. This paper describes how to define the membership functions and fuzzy sets based on the fault symptoms and how to construct the hierarchical fuzzy inference nets with the propagation of probabilities concerning the uncertainty of faults. The designed hierarchical fuzzy inference nets efficiently detect and diagnose the fault type and exact location in a three phase induction motor. The validity and effectiveness of this approach is clearly shown from obtained testing results.
Study of Fuzzy Neural Networks Model for System Condition Monitoring of AUV
Institute of Scientific and Technical Information of China (English)
WANG Yu-jia; ZHANG Ming-jun
2002-01-01
A structure equivalent model of fuzzy-neural networks for system condition monitoring is proposed, whose outputs are the condition or the degree of fault occurring in some parts of the system. This network is composed of six layers of neurons,which represent the membership functions, fuzzy rules and outputs respectively. The structure parameters and weights are obtained by processing off-line learning, and the fuzzy rules are derived from the experience. The results of the computer simulation for the autonomous underwater vehicle condition monitoring based on this fuzzy-neural networks show that the network is efficient and feasible in gaining the condition information or the degree of fault of the two main propellers.
A gradient-descent-based approach for transparent linguistic interface generation in fuzzy models.
Chen, Long; Chen, C L Philip; Pedrycz, Witold
2010-10-01
Linguistic interface is a group of linguistic terms or fuzzy descriptions that describe variables in a system utilizing corresponding membership functions. Its transparency completely or partly decides the interpretability of fuzzy models. This paper proposes a GRadiEnt-descEnt-based Transparent lInguistic iNterface Generation (GREETING) approach to overcome the disadvantage of traditional linguistic interface generation methods where the consideration of the interpretability aspects of linguistic interface is limited. In GREETING, the widely used interpretability criteria of linguistic interface are considered and optimized. The numeric experiments on the data sets from University of California, Irvine (UCI) machine learning databases demonstrate the feasibility and superiority of the proposed GREETING method. The GREETING method is also applied to fuzzy decision tree generation. It is shown that GREETING generates better transparent fuzzy decision trees in terms of better classification rates and comparable tree sizes.
Development of fuzzy control of a fuel cell generation system using FPGA
Institute of Scientific and Technical Information of China (English)
杨帆; 朱新坚; 李浩
2006-01-01
A fuzzy controller based on improved Generalized-Membership-Function(GMF) algorithm for a fuel cell generation system was introduced. Under the demands on control in application of the converter, a Field Programmable Gate Array (FPGA) realization method to manage the power flow was given. This control system based on the proposed modified GMF was proved to be a universal approximation system in theory. The fuzzy control technique was combined with Eletronic Design Automatic(EDA)technique and a paralleling fuzzy controller was implemented in FPGA. Paralleling fuzzy controller based on improved GMF algorithm was implemented on a Cyclone FPGA. The result of simulation based on QuartusⅡ confirmed the validity of the proposed method.
Institute of Scientific and Technical Information of China (English)
ZHONG Hu; AO Guo-qiang; WANG Feng; MA Zi-lin; MAO Xiao-jian; ZHUO Bin
2008-01-01
A torque distribution strategy was designed by using fuzzy logic to realize the optimal control. Thevehicle load zones were dynamically divided into several zones by several torque lines to indicate the driversdemand and the high or low efficient operating areas of the diesel engine. The fuzzy logic controller withtrapezoid membership function and Mamdani rule reference mechanism was utilized. There are over 100 rulesused in this fuzzy-based torque distribution strategy which are sorted into four rule-bases. The fuel economyand acceleration tests were designed to test and validate the integrated starter/generator (ISG) bus perfor-mance using fuzzy-based torque distribution strategy. The fuel economy is improved 7.7% compared with therule-based strategy. Finally the road test results reveal that there is about 157% improvement of fuel economy.And the 0-50 km/h acceleration time is 9.5% shorter than the original bus.
Saini, J. S.; Jain, V.
2015-03-01
This paper presents a genetic algorithm (GA)-based design and optimization of fuzzy logic controller (FLC) for automatic generation control (AGC) for a single area. FLCs are characterized by a set of parameters, which are optimized using GA to improve their performance. The design of input and output membership functions (mfs) of an FLC is carried out by automatically tuning (off-line) the parameters of the membership functions. Tuning is based on maximization of a comprehensive fitness function constructed as inverse of a weighted average of three performance indices, i.e., integral square deviation (ISD), the integral of square of the frequency deviation and peak overshoot (Mp), and settling time (ts). The GA-optimized FLC (GAFLC) shows better performance as compared to a conventional proportional integral (PI) and a hand-designed fuzzy logic controller not only for a standard system (displaying frequency deviations) but also under parametric and load disturbances.
{\\lambda}-statistical convergent function sequences in intuitionistic fuzzy normed spaces
Karakaya, Vatan; Ertürk, Müzeyyen; Gürsoy, Faik
2011-01-01
Fuzzy logic was introduced by Zadeh in 1965. Since then, the importance of fuzzy logic has come increasingly to the present.There are many applications of fuzzy logic in the field of science and engineering, e.g. population dynamics (Barros), chaos control (Feng,Fradkov), computer programming (Giles), nonlinear dynamical systems (Hong), etc. The concept of intuitionistic fuzzy set, as a generalization of fuzzy logic, was introduced by Atanassov in 1986. Quite recently Park has introduced the concept of intuitionistic fuzzy metric space, and Saadati and Park studied the notion of intuitionistic fuzzy normed space. Intuitionistic fuzzy analogues of many concept in classical analysis was studied by many authors (Mursaleen, Rsaadati, Jebril, Dinda, etc.). The concept of statistical convergence was introduced by Fast. Mursaleen defined {\\lambda}-statistical convergence in Muhammed. Also the concept of statistical convergence was studied in intuitionistic fuzzy normed space in Karakus..Quite recently, Karakaya et a...
Analysis of Helical Gear System Dynamic Response Based on Fuzzy Numbers
Institute of Scientific and Technical Information of China (English)
马亮; 李瑰贤; 杨伟君
2001-01-01
A non-linear dynamic model with the single degree of freedom of a helical gear pair introducing frzzy numbers is developed. In this proposed model, time-variant mesh stiffness, which is a non-linear parameter, mesh damping and composite error of a pair of meshing tooth of the gear pair are all included. Mesh stiffness is calculated by expressing Bo (r) as a Fourier series. Ⅱshape function is introduced as the membership function to characterize the fuzziness of the error. Fuzzy displacement dynamic response of the geared system at A- level, which is a closed interval, is ohtained by removing the fuzziness of the fuzzy differential equations and using Runge-Kutta numerical method. In fact, the fuzzy dynamic response and dynamic loading factor are aH the interval functions related λ. The result obtained here can be used to the fuzzy dynamic optimization design course of the helical gear system. The main advantage of this method is to introduce the concept of fuzzy number for the first time to the analysis of the gear system dynamics.
Amador-Angulo, Leticia; Mendoza, Olivia; Castro, Juan R; Rodríguez-Díaz, Antonio; Melin, Patricia; Castillo, Oscar
2016-09-09
A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm.
Systematic methods for the design of a class of fuzzy logic controllers
Yasin, Saad Yaser
2002-09-01
Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental
A new fuzzy-dynamic risk and reliability assessment
Directory of Open Access Journals (Sweden)
Majid Vaziri Sarashk
2014-06-01
Full Text Available The purpose of this article is to consider system safety and reliability analysts to evaluate the risk associated with item failure modes. The factors considered in traditional failure mode and effect analysis (FMEA for risk assessment are frequency of occurrence (O, severity (S and detectability (D of an item failure mode. Because of the subjective, qualitative and dynamic nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic and system dynamics methodology is proposed. In the proposed approach, severity is replaced by dependency parameter then, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and they are evaluated in fuzzy inference engine, which makes use of well-defined rule base and fuzzy logic operations to determine the value of parameters related to system’s transfer functions. The fuzzy conclusion is then defuzzified to get transfer function for risk and failure rate. The applicability of the proposed approach is investigated with the help of an illustrative case study from the automotive industry.
A self-organizing power system stabilizer using Fuzzy Auto-Regressive Moving Average (FARMA) model
Energy Technology Data Exchange (ETDEWEB)
Park, Y.M.; Moon, U.C. [Seoul National Univ. (Korea, Republic of). Electrical Engineering Dept.; Lee, K.Y. [Pennsylvania State Univ., University Park, PA (United States). Electrical Engineering Dept.
1996-06-01
This paper presents a self-organizing power system stabilizer (SOPSS) which use the Fuzzy Auto-Regressive Moving Average (FARMA) model. The control rules and the membership functions of the proposed logic controller are generated automatically without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-line by a self-organizing procedure. To show the effectiveness of the proposed controller, comparison with a conventional controller for one-machine infinite-bus system is presented.
2-D minimum fuzzy entropy method of image thresholding based on genetic algorithm
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley ofthe histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.
Cloud classification from satellite data using a fuzzy sets algorithm: A polar example
Key, J. R.; Maslanik, J. A.; Barry, R. G.
1988-01-01
Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.
Cloud classification from satellite data using a fuzzy sets algorithm - A polar example
Key, J. R.; Maslanik, J. A.; Barry, R. G.
1989-01-01
Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine like areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.
Cloud classification from satellite data using a fuzzy sets algorithm - A polar example
Key, J. R.; Maslanik, J. A.; Barry, R. G.
1989-01-01
Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine like areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.
Stability margin of linear systems with parameters described by fuzzy numbers.
Husek, Petr
2011-10-01
This paper deals with the linear systems with uncertain parameters described by fuzzy numbers. The problem of determining the stability margin of those systems with linear affine dependence of the coefficients of a characteristic polynomial on system parameters is studied. Fuzzy numbers describing the system parameters are allowed to be characterized by arbitrary nonsymmetric membership functions. An elegant solution, graphical in nature, based on generalization of the Tsypkin-Polyak plot is presented. The advantage of the presented approach over the classical robust concept is demonstrated on a control of the Fiat Dedra engine model and a control of the quarter car suspension model.
Research and Implementation of Automatic Fuzzy Garage Parking System Based on FPGA
2016-01-01
Because of many common scenes of reverse parking in real life, this paper presents a fuzzy controller which accommodates front and back adjustment of vehicle’s body attitude, and based on chaotic-genetic arithmetic to optimize the membership function of this controller, and get a vertical parking fuzzy controller whose simulation result is good .The paper makes the hardware-software embedded design for system based on Field-Programmable Gate Array (FPGA), and set up a 1:10 verification platfo...
Multi-item fuzzy inventory problem with space constraint via geometric programming method
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Mandal Kumar Nirmal
2006-01-01
Full Text Available In this paper, a multi-item inventory model with space constraint is developed in both crisp and fuzzy environment. A profit maximization inventory model is proposed here to determine the optimal values of demands and order levels of a product. Selling price and unit price are assumed to be demand-dependent and holding and set-up costs sock dependent. Total profit and warehouse space are considered to be vague and imprecise. The impreciseness in the above objective and constraint goals has been expressed by fuzzy linear membership functions. The problem is then solved using modified geometric programming method. Sensitivity analysis is also presented here.
DCT-Yager FNN: a novel Yager-based fuzzy neural network with the discrete clustering technique.
Singh, A; Quek, C; Cho, S Y
2008-04-01
Earlier clustering techniques such as the modified learning vector quantization (MLVQ) and the fuzzy Kohonen partitioning (FKP) techniques have focused on the derivation of a certain set of parameters so as to define the fuzzy sets in terms of an algebraic function. The fuzzy membership functions thus generated are uniform, normal, and convex. Since any irregular training data is clustered into uniform fuzzy sets (Gaussian, triangular, or trapezoidal), the clustering may not be exact and some amount of information may be lost. In this paper, two clustering techniques using a Kohonen-like self-organizing neural network architecture, namely, the unsupervised discrete clustering technique (UDCT) and the supervised discrete clustering technique (SDCT), are proposed. The UDCT and SDCT algorithms reduce this data loss by introducing nonuniform, normal fuzzy sets that are not necessarily convex. The training data range is divided into discrete points at equal intervals, and the membership value corresponding to each discrete point is generated. Hence, the fuzzy sets obtained contain pairs of values, each pair corresponding to a discrete point and its membership grade. Thus, it can be argued that fuzzy membership functions generated using this kind of a discrete methodology provide a more accurate representation of the actual input data. This fact has been demonstrated by comparing the membership functions generated by the UDCT and SDCT algorithms against those generated by the MLVQ, FKP, and pseudofuzzy Kohonen partitioning (PFKP) algorithms. In addition to these clustering techniques, a novel pattern classifying network called the Yager fuzzy neural network (FNN) is proposed in this paper. This network corresponds completely to the Yager inference rule and exhibits remarkable generalization abilities. A modified version of the pseudo-outer product (POP)-Yager FNN called the modified Yager FNN is introduced that eliminates the drawbacks of the earlier network and yi- elds
Statistical Methods for Fuzzy Data
Viertl, Reinhard
2011-01-01
Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be best described by using fuzzy numbers and fuzzy vectors respectively. Statistical analysis methods have to be adapted for the analysis of fuzzy data. In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy m
Andryani, Diyah Septi; Bustamam, Alhadi; Lestari, Dian
2017-03-01
Clustering aims to classify the different patterns into groups called clusters. In this clustering method, we use n-mers frequency to calculate the distance matrix which is considered more accurate than using the DNA alignment. The clustering results could be used to discover biologically important sub-sections and groups of genes. Many clustering methods have been developed, while hard clustering methods considered less accurate than fuzzy clustering methods, especially if it is used for outliers data. Among fuzzy clustering methods, fuzzy c-means is one the best known for its accuracy and simplicity. Fuzzy c-means clustering uses membership function variable, which refers to how likely the data could be members into a cluster. Fuzzy c-means clustering works using the principle of minimizing the objective function. Parameters of membership function in fuzzy are used as a weighting factor which is also called the fuzzier. In this study we implement hybrid clustering using fuzzy c-means and divisive algorithm which could improve the accuracy of cluster membership compare to traditional partitional approach only. In this study fuzzy c-means is used in the first step to find partition results. Furthermore divisive algorithms will run on the second step to find sub-clusters and dendogram of phylogenetic tree. To find the best number of clusters is determined using the minimum value of Davies Bouldin Index (DBI) of the cluster results. In this research, the results show that the methods introduced in this paper is better than other partitioning methods. Finally, we found 3 clusters with DBI value of 1.126628 at first step of clustering. Moreover, DBI values after implementing the second step of clustering are always producing smaller IDB values compare to the results of using first step clustering only. This condition indicates that the hybrid approach in this study produce better performance of the cluster results, in term its DBI values.
Multilayer perceptron, fuzzy sets, and classification
Pal, Sankar K.; Mitra, Sushmita
1992-01-01
A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.
Institute of Scientific and Technical Information of China (English)
Wang Yajun; Zhang Wohua; Jin Weiliang; Wu Changyu; Ren Dachun
2008-01-01
In order to address the complex uncertainties caused by interfacing between the fuzziness and randomness of the safety problem for embankment engineering projects, and to evaluate the safety of embankment engineering projects more scientifically and reasonably, this study presents the fuzzy logic modeling of the stochastic finite element method (SFEM) based on the harmonious finite element (HFE) technique using a first-order approximation theorem. Fuzzy mathematical models of safety repertories were introduced into the SFEM to analyze the stability of embankments and foundations in order to describe the fuzzy failure procedure for the random safety performance function. The fuzzy models were developed with membership functions with half depressed gamma distribution, half depressed normal distribution, and half depressed echelon distribution. The fuzzy stochastic mathematical algorithm was used to comprehensively study the local failure mechanism of the main embankment section near Jingnan in the Yangtze River in terms of numerical analysis for the probability integration of reliability on the random field affected by three fuzzy factors. The result shows that the middle region of the embankment is the principal zone of concentrated failure due to local fractures. There is also some local shear failure on the embankment crust. This study provides a referential method for solving complex multi-uncertainty problems in engineering safety analysis.
ASIC design of a digital fuzzy system on chip for medical diagnostic applications.
Roy Chowdhury, Shubhajit; Roy, Aniruddha; Saha, Hiranmay
2011-04-01
The paper presents the ASIC design of a digital fuzzy logic circuit for medical diagnostic applications. The system on chip under consideration uses fuzzifier, memory and defuzzifier for fuzzifying the patient data, storing the membership function values and defuzzifying the membership function values to get the output decision. The proposed circuit uses triangular trapezoidal membership functions for fuzzification patients' data. For minimizing the transistor count, the proposed circuit uses 3T XOR gates and 8T adders for its design. The entire work has been carried out using TSMC 0.35 µm CMOS process. Post layout TSPICE simulation of the whole circuit indicates a delay of 31.27 ns and the average power dissipation of the system on chip is 123.49 mW which indicates a less delay and less power dissipation than the comparable embedded systems reported earlier.
On fuzzy sampled-data control of chaotic systems via a time-dependent Lyapunov functional approach.
Wang, Zi-Peng; Wu, Huai-Ning
2015-04-01
In this paper, a novel approach to fuzzy sampled-data control of chaotic systems is presented by using a time-dependent Lyapunov functional. The advantage of the new method is that the Lyapunov functional is continuous at sampling times but not necessarily positive definite inside the sampling intervals. Compared with the existing works, the constructed Lyapunov functional makes full use of the information on the piecewise constant input and the actual sampling pattern. In terms of a new parameterized linear matrix inequality (LMI) technique, a less conservative stabilization condition is derived to guarantee the exponential stability for the closed-loop fuzzy sampled-data system. By solving a set of LMIs, the fuzzy sampled-data controller can be easily obtained. Finally, the chaotic Lorenz system and Rössler's system are employed to illustrate the feasibility and effectiveness of the proposed method.
Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.
Lin, Chuan-Kai
2005-04-01
A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.
GÜNER, Erdal
2007-01-01
Abstract. In this paper, .rstly some fundamental concepts are included re- lating to fuzzy topological spaces. Secondly, the fuzzy connected set is intro- duced. Finally, de.ning fuzzy contractible space, it is shown that X is a fuzzy contractible space if and only if X is fuzzy homotopic equivalent with a fuzzy single-point space.
Diagnosa Gangguan Perkembangan Anak Dengan Metode Fuzzy Expert System
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Diki Arisandi
2017-05-01
Full Text Available AbstrakAnak-anak dibawah umur 10 tahun merupakan fase yang sangat perlu diperhatikan perkembangannya oleh orang tua dan dibantu oleh pakar, apakah mengalami gangguan perkembangan atau tidak. Gangguan perkembangan anak dapat didiagnosis dari perilaku yang diperlihatkan oleh anak dengan cara observasi oleh seorang pakar psikologi anak. Hasil diagnosa dari observasi yang dilakukan beberapa pakar bisa saja berbeda. Hal ini membuat para orang tua menjadi kebingungan terhadap tindak lanjut yang harus dilakukan kepada anak mereka. Untuk mempermudah mendiagnosis gangguan perkembangan pada anak perlu adanya sebuah sistem pakar berbasis Fuzzy. Metode Fuzzy yang diterapkan didasari atas rentang logika berpikir manusia seperti dingin dan panas, tinggi dan rendah, dan lainnya. Diharapkan dengan adanya sistem pakar berbasis fuzzy ini, hasil diagnosa dapat menghasilkan solusi seperti nalar manusia dari sehingga didapatkan solusi untuk tindak lanjut pada gangguan anak. Kata kunci: Diagnosa, Fuzzy, Fungsi Keanggotaan, Gangguan perkembangan, Sistem Pakar. AbstractChildren under 10 years is a critical phase of their developmental and should be noticed by parents and assisted by experts, whether experiencing developmental disruption or not. Children developmental disruption can be diagnosed from behaviors shown by children by observation by a psychologist. Diagnosis results from observations made by some experts may be different. This makes the parents become confused about the follow-up to be done to their children. A Fuzzy-based expert system is needed to overcome the children developmental disruption. The applied Fuzzy method is based on the logical range of human thinking such as cold and hot, high and low, and others. With the fuzzy-based expert system, the diagnostic results can produce solutions such as human reasoning from that obtained a solution to following up on children disruption. Keywords: Diagnosis, Fuzzy, Membership Function, Developmental
Target Recognition Based on Fuzzy Dempster Data Fusion Method
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Yong Deng
2010-08-01
Full Text Available Data fusion technology is widely used in automatic target recognition system. Problems in data fusion system are complex by nature and can often be characterised by not only randomness but also by fuzziness. To accommodate complex natural problems with both types of uncertainties, it is profitable to construct a data fusion structure based on fuzzy set theory and Dempster Shafer evidence theory. In this paper, after representing both, the individual attribute of target in the model database and the sensor observation or report as fuzzy membership function, a likelihood function was constructed to deal with fuzzy data collected by each sensor. The method to determine basic probability assignments of each sensor report is proposed. Sensor reports are fused through classical Dempster combination rule. A numerical example is illustrated to show the target recognition application of the fuzzy-Dempster approach.Defence Science Journal, 2010, 60(5, pp.525-530, DOI:http://dx.doi.org/10.14429/dsj.60.576
POPFNN: A Pseudo Outer-product Based Fuzzy Neural Network.
Quek, C; Zhou, R W.
1996-12-01
A novel fuzzy neural network, called the pseudo outer-product based fuzzy neural network (POPFNN), is proposed in this paper. The functions performed by each layer in the proposed POPFNN strictly correspond to the inference steps in the truth value restriction method in fuzzy logic [[Mantaras (1990)] Approximate reasoning models, Ellis Horwood]. This correspondence gives it a strong theoretical basis. Similar to most of the existing fuzzy neural networks, the proposed POPFNN uses a self-organizing algorithm ([Kohonen, 1988], Self-organization and associative memories, Springer) to learn and initialize the membership functions of the input and output variables from a set of training data. However, instead of employing the popularly used competitive learning [[Kosko (1990)] IEEE Trans. Neural Networks, 3(5), 801], this paper proposes a novel pseudo outer-product (POP) learning algorithm to identify the fuzzy rules that are supported by the training data. The proposed POP learning algorithm is fast, reliable, and highly intuitive. Extensive experimental results and comparisons are presented at the end of the paper for discussion. Copyright 1996 Elsevier Science Ltd.
Vadiati, M; Asghari-Moghaddam, A; Nakhaei, M; Adamowski, J; Akbarzadeh, A H
2016-12-15
Due to inherent uncertainties in measurement and analysis, groundwater quality assessment is a difficult task. Artificial intelligence techniques, specifically fuzzy inference systems, have proven useful in evaluating groundwater quality in uncertain and complex hydrogeological systems. In the present study, a Mamdani fuzzy-logic-based decision-making approach was developed to assess groundwater quality based on relevant indices. In an effort to develop a set of new hybrid fuzzy indices for groundwater quality assessment, a Mamdani fuzzy inference model was developed with widely-accepted groundwater quality indices: the Groundwater Quality Index (GQI), the Water Quality Index (WQI), and the Ground Water Quality Index (GWQI). In an effort to present generalized hybrid fuzzy indices a significant effort was made to employ well-known groundwater quality index acceptability ranges as fuzzy model output ranges rather than employing expert knowledge in the fuzzification of output parameters. The proposed approach was evaluated for its ability to assess the drinking water quality of 49 samples collected seasonally from groundwater resources in Iran's Sarab Plain during 2013-2014. Input membership functions were defined as "desirable", "acceptable" and "unacceptable" based on expert knowledge and the standard and permissible limits prescribed by the World Health Organization. Output data were categorized into multiple categories based on the GQI (5 categories), WQI (5 categories), and GWQI (3 categories). Given the potential of fuzzy models to minimize uncertainties, hybrid fuzzy-based indices produce significantly more accurate assessments of groundwater quality than traditional indices. The developed models' accuracy was assessed and a comparison of the performance indices demonstrated the Fuzzy Groundwater Quality Index model to be more accurate than both the Fuzzy Water Quality Index and Fuzzy Ground Water Quality Index models. This suggests that the new hybrid fuzzy
Energy Technology Data Exchange (ETDEWEB)
Gracios-Marin, C.A.; Nuno-de-la-Parra, P.; Vega-Lebrum, Carlos [Universidad Popular Autonoma del Estado de Puebla, 21 Sur 1103 Col, Puebla, Pue, 72220 Santiago (Mexico); Munoz-Hernandez, G.A.; Estevez-Carreon, J. [Instituto Tecnologico de Puebla, Av. Tecnologico 420. Col. Maravillas, Puebla, Pue (Mexico); Diaz-Sanchez, A. [INAOE. - Luis Enrique Erro. No. 1, Tonantzintla, Puebla (Mexico)
2009-07-15
A new decision feedback extension (DFE) and an alternative application to schedule industrial processes are presented. The DFE is defined as a recursive decision feedback extension (RDFE), where the recursive part is developed to assign the probability of occurrence in the operation of a set of machines working together using an objective function of production. The fundaments of fuzzy factors and the principle of maximum membership function are used to develop the new extension. At last, RDFE is proposed to generate a fuzzy scheduler, which is used to apply an intelligent control scheme to a hydropower station. (author)
Directory of Open Access Journals (Sweden)
Oscar Castillo
2013-01-01
Full Text Available Neural networks (NNs, type-1 fuzzy logic systems (T1FLSs, and interval type-2 fuzzy logic systems (IT2FLSs have been shown to be universal approximators, which means that they can approximate any nonlinear continuous function. Recent research shows that embedding an IT2FLS on an NN can be very effective for a wide number of nonlinear complex systems, especially when handling imperfect or incomplete information. In this paper we show, based on the Stone-Weierstrass theorem, that an interval type-2 fuzzy neural network (IT2FNN is a universal approximator, which uses a set of rules and interval type-2 membership functions (IT2MFs for this purpose. Simulation results of nonlinear function identification using the IT2FNN for one and three variables and for the Mackey-Glass chaotic time series prediction are presented to illustrate the concept of universal approximation.
Development of Fuzzy Logic System to Predict the SAW Weldment Shape Profiles
Institute of Scientific and Technical Information of China (English)
H.K.Narang; M.M.Mahapatra; P.K.Jha; P.Biswas
2012-01-01
A fuzzy model was presented to predict the weldment shape profile of submerged arc welds (SAW)including the shape of heat affected zone (HAZ).The SAW bead-on-plates were welded by following a full factorial design matrix.The design marx consisted of three levels of input welding process parameters.The welds were cross-sectioned and etched,and the zones were measured.A mapping technique was used to measure the various segments of the weld zones.These mapped zones were used to build a fuzzy logic model.The membership functions of the fuzzy model were chosen for the accurate prediction of the weld zone.The fuzzy model was further tested for a set of test case data.The weld zone predicted by the fuzzy logic model was compared with the experimentally obtained shape profiles and close agreement between the two was noted.The mapping technique developed for the weld zones and the fuzzy logic model can be used for on-line control of the SAW process.From the SAW fuzzy logic model an estimation of the fusion and HAZ can also be developed.
Directory of Open Access Journals (Sweden)
Wei Zhang
2014-01-01
Full Text Available The underwater recovery of autonomous underwater vehicles (AUV is a process of 6-DOF motion control, which is related to characteristics with strong nonlinearity and coupling. In the recovery mission, the vehicle requires high level control accuracy. Considering an AUV called BSAV, this paper established a kinetic model to describe the motion of AUV in the horizontal plane, which consisted of nonlinear equations. On the basis of this model, the main coupling variables were analyzed during recovery. Aiming at the strong coupling problem between the heading control and sway motion, we designed a decoupling compensator based on the fuzzy theory and the decoupling theory. We analyzed to the rules of fuzzy compensation, the input and output membership functions of fuzzy compensator, through compose operation and clear operation of fuzzy reasoning, and obtained decoupling compensation quantity. Simulation results show that the fuzzy decoupling controller effectively reduces the overshoot of the system, and improves the control precision. Through the water tank experiments and analysis of experimental data, the effectiveness and feasibility of AUV recovery movement coordinated control based on fuzzy decoupling method are validated successful, and show that the fuzzy decoupling control method has a high practical value in the recovery mission.
Aplikasi Penentuan Tarif Listrik Menggunakan Metode Fuzzy Sugeno
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Hari Santosa
2016-01-01
Full Text Available This reasearch applied Sugeno fuzzy method for determining electricity tariff based on the data of electric customers 450 VA and 900 VA from PLN APJ South Semarang. Tariff of PLN clasifications calculation is done in a progressive way with three block/ classification tariff . This method is considered less representative because many users who consume less or more electrical energy (kWh in one block/class charged or valued equally. Electric customer data during the 5 months from May to September 2012, which contains a large kWh consumption is then calculated by means of progressive tariff . The data is then performed clustering with FCM method (Mean C Fuzz into 5 groups, thus obtained cluster centers or power usage and price rates. The power usage and price of the cluster centers are used as a reference manufacture and fed into the membership function of fuzzy inference system built by Sugeno method. This research used the triangular shape of the curve membership function.The system built in the form of application Sugeno fuzzy method which is tested by inserting a number of sample test data . The results are the tariff to be paid by electricity customers . The tariff is resulting from the calculation of the system compared to the tariff calculated in a progressive way of PLN. The difference in total tariff to customers for power of 450 VA Rp 93.3107 or 0.004%, while for the 900 VA at Rp 3503.2 or 0.12 %. The tariff were calculated using Sugeno fuzzy method from this research is more fair to the consumer there is increase in revenue for PLN. Keywords : Fuzzy Sugeno; Tariff classification; Calculation
Rainfall events prediction using rule-based fuzzy inference system
Asklany, Somia A.; Elhelow, Khaled; Youssef, I. K.; Abd El-wahab, M.
2011-07-01
We are interested in rainfall events prediction by applying rule-based reasoning and fuzzy logic. Five parameters: relative humidity, total cloud cover, wind direction, temperature and surface pressure are the input variables for our model, each has three membership functions. The data used is twenty years METAR data for Cairo airport station (HECA) [1972-1992] 30° 3' 29″ N, 31° 13' 44″ E. and five years METAR data for Mersa Matruh station (HEMM) 31° 20' 0″ N, 27° 13' 0″ E. Different models for each station were constructed depending on the available data sets. Among the overall 243 possibilities we have based our models on one hundred eighteen fuzzy IF-THEN rules and fuzzy reasoning. The output variable which has four membership functions, takes values from zero to one hundred corresponding to the percentage for rainfall events given for every hourly data. We used two skill scores to verify our results, the Brier score and the Friction score. The results are in high agreements with the recorded data for the stations with increasing in output values towards the real time rain events. All implementation are done with MATLAB 7.9.
Poverty Level of Households: A Multidimensional Approach Based on Fuzzy Mathematics
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Amitava Chatterjee
2014-12-01
Full Text Available In recent years, extensive research jobs have been developed on the definition, measurement and analyzing of poverty. Poverty is a multidimensional phenomenon, thus a number of challenges appears measuring it. The fuzzy set theoretic approach has been used to measure the poverty and to classify the difference between poor and non-poor households. This paper aims at proposing a new methodology to measure the poverty index in fuzzy environment via a two-step membership function. The concept of one poverty line is chalked out first and then a general method is developed to split the poverty index. Linguistic variables are used for the attributes to find the membership values of the households against the attributes and to grade the attributes. The effectiveness and usefulness of the proposed method is numerically illustrated through a case study for rural household people living in remote rural areas of India.
Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y
2014-05-01
This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach.
A Different Web-Based Geocoding Service Using Fuzzy Techniques
Pahlavani, P.; Abbaspour, R. A.; Zare Zadiny, A.
2015-12-01
Geocoding - the process of finding position based on descriptive data such as address or postal code - is considered as one of the most commonly used spatial analyses. Many online map providers such as Google Maps, Bing Maps and Yahoo Maps present geocoding as one of their basic capabilities. Despite the diversity of geocoding services, users usually face some limitations when they use available online geocoding services. In existing geocoding services, proximity and nearness concept is not modelled appropriately as well as these services search address only by address matching based on descriptive data. In addition there are also some limitations in display searching results. Resolving these limitations can enhance efficiency of the existing geocoding services. This paper proposes the idea of integrating fuzzy technique with geocoding process to resolve these limitations. In order to implement the proposed method, a web-based system is designed. In proposed method, nearness to places is defined by fuzzy membership functions and multiple fuzzy distance maps are created. Then these fuzzy distance maps are integrated using fuzzy overlay technique for obtain the results. Proposed methods provides different capabilities for users such as ability to search multi-part addresses, searching places based on their location, non-point representation of results as well as displaying search results based on their priority.
A DIFFERENT WEB-BASED GEOCODING SERVICE USING FUZZY TECHNIQUES
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P. Pahlavani
2015-12-01
Full Text Available Geocoding – the process of finding position based on descriptive data such as address or postal code - is considered as one of the most commonly used spatial analyses. Many online map providers such as Google Maps, Bing Maps and Yahoo Maps present geocoding as one of their basic capabilities. Despite the diversity of geocoding services, users usually face some limitations when they use available online geocoding services. In existing geocoding services, proximity and nearness concept is not modelled appropriately as well as these services search address only by address matching based on descriptive data. In addition there are also some limitations in display searching results. Resolving these limitations can enhance efficiency of the existing geocoding services. This paper proposes the idea of integrating fuzzy technique with geocoding process to resolve these limitations. In order to implement the proposed method, a web-based system is designed. In proposed method, nearness to places is defined by fuzzy membership functions and multiple fuzzy distance maps are created. Then these fuzzy distance maps are integrated using fuzzy overlay technique for obtain the results. Proposed methods provides different capabilities for users such as ability to search multi-part addresses, searching places based on their location, non-point representation of results as well as displaying search results based on their priority.
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M. Satheesh
2014-01-01
Full Text Available The high pressure differential across the wall of pressure vessels is potentially dangerous and has caused many fatal accidents in the history of their development and operation. For this reason the structural integrity of weldments is critical to the performance of pressure vessels. In recent years much research has been conducted to the study of variations in welding parameters and consumables on the mechanical properties of pressure vessel steel weldments to optimize weld integrity and ensure pressure vessels are safe. The quality of weld is a very important working aspect for the manufacturing and construction industries. Because of high quality and reliability, Submerged Arc Welding (SAW is one of the chief metal joining processes employed in industry. This paper addresses the application of desirability function approach combined with fuzzy logic analysis to optimize the multiple quality characteristics (bead reinforcement, bead width, bead penetration and dilution of submerged arc welding process parameters of SA 516 Grade 70 steels(boiler steel. Experiments were conducted using Taguchi’s L27 orthogonal array with varying the weld parameters of welding current, arc voltage, welding speed and electrode stickout. By analyzing the response table and response graph of the fuzzy reasoning grade, optimal parameters were obtained. Solutions from this method can be useful for pressure vessel manufacturers and operators to search an optimal solution of welding condition.
Fuzzy Logic Water Quality Index and Importance of Water Quality Parameters
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Raman Bai. V
2009-01-01
Full Text Available Determination of status of water quality of a river or any other water sources is highly indeterminate. It is necessary to have a competent model to predict the status of water quality and to advice for type of water treatment for meeting different demands. One such model (UNIQ2007 is developed as an application software in water quality engineering. The unit operates in a fuzzy logic mode including a fuzzification engine receiving a plurality of input variables on its input and being adapted to compute membership function parameters. A processor engine connected downstream of the fuzzification unit will produce fuzzy set, based on fuzzy variable viz. DO, BOD, COD, AN, SS and pH. It has a defuzzification unit operative to translate the inference results into a discrete crisp value of WQI. The UNIQ2007 contains a first memory device connected to the fuzzification unit and containing the set of membership functions, a secondary memory device connected to the defuzzification unit and containing the set of crisp value which appear in the THEN part of the fuzzy rules and an additional memory device connected to the defuzzification unit. More advantageously, UINQ2007 is constructed with control elements having dynamic fuzzy logic properties wherein target non-linearity can be input to result in a perfect evaluation of water quality. The development of the fuzzy model with one river system is explained in this paper. Further the model has been evaluated with the data from few rivers in Malaysia, India and Thailand. This water quality assessor probe can provide better quality index or identify the status of river with 90% perfection. Presently, WQI in most of the countries is referring to physic-chemical parameters only due to great efforts needed to quantify the biological parameters. This study ensures a better method to include pathogens into WQI due to superior capabilities of fuzzy logic in dealing with non-linear, complex and uncertain systems.
Fuzzy Perfect Mappings and Q-Compactness in Smooth Fuzzy Topological Spaces
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C. Kalaivani
2014-03-01
Full Text Available We point out that the product of two fuzzy closed sets of smooth fuzzy topological spaces need not be fuzzy closed with respect to the the existing notion of product smooth fuzzy topology. To get this property, we introduce a new suitable product smooth fuzzy topology. We investigate whether F1×F2 and (F,H are weakly smooth fuzzy continuity whenever F1, F2, F and H are weakly smooth fuzzy continuous. Using this new product smooth fuzzy topology, we define smooth fuzzy perfect mapping and prove that composition of two smooth fuzzy perfect mappings is smooth fuzzy perfect under some additional conditions. We also introduce two new notions of compactness called Q-compactness and Q-α-compactness; and discuss the compactness of the image of a Q-compact set (Q-α-compact set under a weakly smooth fuzzy continuous function ((α,β-weakly smooth fuzzy continuous function.
Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms
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Arindam Chaudhuri
2015-01-01
Full Text Available Intuitionistic fuzzy sets (IFSs provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic fuzzy sets (IVIFSs leading to interval valued intuitionistic fuzzy possibilistic C means (IVIFPCM. The clustering algorithm has membership and nonmembership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. The experiments are performed on both real and simulated datasets. It generates valuable information and produces overlapped clusters with different membership degrees. It takes into account inherent uncertainty in information captured by IFSs. Some advantages of algorithms are simplicity, flexibility, and low computational complexity. The algorithm is evaluated through cluster validity measures. The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns. The algorithm maintains appreciable performance compared to other methods in terms of pureness ratio.
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Jinjun Tang
Full Text Available Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN, two learning processes are proposed: (1 a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2 a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE, root mean square error (RMSE, and mean absolute relative error (MARE are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR, instantaneous model (IM, linear model (LM, neural network (NN, and cumulative plots (CP.
Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai
2016-01-01
Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639
A Multiagent Transfer Function Neuroapproach to Solve Fuzzy Riccati Differential Equations
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Mohammad Shazri Shahrir
2014-01-01
Full Text Available A numerical solution of fuzzy quadratic Riccati differential equation is estimated using a proposed new approach for neural networks (NN. This proposed new approach provides different degrees of polynomial subspaces for each of the transfer function. This multitude of transfer functions creates unique “agents” in the structure of the NN. Hence it is named as multiagent neuroapproach (multiagent NN. Previous works have shown that results using Runge-Kutta 4th order (RK4 are reliable. The results can be achieved by solving the 1st order nonlinear differential equation (ODE that is found commonly in Riccati differential equation. Multiagent NN shows promising results with the advantage of continuous estimation and improved accuracy that can be produced over Mabood et al. (2013, RK-4, and the existing neuromethod (NM. Numerical examples are discussed to illustrate the proposed method.
模糊语言及其语用功能%Fuzzy Language and Its Pragmatic Function
Institute of Scientific and Technical Information of China (English)
苏焕莉
2011-01-01
As a linguistic phenomenon, fuzzy language is widely existing in human communication. This paper mainly examines the basic features of the fuzzy language, the reasons of its existence as well as its functions from the point of view of cognitive linguistics, rhetoric and pragmatics ajid also the problems we should pay close attention to during the use of fuzzy language.%作为一种语言现象,模糊语言广泛存在于人们的言语交际中.本文主要从认识语言学、修辞学和语用学的角度探讨模糊语言的基本特点、产生原因、语用功能及在模糊语言中应注意的问题.
Institute of Scientific and Technical Information of China (English)
周菁; 戴冠中; 周婷婷
2009-01-01
Through the expression of text characteristic based on fuzzy qualifier and then definition of fuzzy feature using the fuzzy function, represented the text as the imposed membership degree limiting text feature vector. Constructuring membership degree limiting class feature matrix, and mapping each group of texts belong to the same class to its class expectation vector. All of class expectation vectors constructed the membership degree limiting characteristic VSM. Based on that, presented a new text-classification model and the experiment shows that the model is efficient.%通过文档基于模糊限定词的特征表达,定义特征的模糊函数,将文档表示为隶属度限幅的特征向量,构造文本集隶属度限幅的类特征矩阵,将每一类文本集映射为类期望向量,所有类期望向量便构成了隶属度限幅的特征VSM.在此基础上设计了一种新的文本分类模型.实验结果证明,该分类模型能有效实现文本分类.
Yafei Song; Xiaodan Wang; Lei Lei; Aijun Xue
2014-01-01
As a generation of ordinary fuzzy set, the concept of intuitionistic fuzzy set (IFS), characterized both by a membership degree and by a nonmembership degree, is a more flexible way to cope with the uncertainty. Similarity measures of intuitionistic fuzzy sets are used to indicate the similarity degree between intuitionistic fuzzy sets. Although many similarity measures for intuitionistic fuzzy sets have been proposed in previous studies, some of those cannot satisfy the axioms of similarity ...
Energy Technology Data Exchange (ETDEWEB)
Barragan M, A.M.; Martin del Campo M, C.; Palomera P, M.A. [FI-UNAM, Circuito Exterior, Ciudad Universitaria, 04510 Mexico D.F. (Mexico)]. E-mail: ale_bar_m@yahoo.com.mx
2005-07-01
A methodology based on Fuzzy Logic for the construction of the objective function of the optimization problems of nuclear fuel is described. It was created an inference system that responds, in certain form, as a human expert when it has the task of qualifying different radial designs of fuel cells. Specifically it is detailed how an inference system based based on Fuzzy Logic that has five enter variables and one exit variable was built, which corresponds to the objective function for the radial design of a fuel cell for a BWR. The use of Fuzzy with Mat lab offered the visualization capacity of the exit variable in function of one or two enter variables at the same time. This allowed to build, in appropriate way, the combination of the inference rules and the membership functions of those diffuse sets used for each one of the enter variables. The obtained objective function was used in an optimization process based on Taboo search. The new methodology was proven for the design of a cell used in a fuel assemble of the Laguna Verde reactor obtaining excellent results. (Author)
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Ali Mohaghar
2014-02-01
Based on the information accessible for the researchers, this is one of the first works which evaluates the key factors of successful knowledge management through fuzzy quality function deployment approach. It is expected that the proposed method would represent appropriate tools for enterprises which have decided to implement knowledge management because it prioritizes the critical success factors based on the knowledge management outcomes.
a fuzzy homomorphic algori omomorphic algorithm for image ...
African Journals Online (AJOL)
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nalysis of a novel Fuzzy Homomorphic image enhancement technique is p garithmic ... c Filtering, Illumination correction, Low Contrast Images, Enhancement, F g method is ..... Higher FE values mean more membership values are closer to 1 ...
Fuzzy Set Methods for Object Recognition in Space Applications
Keller, James M. (Editor)
1992-01-01
Progress on the following four tasks is described: (1) fuzzy set based decision methodologies; (2) membership calculation; (3) clustering methods (including derivation of pose estimation parameters), and (4) acquisition of images and testing of algorithms.
Fuzzy Decision-Making Approach in Geometric Programming for a Single Item EOQ Model
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Monalisha Pattnaik
2015-06-01
Full Text Available Background and methods: Fuzzy decision-making approach is allowed in geometric programming for a single item EOQ model with dynamic ordering cost and demand-dependent unit cost. The setup cost varies with the quantity produced/purchased and the modification of objective function with storage area in the presence of imprecisely estimated parameters are investigated. It incorporates all concepts of a fuzzy arithmetic approach, the quantity ordered, and demand per unit compares both fuzzy geometric programming technique and other models for linear membership functions. Results and conclusions: Investigation of the properties of an optimal solution allows developing an algorithm whose validity is illustrated through an example problem and the results discu ssed. Sensitivity analysis of the optimal solution is also studied with respect to changes in different parameter values.
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J. Fang
1998-01-01
Full Text Available An approach to the optimum design of structures, in which uncertainties with a fuzzy nature in the magnitude of the loads are considered, is proposed in this study. The optimization process under fuzzy loads is transformed into a fuzzy optimization problem based on the notion of Werners' maximizing set by defining membership functions of the objective function and constraints. In this paper, Werner's maximizing set is defined using the results obtained by first conducting an optimization through anti-optimization modeling of the uncertain loads. An example of a ten-bar truss is used to illustrate the present optimization process. The results are compared with those yielded by other optimization methods.
An empirical fuzzy multifactor dimensionality reduction method for detecting gene-gene interactions.
Leem, Sangseob; Park, Taesung
2017-03-14
Detection of gene-gene interaction (GGI) is a key challenge towards solving the problem of missing heritability in genetics. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. MDR reduces the dimensionality of multi-factor by means of binary classification into high-risk (H) or low-risk (L) groups. Unfortunately, this simple binary classification does not reflect the uncertainty of H/L classification. Thus, we proposed Fuzzy MDR to overcome limitations of binary classification by introducing the degree of membership of two fuzzy sets H/L. While Fuzzy MDR demonstrated higher power than that of MDR, its performance is highly dependent on the several tuning parameters. In real applications, it is not easy to choose appropriate tuning parameter values. In this work, we propose an empirical fuzzy MDR (EF-MDR) which does not require specifying tuning parameters values. Here, we propose an empirical approach to estimating the membership degree that can be directly estimated from the data. In EF-MDR, the membership degree is estimated by the maximum likelihood estimator of the proportion of cases(controls) in each genotype combination. We also show that the balanced accuracy measure derived from this new membership function is a linear function of the standard chi-square statistics. This relationship allows us to perform the standard significance test using p-values in the MDR framework without permutation. Through two simulation studies, the power of the proposed EF-MDR is shown to be higher than those of MDR and Fuzzy MDR. We illustrate the proposed EF-MDR by analyzing Crohn's disease (CD) and bipolar disorder (BD) in the Wellcome Trust Case Control Consortium (WTCCC) dataset. We propose an empirical Fuzzy MDR for detecting GGI using the maximum likelihood of the proportion of cases(controls) as the membership degree of the genotype combination. The program written in R for EF-MDR is available at http://statgen.snu.ac.kr/software/EF-MDR .
A Novel Weak Fuzzy Solution for Fuzzy Linear System
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Soheil Salahshour
2016-03-01
Full Text Available This article proposes a novel weak fuzzy solution for the fuzzy linear system. As a matter of fact, we define the right-hand side column of the fuzzy linear system as a piecewise fuzzy function to overcome the related shortcoming, which exists in the previous findings. The strong point of this proposal is that the weak fuzzy solution is always a fuzzy number vector. Two complex and non-complex linear systems under uncertainty are tested to validate the effectiveness and correctness of the presented method.
An adaptive fuzzy logic controller for robot-manipulator
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Tran Thu Ha
2008-11-01
Full Text Available In this paper, an adaptive fuzzy controller is designed for the robot-manipulator. The synthesized controller ensures that 1 the close-loop system is globally stable and 2 the tracking error converges to zero asymptotically and a cost function is minimized. The fuzzy controller is synthesized from a collection of IF-THEN rules. The parameters of the membership functions characterizing the linguistic terms change according to some adaptive law for the purpose of controlling a plant to track a reference trajectory. The proposed control scheme is demonstrated in a typical nonlinear plant two link manipulator. The computer simulation of control is done by the language MATLAB. The results of simulation show that the adaptipresented results are analyzed.
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Dennis M Bowie
2004-01-01
Full Text Available In an effort to gather feedback from Canadian Thoracic Society (CTS members, the Executive sent out a survey to the membership and received replies from approximately one-third of the membership. This was an attempt to look at the role of the CTS and what the members wanted from the CTS.
Wei, Chih-Chiang; Hsu, Nien-Sheng
2008-02-01
This article compares the decision-tree algorithm (C5.0), neural decision-tree algorithm (NDT) and fuzzy decision-tree algorithm (FIDs) for addressing reservoir operations regarding water supply during normal periods. The conventional decision-tree algorithm, such as ID3 and C5.0, executes rapidly and can easily be translated into if-then-else rules. However, the C5.0 algorithm cannot discover dependencies among attributes and cannot treat the non-axis-parallel class boundaries of data. The basic concepts of the two algorithms presented are: (1) NDT algorithm combines the neural network technologies and conventional decision-tree algorithm capabilities, and (2) FIDs algorithm extends to apply fuzzy sets for all attributes with membership function grades and generates a fuzzy decision tree. In order to obtain higher classification rates in FIDs, the flexible trapezoid fuzzy sets are employed to define membership functions. Furthermore, an intelligent genetic algorithm is utilized to optimize the large number of variables in fuzzy decision-tree design. The applicability of the presented algorithms is demonstrated through a case study of the Shihmen Reservoir system. A network flow optimization model for analyzing long-term supply demand is employed to generate the input-output patterns. Findings show superior performance of the FIDs model in contrast with C5.0, NDT and current reservoir operating rules.
Lei, Qian
2017-01-01
This book offers a comprehensive and systematic review of the latest research findings in the area of intuitionistic fuzzy calculus. After introducing the intuitionistic fuzzy numbers’ operational laws and their geometrical and algebraic properties, the book defines the concept of intuitionistic fuzzy functions and presents the research on the derivative, differential, indefinite integral and definite integral of intuitionistic fuzzy functions. It also discusses some of the methods that have been successfully used to deal with continuous intuitionistic fuzzy information or data, which are different from the previous aggregation operators focusing on discrete information or data. Mainly intended for engineers and researchers in the fields of fuzzy mathematics, operations research, information science and management science, this book is also a valuable textbook for postgraduate and advanced undergraduate students alike.
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A.K. Parida
2016-09-01
Full Text Available In this paper Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system is presented for the prediction and analysis of financial and electrical energy market data. The normally used TSK-type feedforward fuzzy neural network is unable to take the full advantage of the use of the linear fuzzy rule base in accurate input–output mapping and hence the consequent part of the rule base is made nonlinear using polynomial or arithmetic basis functions. Further the Chebyshev polynomial functions provide an expanded nonlinear transformation to the input space thereby increasing its dimension for capturing the nonlinearities and chaotic variations in financial or energy market data streams. Also the locally recurrent neuro-fuzzy information system (LRNFIS includes feedback loops both at the firing strength layer and the output layer to allow signal flow both in forward and backward directions, thereby making the LRNFIS mimic a dynamic system that provides fast convergence and accuracy in predicting time series fluctuations. Instead of using forward and backward least mean square (FBLMS learning algorithm, an improved Firefly-Harmony search (IFFHS learning algorithm is used to estimate the parameters of the consequent part and feedback loop parameters for better stability and convergence. Several real world financial and energy market time series databases are used for performance validation of the proposed LRNFIS model.
Asilturk, Ilhan; AlperInce, Mehmet
2017-06-01
This study includes comparison with experimental results of models and modelling with fuzzy logic of the effect on surface roughness of cutting parameters (rotational speed (n), feed rate (f), depth of cut (a) and tool tip radius (r)) in CNC turning of Co28Cr6Mo wrought steels. Fuzzy logic modelswere established that can determine the optimum rotational speed, feed rate, depth of cut and tool tip radius for surface roughness (Ra) according to the hardness of material and type of cutting tool. In the model created using fuzzy logic, membership functions and foot widths of input parameters and output parameter were utilized. In the rule base, triangular (trim-f) membership functions were selected by the Mamdani approach. The results obtained using this fuzzymodel and experimental results were interpreted and compared with 2dimensional graphics.
Gray, DeLeon L
2017-04-01
Education researchers have consistently linked students' perceptions of "fitting in" at school with patterns of motivation and positive emotions. This study proposes that "standing out" is also helpful for producing these outcomes, and that standing out works in concert with perceptions of fitting in. In a sample of 702 high school students nested within 33 classrooms, principal components analysis and confirmatory factor analysis were each conducted on half of the sample. Results support the proposed structure of measures of standing out and fitting in. Multilevel latent profile analysis was then used to classify students into four profiles of standing out while fitting in (SOFI): Unfulfilled, Somewhat Fulfilled, Nearly Fulfilled, and Fulfilled. A multinomial logistic regression revealed that students of color and those on who paid free/reduced prices lunch were overrepresented in the Unfulfilled and Somewhat Fulfilled profiles. A multilevel path analysis was then performed to assess the direct and indirect associations of profile membership with measures of task value and achievement emotions. Relative to the other profiles, students in the Fulfilled SOFI Profile express greater psychological membership in their classrooms and, in turn, express higher valuing of academic material (i.e., intrinsic value, utility value, and attainment value) and more positive achievement emotions (i.e., more enjoyment and pride; less boredom, hopelessness, and shame). This investigation provides critical insights on the potential benefits of structuring academic learning environments to foster feelings of distinctiveness among adolescents; and has implications for cultivating identities and achievement motivation in academic settings. Copyright © 2017 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.
A fuzzy behaviorist approach to sensor-based robot control
Energy Technology Data Exchange (ETDEWEB)
Pin, F.G.
1996-05-01
Sensor-based operation of autonomous robots in unstructured and/or outdoor environments has revealed to be an extremely challenging problem, mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment, i.e., lack of full knowledge of the environment characteristics and dynamics. An approach. which we have named the {open_quotes}Fuzzy Behaviorist Approach{close_quotes} (FBA) is proposed in an attempt to remedy some of these difficulties. This approach is based on the representation of the system`s uncertainties using Fuzzy Set Theory-based approximations and on the representation of the reasoning and control schemes as sets of elemental behaviors. Using the FBA, a formalism for rule base development and an automated generator of fuzzy rules have been developed. This automated system can automatically construct the set of membership functions corresponding to fuzzy behaviors. Once these have been expressed in qualitative terms by the user. The system also checks for completeness of the rule base and for non-redundancy of the rules (which has traditionally been a major hurdle in rule base development). Two major conceptual features, the suppression and inhibition mechanisms which allow to express a dominance between behaviors are discussed in detail. Some experimental results obtained with the automated fuzzy, rule generator applied to the domain of sensor-based navigation in aprion unknown environments. using one of our autonomous test-bed robots as well as a real car in outdoor environments, are then reviewed and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using the {open_quotes}Fuzzy Behaviorist{close_quotes} concepts.
Akintola, Olayiwola Akin; Sangodoyin, Abimbola Yisau; Agunbiade, Foluso Oyedotun
2016-07-01
This study presents the effects of environmental pollution on the quality of domestic roof-harvested rainwater (DRHRW) using fuzzy comprehensive assessment (FCA). Seven metals (Cu, Cd, Pb, Zn, Fe, Ca, and Mg) and six water-quality parameters (Acidity, PO4 (3-), SO4 (2-), NO3 (-) , Cl(-), and pH) were investigated in DRHRW sampled from 12 sampling points each from Ibadan (residential) and Lagos (industrial) environments, Nigeria. The results of these parameters were formulated into membership function fuzzy matrices based on four contamination classifications of high, marginal, low, and poor qualities using regulatory limits as criteria. The products membership function matrices and weight matrices generated indices that classified the degree of anthropogenic activity impact on the sites. Results of FCA classified the DRHRW from residential environment as between high and marginal quality, whereas DRHRW from industrial environment is classified between marginal and low quality. Lead and Cd were major contaminants of concern found in these harvested water.
Optimal Power Flow With UPFC Using Fuzzy- PSO With NonSmooth Fuel Cost Function
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A.Immanuel
2015-05-01
Full Text Available This paper presents an efficient and reliable evolutionary based approach to solve the Optimal Power Flow problem in electrical power network. The Particle Swarm Optimization method is used to solve optimal power Flow problem in power system by incorporating a powerful and most versatile Flexible Alternating Current Transmission Systems device such as Unified power Flow Controller. It is a new device in FACTS family and has great flexibility that can control Active power, Reactive power and voltage magnitudes simultaneously. In this paper optimal location is find out using Fuzzy approach and control settings of UPFC are determined by PSO. The proposed approach is examined on IEEE-30 bus system with different objective function that reflects fuel cost minimization and fuel cost with valve point effects. The test results show the effectiveness of robustness of the proposed approachcompared with the existing results in the literature.
Zhang, Hongbin; Feng, Gang
2008-10-01
This paper is concerned with stability analysis and H(infinity) decentralized control of discrete-time fuzzy large-scale systems based on piecewise Lyapunov functions. The fuzzy large-scale systems consist of J interconnected discrete-time Takagi-Sugeno (T-S) fuzzy subsystems, and the stability analysis is based on Lyapunov functions that are piecewise quadratic. It is shown that the stability of the discrete-time fuzzy large-scale systems can be established if a piecewise quadratic Lyapunov function can be constructed, and moreover, the function can be obtained by solving a set of linear matrix inequalities (LMIs) that are numerically feasible. The H(infinity) controllers are also designed by solving a set of LMIs based on these powerful piecewise quadratic Lyapunov functions. It is demonstrated via numerical examples that the stability and controller synthesis results based on the piecewise quadratic Lyapunov functions are less conservative than those based on the common quadratic Lyapunov functions.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
Akhavan, P.; Karimi, M.; Pahlavani, P.
2014-10-01
Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
Directory of Open Access Journals (Sweden)
P. Akhavan
2014-10-01
Full Text Available Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
Fuzzy Logic Application in Boron and Cadmium Analysis in U3O8 use of Emission Spectrograph Method
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S. Simbolon
2011-04-01
Full Text Available Boron and cadmium in U3O8 have been analyzed with emission spectrograph. Three inputs of emission spectrograph, current (A, exposure time (second and gap between electrodes (mm were varied. Two outputs, boron and cadmium lines intensities respectively were selected and measured. Thirteen experiments have been carried out and data found were calculated by fuzzy logic Mamdani-type. Three and five memberships functions of straight-line (Triangular, Trapezoidal, Generalized-bell and Gaussian curve were used to analyze the found data. The result found that five memberships functions had less error percentage range than three memberships functions of straight-line (Triangular, Trapezoidal, Generalized-bell and Gaussian curve. The error percentage range of cadmium analysis was wider than boron analysis with this method. Analysis of cadmium in U3O8 with this method needs much exposure time compare to analysis of boron
Design of an iterative auto-tuning algorithm for a fuzzy PID controller
Saeed, Bakhtiar I.; Mehrdadi, B.
2012-05-01
Since the first application of fuzzy logic in the field of control engineering, it has been extensively employed in controlling a wide range of applications. The human knowledge on controlling complex and non-linear processes can be incorporated into a controller in the form of linguistic terms. However, with the lack of analytical design study it is becoming more difficult to auto-tune controller parameters. Fuzzy logic controller has several parameters that can be adjusted, such as: membership functions, rule-base and scaling gains. Furthermore, it is not always easy to find the relation between the type of membership functions or rule-base and the controller performance. This study proposes a new systematic auto-tuning algorithm to fine tune fuzzy logic controller gains. A fuzzy PID controller is proposed and applied to several second order systems. The relationship between the closed-loop response and the controller parameters is analysed to devise an auto-tuning method. The results show that the proposed method is highly effective and produces zero overshoot with enhanced transient response. In addition, the robustness of the controller is investigated in the case of parameter changes and the results show a satisfactory performance.
A parametric programming solution to the F-policy queue with fuzzy parameters
Yang, Dong-Yuh; Chang, Po-Kai
2015-03-01
This paper investigates the F-policy queue using fuzzy parameters, in which the arrival rate, service rate, and start-up rate are all fuzzy numbers. The F-policy deals with the control of arrivals in a queueing system, in which the server requires a start-up time before allowing customers to enter. A crisp F-policy queueing system generalised to a fuzzy environment would be widely applicable; therefore, we apply the α-cuts approach and Zadeh's extension principle to transform fuzzy F-policy queues into a family of crisp F-policy queues. This study presents a mathematical programming approach applicable to the construction of membership functions for the expected number of customers in the system. Furthermore, we propose an efficient solution procedure to compute the membership function of the expected number of customers in the system under different levels of α. Finally, we give an example of the proposed system as applied to a case in the automotive industry to demonstrate its practicality.
A Fuzzy Optimization Technique for the Prediction of Coronary Heart Disease Using Decision Tree
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Persi Pamela. I
2013-06-01
Full Text Available Data mining along with soft computing techniques helps to unravel hidden relationships and diagnose diseases efficiently even with uncertainties and inaccuracies. Coronary Heart Disease (CHD is akiller disease leading to heart attack and sudden deaths. Since the diagnosis involves vague symptoms and tedious procedures, diagnosis is usually time-consuming and false diagnosis may occur. A fuzzy system is one of the soft computing methodologies is proposed in this paper along with a data mining technique for efficient diagnosis of coronary heart disease. Though the database has 76 attributes, only 14 attributes are found to be efficient for CHD diagnosis as per all the published experiments and doctors’ opinion. So only the essential attributes are taken from the heart disease database. From these attributes crisp rules are obtained by employing CART decision tree algorithm, which are then applied to the fuzzy system. A Particle Swarm Optimization (PSO technique is applied for the optimization of the fuzzy membership functions where the parameters of the membership functions are altered to new positions. The result interpreted from the fuzzy system predicts the prevalence of coronary heart disease and also the system’s accuracy was found to be good.
Geometric Programming Approach to an Interactive Fuzzy Inventory Problem
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Nirmal Kumar Mandal
2011-01-01
Full Text Available An interactive multiobjective fuzzy inventory problem with two resource constraints is presented in this paper. The cost parameters and index parameters, the storage space, the budgetary cost, and the objective and constraint goals are imprecise in nature. These parameters and objective goals are quantified by linear/nonlinear membership functions. A compromise solution is obtained by geometric programming method. If the decision maker is not satisfied with this result, he/she may try to update the current solution to his/her satisfactory solution. In this way we implement man-machine interactive procedure to solve the problem through geometric programming method.
Fuzzy Logic Trajectory Tracking Controller for a Tanker
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Dur Muhammad Pathan
2012-04-01
Full Text Available This paper proposes a fuzzy logic controller for design of autopilot of a ship. Triangular membership functions have been use for fuzzification and the centroid method for defuzzification. A nonlinear mathematical model of an oil tanker has been considered whose parameters vary with the depth of water. The performance of proposed controller has been tested under both course changing and trajectory keeping mode of operations. It has been demonstrated that the performance is robust in shallow as well as deep waters.
Mining fuzzy conceptual clusters and constructing the fuzzy conceptual frame lattices
Narang, Vibhu; Kumar, Naveen
2004-04-01
The key idea here is to use formal concept analysis and fuzzy membership criterion to partition the data space into clusters and provide knowledge through fuzzy lattices. The procedures, written here, are regarded as mapping or transform of the original space (samples) onto concepts. The mapping is further given the fuzzy membership criteria for clustering from which the clustered concepts of various degrees are found. Bucket hashing measure has been used as a measure of similarity in the proposed algorithm. The concepts are evaluated on the basis of this criterion and then they are clustered. The intuitive appeal of this approach lies in the fact that once the concepts are clustered, the data analyst is equipped with the concept measure as well as the identification of the bridging points. An interactive concept map visualization technique called Fuzzy Conceptual Frame Lattice or Fuzzy Concept Lattices is presented for user-guided knowledge discovery from the knowledge base.
Reliability Modeling and Optimization Using Fuzzy Logic and Chaos Theory
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Alexander Rotshtein
2012-01-01
Full Text Available Fuzzy sets membership functions integrated with logistic map as the chaos generator were used to create reliability bifurcations diagrams of the system with redundancy of the components. This paper shows that increasing in the number of redundant components results in a postponement of the moment of the first bifurcation which is considered as most contributing to the loss of the reliability. The increasing of redundancy also provides the shrinkage of the oscillation orbit of the level of the system’s membership to reliable state. The paper includes the problem statement of redundancy optimization under conditions of chaotic behavior of influencing parameters and genetic algorithm of this problem solving. The paper shows the possibility of chaos-tolerant systems design with the required level of reliability.
DEFF Research Database (Denmark)
Jantzen, Jan
as any PID controller. In the nonlinear domain, the stability of four standard control surfaces is analysed by means of describing functions and Nyquist plots. The self-organizing controller (SOC) is shown to be a model reference adaptive controller. There is a possibility that a nonlinear fuzzy PID......The objective of this textbook is to acquire an understanding of the behaviour of fuzzy logic controllers. Under certain conditions a fuzzy controller is equivalent to a proportional-integral-derivative (PID) controller. Using that equivalence as a link, the book applies analysis methods from...... linear and nonlinear control theory. In the linear domain, PID tuning methods and stability analyses are transferred to linear fuzzy controllers. The Nyquist plot shows the robustness of different settings of the fuzzy gain parameters. As a result, a fuzzy controller is guaranteed to perform as well...
DEFF Research Database (Denmark)
Jantzen, Jan
linear and nonlinear control theory. In the linear domain, PID tuning methods and stability analyses are transferred to linear fuzzy controllers. The Nyquist plot shows the robustness of different settings of the fuzzy gain parameters. As a result, a fuzzy controller is guaranteed to perform as well......The objective of this textbook is to acquire an understanding of the behaviour of fuzzy logic controllers. Under certain conditions a fuzzy controller is equivalent to a proportional-integral-derivative (PID) controller. Using that equivalence as a link, the book applies analysis methods from...... as any PID controller. In the nonlinear domain, the stability of four standard control surfaces is analysed by means of describing functions and Nyquist plots. The self-organizing controller (SOC) is shown to be a model reference adaptive controller. There is a possibility that a nonlinear fuzzy PID...
Baraldi, Andrea; Parmiggiani, Flavio
1996-06-01
According to the following definition, taken from the literature, a fuzzy clustering mechanism allows the same input pattern to belong to multiple categories to different degrees. Many clustering neural network (NN) models claim to feature fuzzy properties, but several of them (like the Fuzzy ART model) do not satisfy this definition. Vice versa, we believe that Kohonen's Self-Organizing Map, SOM, satisfies the definition provided above, even though this NN model is well-known to (robustly) perform topologically ordered mapping rather than fuzzy clustering. This may sound as a paradox if we consider that several fuzzy NN models (such as the Fuzzy Learning Vector Quantization, FLVQ, which was first called Fuzzy Kohonen Clustering Network, FKCN) were originally developed to enhance Kohonen's models (such as SOM and the vector quantization model, VQ). The fuzziness of SOM indicates that a network of processing elements (PEs) can verify the fuzzy clustering definition when it exploits local rules which are biologically plausible (such as the Kohonen bubble strategy). This is equivalent to state that the exploitation of the fuzzy set theory in the development of complex systems (e.g., clustering NNs) may provide new mathematical tools (e.g., the definition of membership function) to simulate the behavior of those cooperative/competitive mechanisms already identified by neurophysiological studies. When a biologically plausible cooperative/competitive strategy is pursued effectively, neighboring PEs become mutually coupled to gain sensitivity to contextual effects. PEs which are mutually coupled are affected by vertical (inter-layer) as well as horizontal (intra-layer) connections. To summarize, we suggest to relate the study of fuzzy clustering mechanisms to the multi-disciplinary science of complex systems, with special regard to the investigation of the cooperative/competitive local rules employed by complex systems to gain sensitivity to contextual effects in
Fuzzy variable linear programming with fuzzy technical coefficients
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Sanwar Uddin Ahmad
2012-11-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE Fuzzy linear programming is an application of fuzzy set theory in linear decision making problems and most of these problems are related to linear programming with fuzzy variables. In this paper an approximate but convenient method for solving these problems with fuzzy non-negative technical coefficient and without using the ranking functions, is proposed. With the help of numerical examples, the method is illustrated.
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Endra Joelianto
2009-11-01
Full Text Available The well known PID controller has inherent limitations in fulfilling simultaneously the conflicting control design objectives. Parameters of the tuned PID controller should trade off the requirement of tracking set-point performances, disturbance rejection and stability robustness. Combination of hybrid reference control (HRC with PID controller results in the transient response performances can be independently achieved without deteriorating the disturbance rejection properties and the stability robustness requirement. This paper proposes a fuzzy based HRC where the membership functions of the fuzzy logic system are obtained by using a substractive clustering technique. The proposed method guarantees the transient response performances satisfaction while preserving the stability robustness of the closed loop system controlled by the PID controller with effective and systematic procedures in designing the fuzzy hybrid reference control system.
Improvments of Payload-based Intrusion Detection Models by Using Noise Against Fuzzy SVM
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Guiling Zhang
2011-02-01
Full Text Available Intrusion detection plays a very important role in network security system. It is proved to analyze the payload of network protocol and to model a payload-based anomaly detector (PAYL can successfully detect outliers of network servers. This paper extends these works by applying a new noise-reduced fuzzy support vector machine (fSVM to improve the detection rate at lower false positive rate. The new noisy against fuzzy SVM is applied to analyzing 1-gram, 2-grams and 2v-grams distribution classification of network payloads, which constructs three different intrusion detection models, respectively. These new intrusion detection models employ reconstruction error based fuzzy membership function to reduce the noisy of the data and to solve the sharp boundary problem, respectively. Experimental results based on DARPA data set demonstrated that the proposed schemes can achieve higher detection rate at very low false positive rate than the original and general SVM methods.
Interval Type-II Fuzzy Rule-Based STATCOM for Voltage Regulation in the Power System
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Ying-Yi Hong
2015-08-01
Full Text Available The static synchronous compensator (STATCOM has recently received much attention owing to its ability to stabilize power systems and mitigate voltage variations. This paper investigates a novel interval type-II fuzzy rule-based PID (proportional-integral-derivative controller for the STATCOM to mitigate bus voltage variations caused by large changes in load and the intermittent generation of photovoltaic (PV arrays. The proposed interval type-II fuzzy rule base utilizes the output of the PID controller to tune the signal applied to the STATCOM. The rules involve upper and lower membership functions that ensure the stable responses of the controlled system. The proposed method is implemented using the NEPLAN software package and MATLAB/Simulink with co-simulation. A six-bus system is used to show the effectiveness of the proposed method. Comparative studies show that the proposed method is superior to traditional PID and type-I fuzzy rule-based methods.
Fuzzy Logic Applied to an Oven Temperature Control System
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Nagabhushana KATTE
2011-10-01
Full Text Available The paper describes the methodology of design and development of fuzzy logic based oven temperature control system. As simple fuzzy logic controller (FLC structure with an efficient realization and a small rule base that can be easily implemented in existing underwater control systems is proposed. The FLC has been designed using bell-shaped membership function for fuzzification, 49 control rules in its rule base and centre of gravity technique for defuzzification. Analog interface card with 16-bits resolution is designed to achieve higher precision in temperature measurement and control. The experimental results of PID and FLC implemented system are drawn for a step input and presented in a comparative fashion. FLC exhibits fast response and it has got sharp rise time and smooth control over conventional PID controller. The paper scrupulously discusses the hardware and software (developed using ‘C’ language features of the system.
Application of fuzzy logic for determining of coal mine mechanization
Institute of Scientific and Technical Information of China (English)
HOSSEINI SAA; ATAEI M; HOSSEINI S M; AKHYANI M
2012-01-01
The fundamental task of mining engineers is to produce more coal at a given level of labour input and material costs,for optimum quality and maximum efficiency.To achieve these goals,it is necessary to automate and mechanize mining operations.Mechanization is an objective that can result in significant cost reduction and higher levels of profitability for underground mines.To analyze the potential of mechanization,some important factors such as seam inclination and thickness,geological disturbances,seam floor conditions and roof conditions should be considered.In this study we have used fuzzy logic,membership functions and created fuzzy rule-based methods and considered the ultimate objective:mechanization of mining.As a case study,the mechanization of the Tazare coal seams in Shahroud area of Iran was investigated.The results show a low potential for mechanization in most of the Tazare coal seams.
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Majid Moshtagh Khorasani
2009-04-01
Full Text Available
Intuitionistic fuzzy hierarchical clustering algorithms
Institute of Scientific and Technical Information of China (English)
Xu Zeshui
2009-01-01
Intuitionistic fuzzy set (IFS) is a set of 2-tuple arguments, each of which is characterized by a mem-bership degree and a nonmembership degree. The generalized form of IFS is interval-valued intuitionistic fuzzy set (IVIFS), whose components are intervals rather than exact numbers. IFSs and IVIFSs have been found to be very useful to describe vagueness and uncertainty. However, it seems that little attention has been focused on the clus-tering analysis of IFSs and IVIFSs. An intuitionistic fuzzy hierarchical algorithm is introduced for clustering IFSs, which is based on the traditional hierarchical clustering procedure, the intuitionistic fuzzy aggregation operator, and the basic distance measures between IFSs: the Hamming distance, normalized Hamming, weighted Hamming, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance. Subsequently, the algorithm is extended for clustering IVIFSs. Finally the algorithm and its extended form are applied to the classifications of building materials and enterprises respectively.
Generalized semi-extremally disconnectedness in double fuzzy topological spaces
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Fatimah M. Mohammed
2017-03-01
Full Text Available In this paper we introduce the concepts of (r, s-generalized fuzzy semi-extremally disconnectedness spaces and study the effect of generalized double fuzzy semi-irresolute and generalized double fuzzy semiopen functions in this space. Moreover, we investigate some interesting relationship between generalized double fuzzy semiopen functions and (r, s-generalized fuzzy semi-extremally disconnectedness spaces.
Shapley's value for fuzzy games
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Raúl Alvarado Sibaja
2009-02-01
Full Text Available This is the continuation of a previous article titled "Fuzzy Games", where I defined a new type of games based on the Multilinear extensions f, of characteristic functions and most of standard theorems for cooperative games also hold for this new type of games: The fuzzy games. Now we give some other properties and the extension of the definition of Shapley¨s Value for Fuzzy Games Keywords: game theory, fuzzy sets, multiattribute decisions.
Compactness theorems of fuzzy semantics
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
The relationship among diverse fuzzy semantics vs. the corresponding logic consequence operators has been analyzed systematically. The results that compactness and logical compactness of fuzzy semantics are equivalent to compactness and continuity of the logic consequence operator induced by the semantics respectively have been proved under certain conditions. A general compactness theorem of fuzzy semantics have been established which says that every fuzzy semantics defined on a free algebra with members corresponding to continuous functions is compact.
Intuitionistic Fuzzy Automaton for Approximate String Matching
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K.M. Ravi
2014-03-01
Full Text Available This paper introduces an intuitionistic fuzzy automaton model for computing the similarity between pairs of strings. The model details the possible edit operations needed to transform any input (observed string into a target (pattern string by providing a membership and non-membership value between them. In the end, an algorithm is given for approximate string matching and the proposed model computes the similarity and dissimilarity between the pair of strings leading to better approximation.
Bayesian system reliability assessment under fuzzy environments
Energy Technology Data Exchange (ETDEWEB)
Wu, H.-C
2004-03-01
The Bayesian system reliability assessment under fuzzy environments is proposed in this paper. In order to apply the Bayesian approach, the fuzzy parameters are assumed as fuzzy random variables with fuzzy prior distributions. The (conventional) Bayes estimation method will be used to create the fuzzy Bayes point estimator of system reliability by invoking the well-known theorem called 'Resolution Identity' in fuzzy sets theory. On the other hand, we also provide the computational procedures to evaluate the membership degree of any given Bayes point estimate of system reliability. In order to achieve this purpose, we transform the original problem into a nonlinear programming problem. This nonlinear programming problem is then divided into four subproblems for the purpose of simplifying computation. Finally, the subproblems can be solved by using any commercial optimizers, e.g. GAMS or LINGO.
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Tonatiuh Hernández Cortés
2015-01-01
Full Text Available The synchronization of chaotic systems, described by discrete-time T-S fuzzy models, is treated by means of fuzzy output regulation theory. The conditions for designing a discrete-time output regulator are given in this paper. Besides, when the system does not fulfill the conditions for exact tracking, a new regulator based on genetic algorithms is considered. The genetic algorithms are used to approximate the adequate membership functions, which allow the adequate combination of local regulators. As a result, the tracking error is significantly reduced. Both the Complete Synchronization and the Generalized Synchronization problem are studied. Some numerical examples are used to illustrate the effectiveness of the proposed approach.
复模糊值函数的Henstock-Stieltjes积分%Henstock-Stieltjes Integral of Complex Fuzzy Valued Function
Institute of Scientific and Technical Information of China (English)
吴建春; 闫彦宗
2012-01-01
基于对模糊复积分理论的研究,本文借助复区间值函数的Henstock-Stieltjes 积分定义和刻划了复模糊值函数的Henstock-Stieltjes积分,并得到了复模糊值函数Henstock-Stieltjes积分的线性性及区间可加性.%To meet the need of generalizing fuzzy complex integration, the concept of Henstock-Stieltjes integral for complex fuzzy-valued function is given and discussed by means of the Henstock-Stieltjes integral of complex interval-valued function. Furthermore, the linearity of integration and interval additivity of the Henstock-Stieltjes integral of complex interval- valued function are obtained.
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Yuanjiang Huang
2014-01-01
Full Text Available The sensor nodes in the Wireless Sensor Networks (WSNs are prone to failures due to many reasons, for example, running out of battery or harsh environment deployment; therefore, the WSNs are expected to be able to maintain network connectivity and tolerate certain amount of node failures. By applying fuzzy-logic approach to control the network topology, this paper aims at improving the network connectivity and fault-tolerant capability in response to node failures, while taking into account that the control approach has to be localized and energy efficient. Two fuzzy controllers are proposed in this paper: one is Learning-based Fuzzy-logic Topology Control (LFTC, of which the fuzzy controller is learnt from a training data set; another one is Rules-based Fuzzy-logic Topology Control (RFTC, of which the fuzzy controller is obtained through designing if-then rules and membership functions. Both LFTC and RFTC do not rely on location information, and they are localized. Comparing them with other three representative algorithms (LTRT, List-based, and NONE through extensive simulations, our two proposed fuzzy controllers have been proved to be very energy efficient to achieve desired node degree and improve the network connectivity when sensor nodes run out of battery or are subject to random attacks.
Mohammadi, Ali Soltan; Rezaee, D D
2012-01-01
The rough-set theory proposed by Pawlak, has been widely used in dealing with data classification problems. The original rough-set model is, however, quite sensitive to noisy data. Tzung thus proposed deals with the problem of producing a set of fuzzy certain and fuzzy possible rules from quantitative data with a predefined tolerance degree of uncertainty and misclassification. This model allowed, which combines the variable precision rough-set model and the fuzzy set theory, is thus proposed to solve this problem. This paper thus deals with the problem of producing a set of fuzzy certain and fuzzy possible rules from incomplete quantitative data with a predefined tolerance degree of uncertainty and misclassification. A new method, incomplete quantitative data for rough-set model and the fuzzy set theory, is thus proposed to solve this problem. It first transforms each quantitative value into a fuzzy set of linguistic terms using membership functions and then finding incomplete quantitative data with lower an...
Zhou, Ronggang; Chan, Alan H S
2017-01-01
In order to compare existing usability data to ideal goals or to that for other products, usability practitioners have tried to develop a framework for deriving an integrated metric. However, most current usability methods with this aim rely heavily on human judgment about the various attributes of a product, but often fail to take into account of the inherent uncertainties in these judgments in the evaluation process. This paper presents a universal method of usability evaluation by combining the analytic hierarchical process (AHP) and the fuzzy evaluation method. By integrating multiple sources of uncertain information during product usability evaluation, the method proposed here aims to derive an index that is structured hierarchically in terms of the three usability components of effectiveness, efficiency, and user satisfaction of a product. With consideration of the theoretical basis of fuzzy evaluation, a two-layer comprehensive evaluation index was first constructed. After the membership functions were determined by an expert panel, the evaluation appraisals were computed by using the fuzzy comprehensive evaluation technique model to characterize fuzzy human judgments. Then with the use of AHP, the weights of usability components were elicited from these experts. Compared to traditional usability evaluation methods, the major strength of the fuzzy method is that it captures the fuzziness and uncertainties in human judgments and provides an integrated framework that combines the vague judgments from multiple stages of a product evaluation process.
Kumarasabapathy, N; Manoharan, P S
2015-01-01
This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs). The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion.
Design and simulation of an image-based fuzzy tracking controller for a wheeled mobile robot
Shiao, Ying Shing; Wu, Ching Wei
2011-12-01
Image processing algorithms and fuzzy logic method are used to design a visual tracking controller for mobile robot navigation. In this paper, a wheeled mobile robot is equipped with a camera for detecting its task space. The grabbed environmental images are treated using image recognition processing to obtain target's size and position. The images are treated using input membership functions as the fuzzy logic controller input. The recognized target's size and position are input into a fuzzy logic controller in which fuzzy rules are used for inference. The inference results are output to the defuzzifier to obtain a physical control signal to control the mobile robot's movement. The velocity and direction of the mobile robot are the output of fuzzy logic controller. The differences in velocities for two wheels are used to control the robot's movement directions. The fuzzy logic controller outputs the control commands to drive the mobile robot to reach a position 50cm front of the target location. The simulation results verify that the proposed FLC is effective in navigating the mobile robot to track a moving target.
Detection of Stator Winding Fault in Induction Motor Using Fuzzy Logic with Optimal Rules
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Hamid Fekri Azgomi
2013-04-01
Full Text Available Induction motors are critical components in many industrial processes. Therefore, swift, precise and reliable monitoring and fault detection systems are required to prevent any further damages. The online monitoring of induction motors has been becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose traction motor faults. This paper presents a simple method for the detection of stator winding faults (which make up 38% of induction motor failures based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. Simulation results are presented to verify the accuracy of motor’s fault detection and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.
Intelligent control for nonlinear inverted pendulum based on interval type-2 fuzzy PD controller
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Ahmad M. El-Nagar
2014-03-01
Full Text Available The interval type-2 fuzzy logic controller (IT2-FLC is able to model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of a fuzzy logic system (FLS. This paper proposes an interval type-2 fuzzy PD (IT2F-PD controller for nonlinear inverted pendulum. The proposed controller uses the Mamdani interval type-2 fuzzy rule based, interval type-2 fuzzy sets (IT2-FSs with triangular membership function, and the Wu–Mendel uncertainty bound method to approximate the type-reduced set. The proposed controller is able to minimize the effect of the structure uncertainties and the external disturbances for the inverted pendulum. The results of the proposed controller are compared with the type-1 fuzzy PD (T1F-PD controller in order to investigate the effectiveness and the robustness of the proposed controller. The simulation results show that the performance of the proposed controller is significantly improved compared with the T1F-PD controller. Also, the results show good performance over a wide range of the structure uncertainties and the effect of the external disturbances.
Kumarasabapathy, N.; Manoharan, P. S.
2015-01-01
This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs). The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion. PMID:26504895
Directory of Open Access Journals (Sweden)
N. Kumarasabapathy
2015-01-01
Full Text Available This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs. The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion.
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Gurumurthy Sasikumar
2016-01-01
Full Text Available The new direction of understand the signal that is created from the brain organization is one of the main chores in the brain signal processing. Amid all the neurological disorders the human brain epilepsy is measured as one of the extreme prevalent and then programmed artificial intelligence detection technique is an essential due to the crooked and unpredictable nature of happening of epileptic seizures. We proposed an Improved Fuzzy firefly algorithm, which would enhance the classification of the brain signal efficiently with minimum iteration. An important bunching technique created on fuzzy logic is the Fuzzy C means. Together in the feature domain with the spatial domain the features gained after multichannel EEG signals remained combined by means of fuzzy algorithms. And for better precision segmentation process the firefly algorithm is applied to optimize the Fuzzy C-means membership function. Simultaneously for the efficient clustering method the convergence criteria are set. On the whole the proposed technique yields more accurate results and that gives an edge over other techniques. This proposed algorithm result compared with other algorithms like fuzzy c means algorithm and PSO algorithm.
Energy Technology Data Exchange (ETDEWEB)
Djukanovic, M.; Babic, B.; Milosevic, B. [Electrical Engineering Inst. Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D.J. [EPRI, Palo Alto, CA (United States). Power System Control; Pao, Y.H. [Case Western Reserve Univ., Cleveland, OH (United States)]|[AI WARE, Inc., Cleveland, OH (United States)
1996-05-01
In this paper the blending/transloading facilities are modeled using an interactive fuzzy linear programming (FLP), in order to allow the decision-maker to solve the problem of uncertainty of input information within the fuel scheduling optimization. An interactive decision-making process is formulated in which decision-maker can learn to recognize good solutions by considering all possibilities of fuzziness. The application of the fuzzy formulation is accompanied by a careful examination of the definition of fuzziness, appropriateness of the membership function and interpretation of results. The proposed concept provides a decision support system with integration-oriented features, whereby the decision-maker can learn to recognize the relative importance of factors in the specific domain of optimal fuel scheduling (OFS) problem. The formulation of a fuzzy linear programming problem to obtain a reasonable nonfuzzy solution under consideration of the ambiguity of parameters, represented by fuzzy numbers, is introduced. An additional advantage of the FLP formulation is its ability to deal with multi-objective problems.
Direct Drive Electro-hydraulic Servo Control System Design with Self-Tuning Fuzzy PID Controller
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Wang Yeqin
2013-06-01
Full Text Available According to the nonlinear and time-varying uncertainty characteristics of direct drive electro-hydraulic servo control system, a self-tuning fuzzy PID control method with speed change integral and differential ahead optimizing operator is put forward by combining fuzzy inference and traditional PID control in this paper.The rule of fuzzy logic is designed, the membership function of the fuzzy subsets is determined and lookup table method is used to correcte the PID parameters in real-time. Finally the simulation is conducted with the typical input signal, such as tracking step, sine etc. The simulation results show that，the self-tuning fuzzy PID control system can effectively improve the dynamic characteristic when the system is out of the range of the operating point compared with the traditional PID control system, there is obvious improvement in the indexes of rapidity, stability and accuracy, and fuzzy self-tuning PID Control is more robust, and more suitable for direct drive electro-hydraulic servo system.
Fuzzy Logic as a Tool for Assessing Students’ Knowledge and Skills
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Michael Gr. Voskoglou
2013-05-01
Full Text Available Fuzzy logic, which is based on fuzzy sets theory introduced by Zadeh in 1965, provides a rich and meaningful addition to standard logic. The applications which may be generated from or adapted to fuzzy logic are wide-ranging and provide the opportunity for modeling under conditions which are imprecisely defined. In this article we develop a fuzzy model for assessing student groups’ knowledge and skills. In this model the students’ characteristics under assessment (knowledge of the subject matter, problem solving skills and analogical reasoning abilities are represented as fuzzy subsets of a set of linguistic labels characterizing their performance, and the possibilities of all student profiles are calculated. In this way, a detailed quantitative/qualitative study of the students’ group performance is obtained. The centroid method and the group’s total possibilistic uncertainty are used as defuzzification methods in converting our fuzzy outputs to a crisp number. According to the centroid method, the coordinates of the center of gravity of the graph of the membership function involved provide a measure of the students’ performance. Techniques of assessing the individual students’ abilities are also studied and examples are presented to illustrate the use of our results in practice.
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Wuyong Qian
2016-09-01
Full Text Available Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often contains infectious, radioactive, or hazardous waste. This article proposes a two-level hierarchical multicriteria decision model to address medical waste disposal method selection (MWDMS, where disposal methods are assessed against different criteria as intuitionistic fuzzy preference relations and criteria weights are furnished as real values. This paper first introduces new operations for a special class of intuitionistic fuzzy values, whose membership and non-membership information is cross ratio based ]0, 1[-values. New score and accuracy functions are defined in order to develop a comparison approach for ]0, 1[-valued intuitionistic fuzzy numbers. A weighted geometric operator is then put forward to aggregate a collection of ]0, 1[-valued intuitionistic fuzzy values. Similar to Saaty’s 1–9 scale, this paper proposes a cross-ratio-based bipolar 0.1–0.9 scale to characterize pairwise comparison results. Subsequently, a two-level hierarchical structure is formulated to handle multicriteria decision problems with intuitionistic preference relations. Finally, the proposed decision framework is applied to MWDMS to illustrate its feasibility and effectiveness.
Qian, Wuyong; Wang, Zhou-Jing; Li, Kevin W
2016-09-09
Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often contains infectious, radioactive, or hazardous waste. This article proposes a two-level hierarchical multicriteria decision model to address medical waste disposal method selection (MWDMS), where disposal methods are assessed against different criteria as intuitionistic fuzzy preference relations and criteria weights are furnished as real values. This paper first introduces new operations for a special class of intuitionistic fuzzy values, whose membership and non-membership information is cross ratio based ]0, 1[-values. New score and accuracy functions are defined in order to develop a comparison approach for ]0, 1[-valued intuitionistic fuzzy numbers. A weighted geometric operator is then put forward to aggregate a collection of ]0, 1[-valued intuitionistic fuzzy values. Similar to Saaty's 1-9 scale, this paper proposes a cross-ratio-based bipolar 0.1-0.9 scale to characterize pairwise comparison results. Subsequently, a two-level hierarchical structure is formulated to handle multicriteria decision problems with intuitionistic preference relations. Finally, the proposed decision framework is applied to MWDMS to illustrate its feasibility and effectiveness.
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
The neuro-fuzzy network (NFN) is used to model the rules and experience of the process planner.NFN is to select the manufacturing operations sequences for the part features. A detailed description of the NFN system development is given. The rule structure utilizes sigmoid functions to fuzzify the inputs, multiplication to combine the if part of the rules and summation to integrate the fired rules. Expert knowledge from previous process plans is used in determining the initial network structure and parameters of the membership functions. A back-propagation (BP) training algorithm was developed to fine tune the knowledge to company standards using the input-output data from executions of previous plans. The method is illustrated by an industrial example.
Institute of Scientific and Technical Information of China (English)
FENG Tao; ZHAO Fu-jun; LIN Jian
2005-01-01
Distinguishing the difficulty degree of top coal caving was a precondition of the popularization and application of the roadway sub-level caving in steep seam. Because of complexity and uncertainty of the coal seam, the expression of influence factors was difficulty with exact data. According to the fuzzy and uncertainty of influence factors, triangular fuzzy membership functions were adopted to carry out the factors ambiguity, of which the factors not only have the consistency of semantic meaning, but also dissolve sufficiently expert knowledge. Based on the properties and structures of fasART fuzzy neural networks of fuzzy logic system and practical needs, a simplified fasART model was put forward, stability and reliability of the network were improved, the deficiency of learning sampies and uncertainty of the factors were better treated. The method is of effective and practical value was identified by experiments.
Fuzzy diagnostic system for oleo-pneumatic drive mechanism of high-voltage circuit breakers.
Nicolau, Viorel
2013-01-01
Many oil-based high-voltage circuit breakers are still in use in national power networks of developing countries, like those in Eastern Europe. Changing these breakers with new more reliable ones is not an easy task, due to their implementing costs. The acting device, called oleo-pneumatic mechanism (MOP), presents the highest fault rate from all components of circuit breaker. Therefore, online predictive diagnosis and early detection of the MOP fault tendencies are very important for their good functioning state. In this paper, fuzzy logic approach is used for the diagnosis of MOP-type drive mechanisms. Expert rules are generated to estimate the MOP functioning state, and a fuzzy system is proposed for predictive diagnosis. The fuzzy inputs give information about the number of starts and time of functioning per hour, in terms of short-term components, and their mean values. Several fuzzy systems were generated, using different sets of membership functions and rule bases, and their output performances are studied. Simulation results are presented based on an input data set, which contains hourly records of operating points for a time horizon of five years. The fuzzy systems work well, making an early detection of the MOP fault tendencies.
Fuzzy Diagnostic System for Oleo-Pneumatic Drive Mechanism of High-Voltage Circuit Breakers
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Viorel Nicolau
2013-01-01
Full Text Available Many oil-based high-voltage circuit breakers are still in use in national power networks of developing countries, like those in Eastern Europe. Changing these breakers with new more reliable ones is not an easy task, due to their implementing costs. The acting device, called oleo-pneumatic mechanism (MOP, presents the highest fault rate from all components of circuit breaker. Therefore, online predictive diagnosis and early detection of the MOP fault tendencies are very important for their good functioning state. In this paper, fuzzy logic approach is used for the diagnosis of MOP-type drive mechanisms. Expert rules are generated to estimate the MOP functioning state, and a fuzzy system is proposed for predictive diagnosis. The fuzzy inputs give information about the number of starts and time of functioning per hour, in terms of short-term components, and their mean values. Several fuzzy systems were generated, using different sets of membership functions and rule bases, and their output performances are studied. Simulation results are presented based on an input data set, which contains hourly records of operating points for a time horizon of five years. The fuzzy systems work well, making an early detection of the MOP fault tendencies.
Pochampally, Kishore K.; Gupta, Surendra M.; Cullinane, Thomas P.
2004-02-01
The cost-benefit analysis of data associated with re-processing of used products often involves the uncertainty feature of cash-flow modeling. The data is not objective because of uncertainties in supply, quality and disassembly times of used products. Hence, decision-makers must rely on "fuzzy" data for analysis. The same parties that are involved in the forward supply chain often carry out the collection and re-processing of used products. It is therefore important that the cost-benefit analysis takes the data of both new products and used products into account. In this paper, a fuzzy cost-benefit function is proposed that is used to perform a multi-criteria economic analysis to select the most economical products to process in a closed-loop supply chain. Application of the function is detailed through an illustrative example.
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Li Shanmei
2015-06-01
Full Text Available Recently, much attention has been paid to the reliability and vulnerability of critical infrastructure. In air traffic systems, the vulnerability analysis for airport networks can be used to guide air traffic administrations in their prioritization of the maintenance and repair of airports, as well as to avoid unnecessary disturbances in the planning of flight schedules. In this paper, the evaluation methods of airport importance and network efficiency are established. Firstly, the evaluation indices of airport importance are proposed from both the topological and functional perspectives. The topological characteristics come from the structure of airport network and the functional features stem from the traffic flow distribution taking place inside the network. Secondly, an integrated evaluation method based on fuzzy soft set theory is proposed to identify the key airports, which can fuse together importance indices over different time intervals. Thirdly, an airport network efficiency method is established for the purpose of assessing the accuracy of the evaluation method. Finally, empirical studies using real traffic data of US and China’s airport networks show that the evaluation method proposed in this paper is the most accurate. The vulnerability of US and China’s airport networks is compared. The similarities and differences between airport geography distribution and airport importance distribution are discussed here and the dynamics of airport importance is studied as well.
Efficient Fuzzy Logic Controller for Magnetic Levitation Systems
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D. S. Shu’aibu
2016-12-01
Full Text Available Magnetic levitation is a system of suspending a body or a complete system against gravity. Suspending a system in air against gravity without using fixed structure for supporting is highly unstable and complex. In the previous research many techniques of stabilizing magnetic levitation systems were discussed. In this paper magnetic levitation controller using fuzzy logic is proposed. The proposed Fuzzy logic controller (FLC is designed, and developed using triangular membership function with 7×7 rules. The system model was implemented in MATLAB/SIMULINK and the system responses to Fuzzy controller with different input signals were investigated. Using unit step input signal, the proposed controller has a settling time of 0.35 secs, percentage overshoot of 0% and there is no oscillation. The proposed controller is validated with a model of an existing practical conventional proportional plus derivatives (PD controller. The PD controller has a settling time of 0.45 secs, percentage overshoot of 7% and with oscillation. Similarly, with sinusoidal input, the FLC has a phase shift and peak response of 0^0 and 0.9967 respectively, while PD controller has a phase shift and peak response of 24.48o and 0.9616 respectively. A disturbance signal was applied to the input of the control system. Fuzzy controller succeeded in rejecting the disturbance signal without further turning of the parameters whereby PD controller failed.
Fuzzy Linguistic Optimization on Multi-Attribute Machining
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Tian-Syung Lan
2010-06-01
Full Text Available Most existing multi-attribute optimization researches for the modern CNC (computer numerical control turning industry were either accomplished within certain manufacturing circumstances, or achieved through numerous equipment operations. Therefore, a general deduction optimization scheme proposed is deemed to be necessary for the industry. In this paper, four parameters (cutting depth, feed rate, speed, tool nose runoff with three levels (low, medium, high are considered to optimize the multi-attribute (surface roughness, tool wear, and material removal rate finish turning. Through FAHP (Fuzzy Analytic Hierarchy Process with eighty intervals for each attribute, the weight of each attribute is evaluated from the paired comparison matrix constructed by the expert judgment. Additionally, twenty-seven fuzzy control rules using trapezoid membership function with respective to seventeen linguistic grades for each attribute are constructed. Considering thirty input and eighty output intervals, the defuzzifierion using center of gravity is thus completed. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution is moreover utilized to integrate and evaluate the multiple machining attributes for the Taguchi experiment, and thus the optimum general deduction parameters can then be received. The confirmation experiment for optimum general deduction parameters is furthermore performed on an ECOCA-3807 CNC lathe. It is shown that the attributes from the fuzzy linguistic optimization parameters are all significantly advanced comparing to those from benchmark. This paper not only proposes a general deduction optimization scheme using orthogonal array, but also contributes the satisfactory fuzzy linguistic approach for multiple CNC turning attributes with profound insight.
Fuzzy Parametric Deduction for Material Removal Rate Optimization
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Tian-Syung Lan
2011-01-01
Full Text Available Problem statement: A general optimization scheme without equipment operations for CNC (computer numerical control finish turning is deemed to be necessarily developed. Approach: In this study, four parameters (cutting depth, feed rate, speed, tool nose runoff with three levels (low, medium, high were considered to optimize the Material Removal Rate (MRR based on L9(34 orthogonal array. Twenty-seven fuzzy control rules using trapezoid membership function with respective to seventeen linguistic grades for material removal rate were additionally constructed. Considering thirty input and eighty output intervals, the defuzzification using center of gravity was moreover completed. Through the Taguchi experiment, the optimum fuzzy deduction parameters could then be received. Results: The confirmation experiment for optimum deduction parameters was furthermore computed within the parameter ranges on an ECOCA-3807 CNC lathe. It is shown that the material removal rate from the fuzzy deduction optimization parameters was significantly advanced comparing to that from the benchmark. Conclusions: This study not only proposed a parametric deduction optimization scheme using orthogonal array, but also contributed the satisfactory fuzzy approach to the material removal rates for CNC turning with profound insight.
Venous thrombosis supervised image indexing and fuzzy retrieval.
Dahabiah, A; Puentes, J; Solaiman, B
2007-01-01
Clinical assessment of venous thrombosis (VT) is essential to evaluate the risk of size increase or embolism. Analyses like echogenecity and echostructure characterization, examine ancillary evidence to improve diagnosis. However, such analyses are inherently uncertain and operator dependent, adding enormous complexity to the task of indexing diagnosed images for medical practice support, by retrieving similar images, or to exploit electronic patient record repositories for data mining. This paper proposes a VT ultrasound image indexing and retrieval approach, which shows the suitability of neural network VT characterization, combined with a fuzzy similarity. Three types of image descriptors (sliding window, wavelet coefficients energy and co-occurrence matrix), are processed by three different neural networks, producing equivalent VT characterizations. Resulting values are projected on fuzzy membership functions and then compared with the fuzzy similarity. Compared to nominal and Euclidean distances, an experimental validation indicates that the fuzzy similarity increases image retrieval precision beyond the identification of images that belong to the same diagnostic class, taking into account the characterization result uncertainty, and allowing the user to privilege any particular feature.
A New Approach of Fuzzy Theory with Uncertainties in Geographic Information Systems
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Mohammad Bazmara
2013-01-01
Full Text Available Until now, fuzzy logic has been extensively used to better analyze and design controllers for chemical processes. It has also been used for other applications like parameter estimation of nonlinear continuous-time systems but in general fuzzy logic has been intensively used for heuristics based system. Recently, fuzzy logic has been applied successfully in many areas where conventional model based approaches are difficult or not cost effective to implement. Mechanistic modeling of physical systems is often complicated by the presence of uncertainties. When models are used as purely predictive tools, uncertainty and variability lead to the need for assessment of the plausible range of model outcomes. A systematic uncertainty analysis provides insight into the level of confidence in model estimates, and can aid in assessing how various possible model estimates should be weighed. In this paper, generalized fuzzy α-cut is used to show the utility of fuzzy approach in uncertainty analysis of pollutant transport in ground water. Based on the concept of transformation method which is an extension of α-cuts, the approach shows superiority over conventional methods of uncertainty modeling. A 2-D groundwater transport model has been used to show the utility of this approach. Results are compared with commonly used probabilistic method and normal Fuzzy alpha-cut technique. In order to provide a basis for comparison between the two approaches, the shape of the membership functions used in the fuzzy methods are the same as the shape of the probability density function used in the Monte-Carlo method. The extended fuzzy α-cut technique presents a strong alternative to the conventional approach.
Estimating outcomes in newborn infants using fuzzy logic.
Chaves, Luciano Eustáquio; Nascimento, Luiz Fernando C
2014-06-01
To build a linguistic model using the properties of fuzzy logic to estimate the risk of death of neonates admitted to a Neonatal Intensive Care Unit. Computational model using fuzzy logic. The input variables of the model were birth weight, gestational age, 5th-minute Apgar score and inspired fraction of oxygen in newborn infants admitted to a Neonatal Intensive Care Unit of Taubaté, Southeast Brazil. The output variable was the risk of death, estimated as a percentage. Three membership functions related to birth weight, gestational age and 5th-minute Apgar score were built, as well as two functions related to the inspired fraction of oxygen; the risk presented five membership functions. The model was developed using the Mandani inference by means of Matlab(r) software. The model values were compared with those provided by experts and their performance was estimated by ROC curve. 100 newborns were included, and eight of them died. The model estimated an average possibility of death of 49.7±29.3%, and the possibility of hospital discharge was 24±17.5%. These values are different when compared by Student's t-test (p<0.001). The correlation test revealed r=0.80 and the performance of the model was 81.9%. This predictive, non-invasive and low cost model showed a good accuracy and can be applied in neonatal care, given the easiness of its use.
Estimating outcomes in newborn infants using fuzzy logic
Chaves, Luciano Eustáquio; Nascimento, Luiz Fernando C.
2014-01-01
OBJECTIVE: To build a linguistic model using the properties of fuzzy logic to estimate the risk of death of neonates admitted to a Neonatal Intensive Care Unit. METHODS: Computational model using fuzzy logic. The input variables of the model were birth weight, gestational age, 5th-minute Apgar score and inspired fraction of oxygen in newborn infants admitted to a Neonatal Intensive Care Unit of Taubaté, Southeast Brazil. The output variable was the risk of death, estimated as a percentage. Three membership functions related to birth weight, gestational age and 5th-minute Apgar score were built, as well as two functions related to the inspired fraction of oxygen; the risk presented five membership functions. The model was developed using the Mandani inference by means of Matlab(r) software. The model values were compared with those provided by experts and their performance was estimated by ROC curve. RESULTS: 100 newborns were included, and eight of them died. The model estimated an average possibility of death of 49.7±29.3%, and the possibility of hospital discharge was 24±17.5%. These values are different when compared by Student's t-test (p<0.001). The correlation test revealed r=0.80 and the performance of the model was 81.9%. CONCLUSIONS: This predictive, non-invasive and low cost model showed a good accuracy and can be applied in neonatal care, given the easiness of its use. PMID:25119746
Estimating outcomes in newborn infants using fuzzy logic
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Luciano Eustáquio Chaves
2014-06-01
Full Text Available OBJECTIVE: To build a linguistic model using the properties of fuzzy logic to estimate the risk of death of neonates admitted to a Neonatal Intensive Care Unit.METHODS: Computational model using fuzzy logic. The input variables of the model were birth weight, gestational age, 5th-minute Apgar score and inspired fraction of oxygen in newborn infants admitted to a Neonatal Intensive Care Unit of Taubaté, Southeast Brazil. The output variable was the risk of death, estimated as a percentage. Three membership functions related to birth weight, gestational age and 5th-minute Apgar score were built, as well as two functions related to the inspired fraction of oxygen; the risk presented five membership functions. The model was developed using the Mandani inference by means of Matlab(r software. The model values were compared with those provided by experts and their performance was estimated by ROC curve.RESULTS: 100 newborns were included, and eight of them died. The model estimated an average possibility of death of 49.7±29.3%, and the possibility of hospital discharge was 24±17.5%. These values are different when compared by Student's t-test (p<0.001. The correlation test revealed r=0.80 and the performance of the model was 81.9%.CONCLUSIONS: This predictive, non-invasive and low cost model showed a good accuracy and can be applied in neonatal care, given the easiness of its use.
Fuzzy support vector machines based on linear clustering
Xiong, Shengwu; Liu, Hongbing; Niu, Xiaoxiao
2005-10-01
A new Fuzzy Support Vector Machines (FSVMs) based on linear clustering is proposed in this paper. Its concept comes from the idea of linear clustering, selecting the data points near to the preformed hyperplane, which is formed on the training set including one positive and one negative training samples respectively. The more important samples near to the preformed hyperplane are selected by linear clustering technique, and the new FSVMs are formed on the more important data set. It integrates the merit of two kinds of FSVMs. The membership functions are defined using the relative distance between the data points and the preformed hyperplane during the training process. The fuzzy membership decision functions of multi-class FSVMs adopt the minimal value of all the decision functions of two-class FSVMs. To demonstrate the superiority of our methods, the benchmark data sets of machines learning databases are selected to verify the proposed FSVMs. The experimental results indicate that the proposed FSVMs can reduce the training data and running time, and its recognition rate is greater than or equal to that of FSVMs through selecting a suitable linear clustering parameter.
Using Fuzzy Logic to Evaluate Normalization Completeness for An Improved Database Design
Qureshi, M Rizwan Jameel; Iqbal, Nayyar; 10.5815/ijitcs.2012.02.07
2012-01-01
A new approach, to measure normalization completeness for conceptual model, is introduced using quantitative fuzzy functionality in this paper. We measure the normalization completeness of the conceptual model in two steps. In the first step, different normalization techniques are analyzed up to Boyce Codd Normal Form (BCNF) to find the current normal form of the relation. In the second step, fuzzy membership values are used to scale the normal form between 0 and 1. Case studies to explain schema transformation rules and measurements. Normalization completeness is measured by considering completeness attributes, preventing attributes of the functional dependencies and total number of attributes such as if the functional dependency is non-preventing then the attributes of that functional dependency are completeness attributes. The attributes of functional dependency which prevent to go to the next normal form are called preventing attributes.
Using Fuzzy Logic to Evaluate Normalization Completeness for an Improved Database Design
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M. Rizwan Jameel Qureshi
2012-03-01
Full Text Available A new approach, to measure normalization completeness for conceptual model, is introduced using quantitative fuzzy functionality in this paper. We measure the normalization completeness of the conceptual model in two steps. In the first step, different normalization techniques are analyzed up to Boyce Codd Normal Form (BCNF to find the current normal form of the relation. In the second step, fuzzy membership values are used to scale the normal form between 0 and 1. Case studies to explain schema transformation rules and measurements. Normalization completeness is measured by considering completeness attributes, preventing attributes of the functional dependencies and total number of attributes such as if the functional dependency is non-preventing then the attributes of that functional dependency are completeness attributes. The attributes of functional dependency which prevent to go to the next normal form are called preventing attributes.
Fuzzy Set Field and Fuzzy Metric
Gebru Gebray; B. Krishna Reddy
2014-01-01
The notation of fuzzy set field is introduced. A fuzzy metric is redefined on fuzzy set field and on arbitrary fuzzy set in a field. The metric redefined is between fuzzy points and constitutes both fuzziness and crisp property of vector. In addition, a fuzzy magnitude of a fuzzy point in a field is defined.
Feizizadeh, Bakhtiar; Blaschke, Thomas; Tiede, Dirk; Moghaddam, Mohammad Hossein Rezaei
2017-09-01
This article presents a method of object-based image analysis (OBIA) for landslide delineation and landslide-related change detection from multi-temporal satellite images. It uses both spatial and spectral information on landslides, through spectral analysis, shape analysis, textural measurements using a gray-level co-occurrence matrix (GLCM), and fuzzy logic membership functionality. Following an initial segmentation step, particular combinations of various information layers were investigated to generate objects. This was achieved by applying multi-resolution segmentation to IRS-1D, SPOT-5, and ALOS satellite imagery in sequential steps of feature selection and object classification, and using slope and flow direction derivatives from a digital elevation model together with topographically-oriented gray level co-occurrence matrices. Fuzzy membership values were calculated for 11 different membership functions using 20 landslide objects from a landslide training data. Six fuzzy operators were used for the final classification and the accuracies of the resulting landslide maps were compared. A Fuzzy Synthetic Evaluation (FSE) approach was adapted for validation of the results and for an accuracy assessment using the landslide inventory database. The FSE approach revealed that the AND operator performed best with an accuracy of 93.87% for 2005 and 94.74% for 2011, closely followed by the MEAN Arithmetic operator, while the OR and AND (*) operators yielded relatively low accuracies. An object-based change detection was then applied to monitor landslide-related changes that occurred in northern Iran between 2005 and 2011. Knowledge rules to detect possible landslide-related changes were developed by evaluating all possible landslide-related objects for both time steps.
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Kharatti Lal
2015-12-01
Full Text Available This section define a level subring or level ideals obtain a set of necessary and sufficient condition for the equality of two ideals and characterizes field in terms of its fuzzy ideals. It also presents a procedure to construct a fuzzy subrings (fuzzy ideals from any given ascending chain of subring ideal. We prove that the lattice of fuzzy congruence of group G (respectively ring R is isomorphic to the lattice of fuzzy normal subgroup of G (respectively fuzzy ideals of R.In Yuan Boond Wu wangrning investigated the relationship between the fuzzy ideals and the fuzzy congruences on a distributive lattice and obtained that the lattice of fuzzy ideals is isomorphic to the lattice of fuzzy congruences on a generalized Boolean algebra. Fuzzy group theory can be used to describe, symmetries and permutation in nature and mathematics. The fuzzy group is one of the oldest branches of abstract algebra. For example group can be used is classify to all of the forms chemical crystal can take. Group can be used to count the number of non-equivalent objects and permutation or symmetries. For example, the number of different is switching functions of n, variable when permutation of the input are allowed. Beside crystallography and combinatory group have application of quantum mechanics.
Park, Tae Joo; Haigo, Saori L; Wallingford, John B
2006-03-01
The vertebrate planar cell polarity (PCP) pathway has previously been found to control polarized cell behaviors rather than cell fate. We report here that disruption of Xenopus laevis orthologs of the Drosophila melanogaster PCP effectors inturned (in) or fuzzy (fy) affected not only PCP-dependent convergent extension but also elicited embryonic phenotypes consistent with defective Hedgehog signaling. These defects in Hedgehog signaling resulted from a broad requirement for Inturned and Fuzzy in ciliogenesis. We show that these proteins govern apical actin assembly and thus control the orientation, but not assembly, of ciliary microtubules. Finally, accumulation of Dishevelled and Inturned near the basal apparatus of cilia suggests that these proteins function in a common pathway with core PCP components to regulate ciliogenesis. Together, these data highlight the interrelationships between cell polarity, cellular morphogenesis, signal transduction and cell fate specification.
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Dalbinder Kour
2015-12-01
Full Text Available This paper focuses on solving the transportation problems with neutrosophic data for the first time. The indeterminacy factor has been considered in Transportation Problems (TP. The two methods of linear programming – Fuzzy Linear Programming (FLP and Crisp Linear Programming (CLP are discussed with reference to neutrosophic transportation problems. The first method uses the membership, non-membership and indeterminacy degrees separately to find the crisp solution using the Fuzzy Programming Technique and then the optimal solution is calculated in terms of neutrosophic data with the help of defined cost membership functions. The satisfaction degree is then calculated to check the better solution. The second method directly solves the TP to find crisp solution considering a single objective function. The cost objective function is taken as neutrosophic data and the methods have been used as such for the first time. Both the methods have been illustrated with the help of a numerical example and these are then applied to solve a real life multi - objective and multi-index transportation problem. Finally the results are compared.
Institute of Scientific and Technical Information of China (English)
柳益君; 朱明放; 习海旭; 朱广萍; 蒋红芬; 陈丹
2012-01-01
The paper proposes a classification method of Gene Expression Programming(GEP) based on the principle of maximum degree of membership, which is named MDM-GER Describing fuzziness of classification by membership degree of fuzzy set, the GEP classifier approximating membership function is obtained on training data set. For the instance to be classified, it computes the membership degree of in fuzzy sets, and determines the final class based on the principle of maximum degree of membership. The experiments carried on three datasets from the UCI machine learning repository show that MDM-GEP not only is effective for classification, but also resolves the un-classifiable region problems in the conventional simple GEP classification strategy.%提出了一种基于最大隶属度原则的基因表达式编程(Gene Expression Programming,GEP)分类方法MDM-GEP.引入模糊集合中的隶属度描述分类的模糊性,在训练集上得到逼近各类别隶属函数的GEP分类器.对于待分类实例,计算其在各模糊集中的隶属度,基于最大隶属度的模糊模式识别原则确定最终归属类,并在三个UCI数据集上对该算法进行了实验.实验结果表明,MDM-GEP不仅具有较好的分类性能,而且有效解决了传统的简单GEP分类方法中存在的拒分区域问题.
Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding.
Jati, Arindam; Singh, Garima; Mukherjee, Rashmi; Ghosh, Madhumala; Konar, Amit; Chakraborty, Chandan; Nagar, Atulya K
2014-03-01
The paper proposes a robust approach to automatic segmentation of leukocyte's nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert hematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises, respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm.
Indian Academy of Sciences (India)
Rohan Kumar; R Anbalagan
2015-03-01
A comprehensive study for the identification of landslide susceptible zones using landslide frequency ratio and fuzzy logic in GIS environment is presented for Tehri reservoir rim region (Uttarakhand, India). Temporal remote sensing data was used to prepare important landslide causative factor layers and landslide inventory. Primary and secondary topographic attributes namely slope, aspect, relative relief, profile curvature, topographic wetness index, and stream power index, were derived from digital elevation model. Landslide frequency ratio technique was adopted to correlate factors with landslides. Further, fuzzy logic method was applied for the integration of factors (causative factor) to map landslide susceptible zones. Normalized landslide frequency ratio value was used for the fuzzy membership function and different fuzzy operators were considered for the preparation of landslide susceptibility/hazard index map. The factors considered in this study were found to be carrying a wide range of information. Accordingly, a methodology was evolved to integrate the factors using combined fuzzy gamma and fuzzy OR operation. Fuzzy gamma integration was performed for six different gamma values (range: 0–1). Gamma value of 0.95 was selected for the preparation of final susceptibility map. Landslide susceptibility index map was divided into the following five hazard zones – very low, low, moderate, high, and very high – on the basis of natural break classification. Validation of the model was performed by using cumulative percentage curve technique. Area under curve value of cumulative percentage curve of proposed landslide susceptibility map (gamma = 0.95) was found to be 0.834 and it can be said that 83.4% accuracy was achieved by applying combined fuzzy logic and landslide frequency ratio method.
An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination.
Li, Jianbing; Huang, Gordon H; Zeng, Guangming; Maqsood, Imran; Huang, Yuefei
2007-01-01
An integrated fuzzy-stochastic risk assessment (IFSRA) approach was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with site conditions, environmental guidelines, and health impact criteria. The contaminant concentrations in groundwater predicted from a numerical model were associated with probabilistic uncertainties due to the randomness in modeling input parameters, while the consequences of contaminant concentrations violating relevant environmental quality guidelines and health evaluation criteria were linked with fuzzy uncertainties. The contaminant of interest in this study was xylene. The environmental quality guideline was divided into three different strictness categories: "loose", "medium" and "strict". The environmental-guideline-based risk (ER) and health risk (HR) due to xylene ingestion were systematically examined to obtain the general risk levels through a fuzzy rule base. The ER and HR risk levels were divided into five categories of "low", "low-to-medium", "medium", "medium-to-high" and "high", respectively. The general risk levels included six categories ranging from "low" to "very high". The fuzzy membership functions of the related fuzzy events and the fuzzy rule base were established based on a questionnaire survey. Thus the IFSRA integrated fuzzy logic, expert involvement, and stochastic simulation within a general framework. The robustness of the modeling processes was enhanced through the effective reflection of the two types of uncertainties as compared with the conventional risk assessment approaches. The developed IFSRA was applied to a petroleum-contaminated groundwater system in western Canada. Three scenarios with different environmental quality guidelines were analyzed, and reasonable results were obtained. The risk assessment approach developed in this study offers a unique tool for systematically quantifying various uncertainties in contaminated site management, and it also
Application of fuzzy logic to social choice theory
Mordeson, John N; Clark, Terry D
2015-01-01
Fuzzy social choice theory is useful for modeling the uncertainty and imprecision prevalent in social life yet it has been scarcely applied and studied in the social sciences. Filling this gap, Application of Fuzzy Logic to Social Choice Theory provides a comprehensive study of fuzzy social choice theory.The book explains the concept of a fuzzy maximal subset of a set of alternatives, fuzzy choice functions, the factorization of a fuzzy preference relation into the ""union"" (conorm) of a strict fuzzy relation and an indifference operator, fuzzy non-Arrowian results, fuzzy versions of Arrow's
Golzari, Fahimeh; Jalili, Saeed
2015-07-21
In protein function prediction (PFP) problem, the goal is to predict function of numerous well-sequenced known proteins whose function is not still known precisely. PFP is one of the special and complex problems in machine learning domain in which a protein (regarded as instance) may have more than one function simultaneously. Furthermore, the functions (regarded as classes) are dependent and also are organized in a hierarchical structure in the form of a tree or directed acyclic graph. One of the common learning methods proposed for solving this problem is decision trees in which, by partitioning data into sharp boundaries sets, small changes in the attribute values of a new instance may cause incorrect change in predicted label of the instance and finally misclassification. In this paper, a Variance Reduction based Binary Fuzzy Decision Tree (VR-BFDT) algorithm is proposed to predict functions of the proteins. This algorithm just fuzzifies the decision boundaries instead of converting the numeric attributes into fuzzy linguistic terms. It has the ability of assigning multiple functions to each protein simultaneously and preserves the hierarchy consistency between functional classes. It uses the label variance reduction as splitting criterion to select the best "attribute-value" at each node of the decision tree. The experimental results show that the overall performance of the proposed algorithm is promising. Copyright © 2015 Elsevier Ltd. All rights reserved.
FPGA implementation of neuro-fuzzy system with improved PSO learning.
Karakuzu, Cihan; Karakaya, Fuat; Çavuşlu, Mehmet Ali
2016-07-01
This paper presents the first hardware implementation of neuro-fuzzy system (NFS) with its metaheuristic learning ability on field programmable gate array (FPGA). Metaheuristic learning of NFS for all of its parameters is accomplished by using the improved particle swarm optimization (iPSO). As a second novelty, a new functional approach, which does not require any memory and multiplier usage, is proposed for the Gaussian membership functions of NFS. NFS and its learning using iPSO are implemented on Xilinx Virtex5 xc5vlx110-3ff1153 and efficiency of the proposed implementation tested on two dynamic system identification problems and licence plate detection problem as a practical application. Results indicate that proposed NFS implementation and membership function approximation is as effective as the other approaches available in the literature but requires less hardware resources.
Sotirov, Sotir
2016-01-01
The book offers a comprehensive and timely overview of advanced mathematical tools for both uncertainty analysis and modeling of parallel processes, with a special emphasis on intuitionistic fuzzy sets and generalized nets. The different chapters, written by active researchers in their respective areas, are structured to provide a coherent picture of this interdisciplinary yet still evolving field of science. They describe key tools and give practical insights into and research perspectives on the use of Atanassov's intuitionistic fuzzy sets and logic, and generalized nets for describing and dealing with uncertainty in different areas of science, technology and business, in a single, to date unique book. Here, readers find theoretical chapters, dealing with intuitionistic fuzzy operators, membership functions and algorithms, among other topics, as well as application-oriented chapters, reporting on the implementation of methods and relevant case studies in management science, the IT industry, medicine and/or ...
Fuzzy classification for farm household characterization
Salasya, B.D.S.; Stoorvogel, J.J.
2010-01-01
Most household classifications use hard classification procedures that limit a household to only one cluster. In this paper, fuzzy classification, in which individuals can belong totally, partially or not at all to a particular cluster, with membership showing how well they fit in each cluster, was
Energy Technology Data Exchange (ETDEWEB)
Cavalcante, Patricia L.; Murari, Carlos Alberto F.; Salas, Silvio S. [Universidade Estadual de Campinas (UNICAMP), SP (Brazil). Fac. de Engenharia Eletrica e de Computacao], Emails: plc@dsee.fee.unicamp.br, murari@dsee.fee.unicamp.br, ssegura@dsee.fee.unicamp.br
2009-07-01
During the development of models for power systems, the researchers aim always to get results compatible with reality, and in this research it was our objective consider that some electric system variables are not deterministic, i e there is imprecision or variations, for example, on the loads. In this study, imprecise variables are represented as fuzzy numbers (the bell shape) and is presented a methodology for analysis of electrical networks of distribution through a specialist three-phase load flow that incorporates fuzzy sets and mathematical operations based on fuzzy logic. The results confirm the good performance of this new method.
Soybean Yield Prediction Using Adaptive Nero-Fuzzy Interface System (ANFIS
Directory of Open Access Journals (Sweden)
S. J. Sajadi
2015-09-01
Full Text Available Productivity of rainfed crops may be predicted using the climatic parameters. Crop yield prediction has an important role in agricultural policies including determining the crop price. Well-known prediction methods are regression method and arterial neural networks. In this paper soybean yield is predicted using Adaptive Nero-Fuzzy Interface System (ANFIS and 11 years of climatic data (1998-2009 in Gonbad-e-Kavous region of Golestan province, Iran. Mean weekly rainfall, mean weekly temperature, mean weekly relative humidity and mean weekly sun shine hours were ANFIS inputs and its output was soybean grain yield (kg/ha. Stepwise Regression for Feature selection from climatic data was done with the SPSS18 software and ANFIS was created, trained and tested with MATLAB R2011a software. Trained ANFIS has ‘constant’ membership function in output layer and ‘gaussmf’ membership function in input layer. Each input has 3 membership functions and each output has one membership function. Root Mean Square Error (RMSE criterion was used to evaluate the performance of the ANFIS. The results showed that the proposed ANFIS with 21 rules has a prediction error (RMSE of 102.170.
PCR仪模糊自整定PID温度控制算法的研究%Fuzzy Self-tuning PID Temperature Control about PCR Instrument
Institute of Scientific and Technical Information of China (English)
毕雪芹; 于媛美
2013-01-01
论文针对PCR基因扩增仪对温度的要求提出了与其适应的算法模糊自整定PID算法.首先给出了PCR反应的各个阶段对温度的响应速度及精度的要求.然后通过对系统建模仿真,分别得到变换隶属函数前后模糊PID与模糊自整定PID的两条曲线.最后给出了模糊PID与模糊自整定PID的对比关系.发现同模糊PID相比模糊自整定PID的响应速度快,超调量小,使系统更稳定.%Based on the temperature requirements of PCR gene amplification instrument and its adaptive algorithm, putting forward a fuzzy self-tuning PID algorithm. First, the polymerase chain reaction (PCR) of each stage of temperature response speed and precision requirements are given. Then through the system modeling simulation, the two curves are repectively got before and after the transformation membership about function of fuzzy PID and fuzzy self-tuning PID. Finally give the contrast relationship of fuzzy PID and fuzzy self-tuning PID. Found that compared with fuzzy PID, fuzzy self-tuning PID has fast response, small overshoot, and makes the system more stable.
Zorić, Nemanja D.; Simonović, Aleksandar M.; Mitrović, Zoran S.; Stupar, Slobodan N.; Obradović, Aleksandar M.; Lukić, Nebojša S.
2014-10-01
This paper deals with active free vibrations control of smart composite beams using particle-swarm optimized self-tuning fuzzy logic controller. In order to improve the performance and robustness of the fuzzy logic controller, this paper proposes integration of self-tuning method, where scaling factors of the input variables in the fuzzy logic controller are adjusted via peak observer, with optimization of membership functions using the particle swarm optimization algorithm. The Mamdani and zero-order Takagi-Sugeno-Kang fuzzy inference methods are employed. In order to overcome stability problem, at the same time keeping advantages of the proposed self-tuning fuzzy logic controller, this controller is combined with the LQR making composite controller. Several numerical studies are provided for the cantilever composite beam for both single mode and multimodal cases. In the multimodal case, a large-scale system is decomposed into smaller subsystems in a parallel structure. In order to represent the efficiency of the proposed controller, obtained results are compared with the corresponding results in the cases of the optimized fuzzy logic controllers with constant scaling factors and linear quadratic regulator.
Directory of Open Access Journals (Sweden)
Y Chalco-Cano
2009-08-01
Full Text Available En este trabajo se presenta una aproximación para un número difuso de tipo trapezoidal por una secuencia de números difusos ε-diferenciables, es decir, números difusos cuya función de pertenencia es diferenciable en el interior del nivel ε. El proceso consiste en la construcción de un número difuso diferenciable usando la sup-min-convolución de un número difuso con un número difuso simétrico casi Gaussiano. Se presenta algunos ejemplos numéricos y un algoritmo computacional para este proceso.In this paper present an approximation for a trapezoidal type fuzzy number by a sequence of ε-differentiable fuzzy numbers, i.e. fuzzy numbers with differentiable membership function in the interior of the ε-level. The process consists on the construction of a differentiable fuzzy number using sup-min-convolution of a fuzzy number with a quasi-Gaussian fuzzy number. This work shows some numerical examples and a computational algorithm for this process.
Peng, Chen; Ma, Shaodong; Xie, Xiangpeng
2017-02-07
This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi-Sugeno (T-S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions. First, a unified event-triggered T-S fuzzy model is provided, in which: 1) a fuzzy observer with the imperfect premise matching is constructed to estimate the unmeasurable states of the studied system; 2) a fuzzy controller is designed following the same premise as the observer; and 3) an output-based event-triggering transmission scheme is designed to economize the restricted network resources. Different from the traditional PDC method, the synchronous premise between the fuzzy observer and the T-S fuzzy system are no longer needed in this paper. Second, by use of Lyapunov theory, a stability criterion and a stabilization condition are obtained for ensuring asymptotically stable of the studied system. On account of the imperfect premise matching conditions are well considered in the derivation of the above criteria, less conservation can be expected to enhance the design flexibility. Compared with some existing emulation-based methods, the controller gains are no longer required to be known a priori. Finally, the availability of proposed non-PDC design scheme is illustrated by the backing-up control of a truck-trailer system.
Institute of Scientific and Technical Information of China (English)
Feng Yi-Fu; Zhang Qing-Ling; Feng De-Zhi
2012-01-01
The global stability problem of Takagi-Sugeno (T S) fuzzy Hopfield neural networks (FHNNs) with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism.Firstly,using both Finsler's lemma and an improved homogeneous matrix polynomial technique,and applying an affine parameter-dependent Lyapunov-Krasovskii functional,we obtain the convergent LMI-based stability criteria.Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique.Secondly,to further reduce the conservatism,a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs,which is suitable to the homogeneous matrix polynomials setting.Finally,two illustrative examples are given to show the efficiency of the proposed approaches.
SECANT-FUZZY LINEAR REGRESSION METHOD FOR HARMONIC COMPONENTS ESTIMATION IN A POWER SYSTEM
Institute of Scientific and Technical Information of China (English)
Garba Inoussa; LUO An
2003-01-01
In order to avoid unnecessary damage of electrical equipments and installations,high quality power should be delivered to the end user and strict control on frequency should be made, Therefore, it is important to estimate the power system's harmonic components with higher accuracy. This paper presents a new approach for estimating harmonic component in a power system using secant - fuzzy linear regression method. In this approach the non - sinusoidal voltage or current waveform is written as I linear function. The coefficient of this function is assumed to be fuzzy number with a membership function that has center and spread value. The time dependent quantity is written as Taylor series with two different time dependent quantities. The objective is to use the sample obtained from the transmission line to find the power system harmonic components and frequencies. We used an experimental voltage signal from a sub power station as a numerical test.
A study on target recognition fusion algorithm based on fuzzy theory
Han, Feng; Yang, WanHai
2008-03-01
In the process of the multi-sensors target recognition fusion, focused on the problem that it is difficult to determine the reliability of each sensor and how the data measured by different sensors are fused, a multi-sensor target recognition fusion method based on fuzzy theory is proposed. The mutual supportability of multiple sensors is obtained from the correlation function. Then by the membership function, the reliability of information provide by each sensor is gained. Finally, the supposed fusion result of multi-sensors target recognition can be produced on the basis of fuzzy integration function. The method is simple computationally and can objectively reflect the reliability of each sensor and interrelationship between these sensors. By applying the method to the target recognition, the simulation experiment shows that it can identify the target accurately and is an effective and feasible multi-sensors target recognition fusion method.
Fuzzy difference-of-Gaussian-based iris recognition method for noisy iris images
Kang, Byung Jun; Park, Kang Ryoung; Yoo, Jang-Hee; Moon, Kiyoung
2010-06-01
Iris recognition is used for information security with a high confidence level because it shows outstanding recognition accuracy by using human iris patterns with high degrees of freedom. However, iris recognition accuracy can be reduced by noisy iris images with optical and motion blurring. We propose a new iris recognition method based on the fuzzy difference-of-Gaussian (DOG) for noisy iris images. This study is novel in three ways compared to previous works: (1) The proposed method extracts iris feature values using the DOG method, which is robust to local variations of illumination and shows fine texture information, including various frequency components. (2) When determining iris binary codes, image noises that cause the quantization error of the feature values are reduced with the fuzzy membership function. (3) The optimal parameters of the DOG filter and the fuzzy membership function are determined in terms of iris recognition accuracy. Experimental results showed that the performance of the proposed method was better than that of previous methods for noisy iris images.
Energy Technology Data Exchange (ETDEWEB)
Pavan, Ana Luiza Menegatti; Alvarez, Matheus; Alves, Allan Felipe Fattori; Rosa, Maria Eugenia Dela; Miranda, Jose Ricardo de Arruda [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Botucatu, SP (Brazil). Instituto de Biociencias; Pina, Diana Rodrigues [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Botucatu, SP (Brazil). Departamento de Doencaas Tropicais e Diagnostico por Imagem; Duarte, Sergio Barbosa [Centro Brasileiro de Pesquisas Fisicas (CBPF), Rio de Janeiro, RJ (Brazil)
2014-08-15
Fractures and dislocations of the hand are some injuries most frequently encountered in trauma of the musculoskeletal system. In evaluating these lesions, in addition to physical examination, radiography, in at least two incidents, is the investigation of choice, and rarely is necessary the help of other images to establish the diagnosis and treatment. The image quality of X-ray examination is therefore essential. In this study, a homogeneous phantom hand was developed to be used in the optimization of images from hand using computed radiography system process. In this procedure were quantified thicknesses of different tissues that constitute an anthropomorphic phantom hand. To perform the classification and quantification of tissue was applied membership functions for histograms of CT scans. The same procedure was adopted for retrospective examinations of 30 patients of the Hospital das Clinicas, Botucatu Medicine School, UNESP (HCFMB-UNESP). The results showed agreement between the thicknesses of tissues that make up the anthropomorphic phantom and sampling of patients, presenting variations between 12.63% and 6.48% for soft tissue and bone, respectively. (author)
2010-05-01
the world of logic than friction in mechanics. — Charles Sanders Peirce 1 Rational deterrence theory rests on the foundation that...4 Kosko, Fuzzy Thinking, 4-17. 5 Daniel McNeill and Paul Freiberger, Fuzzy Logic: The Revolutionary Computer Technology That Is Changing Our...1 McNeill and Freiberger, Fuzzy Logic, 174. 2 Yarger, Little Book on Big Strategy, 16. 3 Mukaidono, Fuzzy Logic for
A Fuzzy Goal Programming for a Multi-Depot Distribution Problem
Nunkaew, Wuttinan; Phruksaphanrat, Busaba
2010-10-01
A fuzzy goal programming model for solving a Multi-Depot Distribution Problem (MDDP) is proposed in this research. This effective proposed model is applied for solving in the first step of Assignment First-Routing Second (AFRS) approach. Practically, a basic transportation model is firstly chosen to solve this kind of problem in the assignment step. After that the Vehicle Routing Problem (VRP) model is used to compute the delivery cost in the routing step. However, in the basic transportation model, only depot to customer relationship is concerned. In addition, the consideration of customer to customer relationship should also be considered since this relationship exists in the routing step. Both considerations of relationships are solved using Preemptive Fuzzy Goal Programming (P-FGP). The first fuzzy goal is set by a total transportation cost and the second fuzzy goal is set by a satisfactory level of the overall independence value. A case study is used for describing the effectiveness of the proposed model. Results from the proposed model are compared with the basic transportation model that has previously been used in this company. The proposed model can reduce the actual delivery cost in the routing step owing to the better result in the assignment step. Defining fuzzy goals by membership functions are more realistic than crisps. Furthermore, flexibility to adjust goals and an acceptable satisfactory level for decision maker can also be increased and the optimal solution can be obtained.
Optimized and Self-Organized Fuzzy Logic Controller for pH Neutralization Process
Directory of Open Access Journals (Sweden)
Parikshit Kishor Singh
2013-11-01
Full Text Available To conform to strict environmental safety regulations, pH control is used in many industrial applications. For this purpose modern process industries are increasingly relying on intelligent and adaptive control strategies. On one hand intelligent control strategies try to imitate human way of thinking and decision making using artificial intelligence (AI based techniques such as fuzzy logic whereas on the other hand adaptive mechanism ensures adjusting of the controller parameters. A self-organized fuzzy logic controller (SOFLC is intelligent in nature and adapts its performance to meet the figure of merit. This paper presents an optimized SOFLC for pH control using performance correction table. The fuzzy adaptation mechanism basically involves a penalty for the output membership functions if the controller performance is poor. The evolutionary genetic algorithm (GA is used for optimization of input-output scaling factors of the conventional fuzzy logic controller (FLC as well as elements of the fuzzy performance correction table. The resulting optimized SOFLC is compared with optimized FLC for servo and regulatory control. Comparison indicate superior performance of SOFLC over FLC in terms of much reduced integral of squared error (ISE, maximum overshoot and undershoot, and increased speed of response.
Institute of Scientific and Technical Information of China (English)
Jie Zhang
2006-01-01
In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a "global" nonlinear long-range prediction model through the fuzzy conjunction of a number of "local" linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.