Dissertations / Theses on the topic 'Fuzzy analysis'
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Karim, Ehsanul, Sri Phani Venkata Siva Krishna Madani, and Feng Yun. "Fuzzy Clustering Analysis." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2165.
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The Objective of this thesis is to talk about the usage of Fuzzy Logic in pattern recognition. There are different fuzzy approaches to recognize the pattern and the structure in data. The fuzzy approach that we choose to process the data is completely depends on the type of data. Pattern reorganization as we know involves various mathematical transforms so as to render the pattern or structure with the desired properties such as the identification of a probabilistic model which provides the explaination of the process generating the data clarity seen and so on and so forth. With this basic school of thought we plunge into the world of Fuzzy Logic for the process of pattern recognition. Fuzzy Logic like any other mathematical field has its own set of principles, types, representations, usage so on and so forth. Hence our job primarily would focus to venture the ways in which Fuzzy Logic is applied to pattern recognition and knowledge of the results. That is what will be said in topics to follow. Pattern recognition is the collection of all approaches that understand, represent and process the data as segments and features by using fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. In the broadest sense, pattern recognition is any form of information processing for which both the input and output are different kind of data, medical records, aerial photos, market trends, library catalogs, galactic positions, fingerprints, psychological profiles, cash flows, chemical constituents, demographic features, stock options, military decisions.. Most pattern recognition techniques involve treating the data as a variable and applying standard processing techniques to it.
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Drobics, Mario. "Data analysis using fuzzy expressions /." Linz : Trauner, 2005. http://aleph.unisg.ch/hsgscan/hm00166742.pdf.
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Chan, Chee Seng. "Fuzzy qualitative human motion analysis." Thesis, University of Portsmouth, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494009.
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Human motion analysis is a very important task for computer vision with a spectrum of potential applications. This thesis presents a novel approach to the problem of human motion understanding. The main contribution of the thesis is that fuzzy qualitative description has been developed for studying human motion from image sequences.
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Yamazaki, Tsukasa. "An improved algorithm for a self-organising controllerd its experimental analysis." Thesis, Queen Mary, University of London, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320959.
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Reynolds, Robert. "Gene Expression Data Analysis Using Fuzzy Logic." Fogler Library, University of Maine, 2001. http://www.library.umaine.edu/theses/pdf/REynoldsR2001.pdf.
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Conroy, Justin Anderson. "Analysis of adaptive neuro-fuzzy network structures." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/19684.
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Popoola, Ademola Olayemi. "Fuzzy-wavelet method for time series analysis." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/804949/.
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Merilan, Jean Elizabeth 1962. "The Use of Fuzzy Analysis in Epidemiology." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/565573.
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Glodeanu, Cynthia Vera. "Conceptual Factors and Fuzzy Data." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-103775.
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With the growing number of large data sets, the necessity of complexity reduction applies today more than ever before. Moreover, some data may also be vague or uncertain. Thus, whenever we have an instrument for data analysis, the questions of how to apply complexity reduction methods and how to treat fuzzy data arise rather naturally. In this thesis, we discuss these issues for the very successful data analysis tool Formal Concept Analysis. In fact, we propose different methods for complexity reduction based on qualitative analyses, and we elaborate on various methods for handling fuzzy data. These two topics split the thesis into two parts. Data reduction is mainly dealt with in the first part of the thesis, whereas we focus on fuzzy data in the second part. Although each chapter may be read almost on its own, each one builds on and uses results from its predecessors. The main crosslink between the chapters is given by the reduction methods and fuzzy data. In particular, we will also discuss complexity reduction methods for fuzzy data, combining the two issues that motivate this thesisKomplexitätsreduktion ist eines der wichtigsten Verfahren in der Datenanalyse. Mit ständig wachsenden Datensätzen gilt dies heute mehr denn je. In vielen Gebieten stößt man zudem auf vage und ungewisse Daten. Wann immer man ein Instrument zur Datenanalyse hat, stellen sich daher die folgenden zwei Fragen auf eine natürliche Weise: Wie kann man im Rahmen der Analyse die Variablenanzahl verkleinern, und wie kann man Fuzzy-Daten bearbeiten? In dieser Arbeit versuchen wir die eben genannten Fragen für die Formale Begriffsanalyse zu beantworten. Genauer gesagt, erarbeiten wir verschiedene Methoden zur Komplexitätsreduktion qualitativer Daten und entwickeln diverse Verfahren für die Bearbeitung von Fuzzy-Datensätzen. Basierend auf diesen beiden Themen gliedert sich die Arbeit in zwei Teile. Im ersten Teil liegt der Schwerpunkt auf der Komplexitätsreduktion, während sich der zweite Teil der Verarbeitung von Fuzzy-Daten widmet. Die verschiedenen Kapitel sind dabei durch die beiden Themen verbunden. So werden insbesondere auch Methoden für die Komplexitätsreduktion von Fuzzy-Datensätzen entwickelt
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Touz'e, Patrick A. "Applications of fuzzy logic to mechanical reliability analysis /." This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-03142009-040345/.
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Wang, Yanfei. "Fuzzy methods for analysis of microarrays and networks." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48175/1/Yanfei_Wang_Thesis.pdf.
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Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.
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Raghfar, Hossein. "Application of fuzzy set theory to poverty analysis." Thesis, University of Essex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343582.
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Touzé, Patrick A. "Applications of fuzzy logic to mechanical reliability analysis." Thesis, Virginia Tech, 1993. http://hdl.handle.net/10919/41583.
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Saboo, Jai Vardhan. "An investment analysis model using fuzzy set theory." Thesis, Virginia Polytechnic Institute and State University, 1989. http://hdl.handle.net/10919/50087.
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Traditional methods for evaluating investments in state-of-the-art technology are sometimes found lacking in providing equitable recommendations for project selection. The major cause for this is the inability of these methods to handle adequately uncertainty and imprecision, and account for every aspect of the project, economic and non-economic, tangible and intangible. Fuzzy set theory provides an alternative to probability theory for handling uncertainty, while at the same time being able to handle imprecision. It also provides a means of closing the gap between the human thought process and the computer, by enabling the establishment of linguistic quantifiers to describe intangible attributes. Fuzzy set theory has been used successfully in other fields for aiding the decision-making process.
The intention of this research has been the application of fuzzy set theory to aid investment decision making. The research has led to the development of a structured model, based on theoretical algorithms developed by Buckley and others. The model looks at a project from three different standpoints- economic, operational, and strategic. It provides recommendations by means of five different values for the project desirability, and results of two sensitivity analyses. The model is tested on a hypothetical case study. The end result is a model that can be used as a basis for promising future development of investment analysis models.Master of Science
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Wang, Yu. "Fuzzy clustering models for gene expression data analysis." Thesis, Northumbria University, 2014. http://nrl.northumbria.ac.uk/21438/.
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With the advent of microarray technology, it is possible to monitor gene expression of tens of thousands of genes in parallel. In order to gain useful biological knowledge, it is necessary to study the data and identify the underlying patterns, which challenges the conventional mathematical models. Clustering has been extensively used for gene expression data analysis to detect groups of related genes. The assumption in clustering gene expression data is that co-expression indicates co-regulation, thus clustering should identify genes that share similar functions. Microarray data contains plenty of uncertain and imprecise information. Fuzzy c-means (FCM) is an efficient model to deal with this type of data. However, it treats samples equally and cannot differentiate noise and meaningful data. In this thesis, motivated by the preservation of local structure, a local weighted FCM is proposed which concentrate on the samples in neighborhood. Experiments show that the proposed method is not only robust to the noise, but also identifies clusters with biological significance. Due to FCM is sensitive to the initialization and the choice of parameters, clustering result lacks stability and biological interpretability. In this thesis, a new clustering approach is proposed, which computes genes similarity in kernel space. It not only finds nonlinear relationship between gene expression profiles, but also identifies arbitrary shape of clusters. In addition, an initialization scheme is presented based on Parzen density estimation. The objective function is modified by adding a new weighted parameter, which accentuates the samples in high density areas. Furthermore, a parameters selection algorithm is incorporated with the proposed approach which can automatically find the optimal values for the parameters in the clustering process. Experiments on synthetic data and real gene expression data show that the proposed method substantially outperforms conventional models in term of stability and biological significance. Time series gene expression is a special kind of microarray data. FCM rarely consider the characteristics of the time series. In this work, a fuzzy clustering approach (FCMS) is proposed by using splines to smooth time-series expression profiles to minimize the noise and random variation, by which the general trend of expression can be identified. In addition, FCMS introduces a new geometry term of radius of curvature to capture the trend information between splines. Results demonstrate that the new method has substantial advantages over FCM for time-series expression data.
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Zhao, Yanbin. "Relaxed stability analysis of fuzzy-model-based control systems." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/relaxed-stability-analysis-of-fuzzymodelbased-control-systems(df7ec615-6b23-4344-844d-00300a43f975).html.
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This thesis presents and extrapolates on the research works concerning the stability analysis of fuzzy-model-based (FMB) control systems. In this study, two types of FMB control systems are considered: 1) Takagi-Sugeno (T-S) FMB control systems; and 2) polynomial fuzzy-model-based (PFMB) control systems. The control scheme illustrated in this thesis has great design flexibility because it allows the number and/or shape of membership functions of fuzzy controllers to be designed independently from the fuzzy models. However, in wake of the imperfectly matched membership functions, the stability conditions of the FMB control systems are typically very conservative given the fact that they are con-gruent with traditional stability analysis methods. In this thesis, based on Lya-punov stability theory, membership-function-dependent (MFD) stability analysis methods are proposed to relax the stability conditions. Firstly, piecewise mem-bership functions (PMFs) are utilised as approximate membership functions to carry out a relaxed stability analysis of T-S FMB control system. Subsequently, PMF-based stability analysis is improved with the consideration of membership function boundary information. Based on the PMF method, we propose a lower-upper-PFM-based stability analysis method. Relaxed stability conditions are obtained in the form of linear matrix inequalities (LMIs) in consideration of the approximation accuracy of the membership function. For the purpose of stability analysis of PFMB control system, the other MFD method proposed is to extract the regional membership function information via operating domain partition. Two types of membership information are consid-ered in each sub-domain: 1) the numerical relationship between all membership function overlap terms; and 2) the bounds of every single membership function overlap term. Thereafter, relaxed sum of squares (SOS)-based stability conditions are derived. In conjunction with these proposed MFD methods, sub-domain fuzzy controllers are utilised to enhance the capability of feedback compensation. In this thesis, all the LMI/SOS-based stability conditions obtained can be solved nu-merically using existing computational tools. Furthermore, simulation examples are provided to illustrate the validity and applicability of the proposed methods.
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Sisman, Yilmaz Nuran Arzu. "A Temporal Neuro-fuzzy Approach For Time Series Analysis." Phd thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/570366/index.pdf.
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The subject of this thesis is to develop a temporal neuro-fuzzy system for fore-
casting the future behavior of a multivariate time series data.
The system has two components combined by means of a system interface.
First, a rule extraction method is designed which is named Fuzzy MAR (Multivari-
ate Auto-regression). The method produces the temporal relationships between
each of the variables and past values of all variables in the multivariate time series
system in the form of fuzzy rules. These rules may constitute the rule-base in a
fuzzy expert system.
Second, a temporal neuro-fuzzy system which is named ANFIS unfolded in -
time is designed in order to make the use of fuzzy rules, to provide an environment
that keeps temporal relationships between the variables and to forecast the future
behavior of data. The rule base of ANFIS unfolded in time contains temporal
TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, Back-propagation
learning algorithm is used. The system takes the multivariate data and the num-
ber of lags needed which are the output of Fuzzy MAR in order to describe a
variable and predicts the future behavior.
Computer simulations are performed by using synthetic and real multivariate
data and a benchmark problem (Gas Furnace Data) used in comparing neuro-
fuzzy systems. The tests are performed in order to show how the system efficiently
model and forecast the multivariate temporal data. Experimental results show
that the proposed model achieves online learning and prediction on temporal data.
The results are compared by other neuro-fuzzy systems, specifically ANFIS.
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Pao-Tan, Wang, and 王保丹. "Fuzzy Reliability Analysis." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/68604522675022530624.
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碩士中原大學
數學系
88
In the reliability theory, studies always focus on the states of system and its components under nonfuzzy(crisp) situation (see [1, 2, 7, 9]). Since Zadeh proposed the idea of fuzzy sets in 1965, the reliability theory in fuzzy environment has been studied (see [5, 6, 8, 10]). In this thesis we explore fuzzy-state structure functions and its possibility reliability by the Zadeh`s extension principle with triangular fuzzy numbers. First we review definitions and properties of coherent systems and its reliability functions. Then we propose coherent systems with triangular fuzzy numbers and their series and parallel fuzzy reliability systems. Finally we give some definitions and properties of possibility reliability functions with fuzzy states. Furthermore, we have possibility reliability function of series and parallel fuzzy systems.
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Liao, Wen-Du, and 廖文督. "Fuzzy Portfolio Analysis with FuzzyReturns and Fuzzy InvestmentProportion." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/53063367115813079879.
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碩士淡江大學
管理科學研究所碩士班
99
In this paper, the fuzzy portfolio will be discussed due to uncertainty of proportion invested in each selected security in a portfolio. The paper will discuss how to solve the portfolio problem about investment proportion of each selected security based on possibilistic mean-standard deviation models. Then, the uncertain investment proportion of each chosen security in the portfolio will be regarded as a fuzzy number and also be formulated and proposed in this paper, showing how the portfolio selection problem will be solved. Finally, a numerical example of a portfolio selection problem will be shown to illustrate how to deal with it by the mean and the approach the paper presents.
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Wang, Shinn-Wen, and 王信文. "Optimization of Fuzzy System by Fuzzy Clustering Analysis." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/91650923483074530257.
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碩士大葉工學院
電機工程研究所
84
Optimization of Fuzzy System by Fuzzy Clustering Analysis ABSTRACT Fuzzy rule base and fuzzy membership functions(MFs) are two major factors in deciding the performance of fuzzy inference system. Therefore, the design plays an important role for the performance stated above. Trial and error was usually the way to solution, which was not only costly and time-consuming but also promised no optimized result. In recent years, many papers were presented about this topic, but none of them has perfect answer. To attack the above problems, we propose the Modified Fuzzy C- Means Method(MFCM) for tuning the parameters of MFs. Then, we fine-tune the MFs with backpropagation learning method. MFCM will be examed for modeling with highly complicated nonlinear functions, such as sinc function and gaussian function, and pattern classification. Finally,there is a simulation test of anti-collision driving system, including first kind of trajectory, second kind of trajectory and evading trajectory of anti-collision driving system, to prove MFCM is suitable for the real world application. The results are quite impressive compared with other approaches such as equalized universe methods(EUM) and subtractive methods(SCM) and show the efficacy of MFCM. Via the MFCM, the bottleneck to be overcomed while designing MFs and the fuzzy system is optimized and has better performance. (Key words: Fuzzy System, Fuzzy Rule Base, FUzzy Membership Function, Fuzzy C-Means, Neural Networks, Backpropagation, Modeling.)
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Wen, Jee-Chean, and 溫志群. "Stability Analysis of Fuzzy System and Fuzzy Perturbed System." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/62340473891519384768.
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碩士義守大學
電子工程學系
88
Stability is the main requirement of control system designs. Fuzzy control system should meet the requirement, too. We will discuss the stability of Takagi-Sugeno fuzzy model. System identification is the most important process to an unknown system. So, to identify a fuzzy model is the first thing we have to do. We discuss the stability analysis and design technique of fuzzy control system using fuzzy block diagram method, and obtain a sufficient condition to guarantee the stability of fuzzy nominal system by Lyapunov stability equation. When practical systems are modeled in mathematical models, uncertain perturbations always exist and make the stable fuzzy system unstable. We also use the Lyapunov theorem to derive the sufficient condition of stability in uncertain perturbation matrix. By using the stable condition of perturbation matrix, we can determine if the stable fuzzy nominal system is still stable with uncertain perturbations. Finally, we use one example to describe the design procedures of a stable fuzzy control system. Then the applicability of the proposed criteria with fuzzy perturbation matrix is examined and result satisfies us.
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黃聖芫. "The comparison of gaussian fuzzy numbers and triangular fuzzy analysis." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/75157544660722738259.
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Chen, Chien Hung, and 陳建宏. "Fuzzy Regression Analysis and Application of Interval Fuzzy Random Variables." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/03996291397162071325.
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碩士國立政治大學
應用數學研究所
97
The aim of this paper is to discuss the linear correspondance between two interval fuzzy random variables. We construct the regression equations of the upper and lower bounds of some interval fuzzy random variables, respectively, by the least squares. The upper and lower bounds of the estimated interval fuzzy random variables are derived by the regression equations of upper and lower bounds, respectively. The collected upper and lower bounds are all crisp data, not fuzzy ones. In this paper, the interval fuzzy random variables discussed are constructed by crisp upper and lower bounds. In order to increase the reprsentative of the interval fuzzy random variables, we need to minimize the errors of the estimated upper and lower bounds. Applying the least squares along with the conventional regression analysis to construct regression lines of upper and lower bounds, respectively, should be the better way to minimize the errors of the estimated upper and lower bounds. However, the errors of the upper and lower bounds estimated by the least squares are the least according to the arithmetic mean value. The more discrete the data we collected , the less representative of the arithmetic mean value is. That will also affect the accuracy of the estimated interval fuzzy random variables. This is what we are worried while we take the least squares as an tool to analyse the interval fuzzy random variables. The coefficient of determination is a reference value which is mostly often used to distinguish the accuracy of the conventional regression model. In the view of the characteristics of fuzzy regression model, the conventional coefficient of determination cannot properly explain the fuzzy linear regression model. In this paper, we propose the fuzzy coverage rate to distinguish the accuracy of the fuzzy linear regression model between two interval fuzzy random variables. Finally, we give an example about the mean monthly working-hour and the mean monthly salary of the manufacturing industry in Taiwan from 1991 to 2007, demonstrating the application of the fuzzy coverage rate in reality.
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CHEN, JIAN-LIANG, and 陳建良. "study of application of fuzzy cluster and fuzzy discriminant analysis." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/78498594847952053296.
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STASI, SERENELLA. "LA LOGICA FUZZY NELLA RICERCA SOCIALE CON PARTICOLARE ATTENZIONE ALLE SCALE DI ATTEGGIAMENTO." Doctoral thesis, 2011. http://hdl.handle.net/11573/917150.
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Yang, Jia-Chi O., and 歐陽嘉麒. "Analysis of Fuzzy Time Series." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/11173549469127417428.
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碩士國立清華大學
工業工程與工程管理學系
89
Fuzzy Sets Theory was introduced by L. A. Zadeh in 1965. Up to now, fuzzy sets have been applied to many fields such as Decision Analysis, System Theory, Artificial Intelligence, Economics and Control Theory. However, until 1993, Q. Song and B.S Chissom proposed a fuzzy time series method which provides an alternative approach for some special dynamic process. This paper presents two methods to forecast secular trend and seasonal variation time series problems respectively. The revised fuzzy time series method uses Song and Chissom’s first-order time-invariant model to predict such linguistic historical data problems and we illustrate the forecasting process by the enrollments of the University of Alabama. This method obtains a better average error than the error in Song and Chissom’s method. The method using fuzzy regression theory solves the shortcoming that fuzzy time series method could not work in dealing with seasonal variation time series problems. Under different confidence level the resultant forecasting interval would provide more flexibility for a decision maker in making decisions. ABSTRACT ii ACKNOWLEDGEMENTS iii CONTENTS iv TABLE CAPTIONS iv FIGURE CAPTIONS iv LIST OF NOTATIONS iv Chapter 1 INTRODUCTION 1 Chapter 2 LITERATURE REVIEW 3 2.1 Fuzzy Time Series 3 2.2 Fuzzy Regression 8 2.3 Conclusions 11 Chapter 3 A FUZZY TIME SERIES MODEL FOR SECULAR DATA 12 3.1 S&C Fuzzy Time Series Method 13 3.2 A Revised Fuzzy Time Series Method 20 3.3 Evaluation and Discussion 25 Chapter 4 FUZZY REGRESSION METHOD FOR SEASONAL TREND 30 4.1 Fuzzy Regression Model 31 4.2 Conclusion and Discussion 36 Chapter 5 SUMMARY AND CONCLUSION 38 REFERENCE 40
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Tseng, Chun-Shu, and 曾淳煦. "Analysis for Fuzzy Mathematical Programming." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/54827731249729679157.
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碩士大同大學
電機工程研究所
81
In this thesis, the main goal is to analyze the fuzzy mathematical programming and how to apply it in an optimal problem. In the fuzzy mathematical programming, it included symmetrical model and nonsymmetrical model. Otherwise, we use the duality property of the fuzzy mathematical programming problems for transferring a difficult solving problem to easy. Next, in this thesis, we proposed a general form of fuzzy numbers. Then using this general form, we can rewrite the problem with no fuzzy components by different kinds of fuzzy index. Finally, when there exist uncertainty components in a system, for instance, disturbances or perturbations, the fuzzy mathematical programming or fuzzy number can be used to solve these two cases.
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Lertworasirikul, Saowanee. "Fuzzy Data Envelopment Analysis (DEA)." 2002. http://www.lib.ncsu.edu/theses/available/etd-05032002-101350/unrestricted/etd.pdf.
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Lin, Nancy Pei-ching, and 林丕靜. "Correlation Analysis of Fuzzy Sets." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/04600114630994536322.
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呂國忠. "Fuzzy Decision Making Analysis -- Evaluating Weapon Systems Using Ranking Fuzzy Number." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/23131979699426093959.
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碩士國防管理學院
資源管理研究所
86
In this paper, we modify Chen''s evaluating model and Hwang''s relative distance method to propose a new algorithm. That is, we develop a general and new method for evaluating weapon system, which use nine scales concepts of AHP and combine the triangular fuzzy numbers to represent fuzzy performance of the corresponding to each attribute, and establish fuzzy judgement matrix by total score of performance. Then take fuzzy number 1, 3, 5, 7, 9 to denote the weight for each attribute. Last, use ranking fuzzy numbers to evaluating the optimal alternative. For practical apphcation, we structure a practical example of evaluating anti-armor weapon system to illustrate our proposed method. The main results of this research are listed in the following: 1. Use fuzzy numbers to represent fuzzy performance of the corresponding to each attribute, and modify Chen''s evaluating model for his shortcoming is by crisp integer to denote sub attribute judgment number. 2. Take Saaty''s 1-9 scale as evaluating range, it abase the mistake of decision making for Chen''s evaluating model is applied in extremely judgment problem. 3. Re-define Hwang''s maximal fuzzy number γmax and minimal fuzzy number γmin, which can decrease the argument for taking β1, and β2 values. 4. By illustrating example, the weight dimension of each attribute has determinant effect for evaluating alternatives.
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Shun, Lin Tsu, and 林子舜. "On Fuzzy Laest-Square Regression Analysis for Fuzzy Input-Output Data." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/26252701230272832734.
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碩士中原大學
數學研究所
86
In the fuzzy linear regression model , studies were focus on the output and parameters as fuzzy numbers, and the input as nonfuzzy real numbers. One ofthese fuzzy linear regression analyses is Tanaka linear programming method and the other is the least square method. Tanaka method is to consider the fuzziness of models and then transform the method of parameter estimate into linear programming method. Least square method is to define the distance between two fuzzy numbers, and then define a least square objective function. Moreover, the concept of noise cluster is used to yield a robust fuzzy least square method. In this thesis, we regard the output , parameters and the inputs as fuzzy numbers. Although Sakawa and Yano (1992) give a new Tanaka linear programming method to estimate the parameters of this model, no one investigates the least square approach in this complicated model. This is the subject of the thesis. Since the product of two LR type fuzzy are not always the corresponding LR type. Therefore, we first use the interval to represent the product of fuzzy numbers and apply the least square method to get a least square estimate, which is called interval least-square estimate. On the other hand, we use the approximate introduced by Dubois and Prade (1980) and the distance defined by Yang and Ko (1997) to get the fuzzy least square method.
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Cai, Hao Xu, and 蔡皓旭. "Interval regression analysis with fuzzy data." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/48136091858014961077.
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碩士國立政治大學
應用數學系
104
Objective: This study concerns how to develop effective fuzzy regression models. In the literature, little is addressed on how to evaluate the effectiveness of fuzzy regression models developed with different regression methods. We consider this issue in this work and present a framework for such evaluation. Method: We consider fuzzy regression models developed with different regression approaches. A method to evaluate the developed models is proposed. We then show that the proposed method possesses desirable mathematical properties and it is applied to compare the two-dimensional regression method and the traditional least square based regression method in our case studies: predicating the concentration of and the volatility of the weighted price index of the Taiwanese stock exchange. Innovation: We propose a new metric to define a distance between two fuzzy numbers. This metric can be used to evaluate the performance of different fuzzy regression models. When a prediction from one model is closest to the sample data measured in terms of the proposed metric, it can be recognized as the optimal predication. Results: Based on the proposed metric, it can be obtained that the two-dimensional fuzzy regression method is better than the traditional least square based regression method. Especially, its resulting generalized residual is smaller. Conclusion: In the literature, no unified framework has been previously proposed in evaluating the effectiveness of developed fuzzy regression models. In this work, we present a metric to achieve this goal. It facilitates the work to determine whether a fuzzy regression model suitably fits obtained samples and whether the model has potential to provide sufficient accuracy for follow-up analysis in a considered problem.
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Chang, Shih-Da, and 張世達. "Fuzzy Logic System Analysis and Applications." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/53727799037537521001.
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LEE, SHENG-EN, and 李聖恩. "USING FUZZY CLASSIFIER FOR SCENE ANALYSIS." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/44259067115352878850.
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Liu, Man Jun, and 劉曼君. "On Possibility Analysis For Fuzzy Data." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/37394476899461085668.
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碩士中原大學
應用數學研究所
83
Although there are many researches on statistical analysis for fuzzy data, there are less discussions on possibility analysis for fuzzy data. In this thesis, our goal is to construct a possibility space for the analysis of fuzzy data. Especially we propose the so-called double fuzzy variable. What is " possibility "? Zadeh proposed the concept of fuzzy sets. Then there are two types of description for the uncertainty : one with randomness, the other with fuzziness. The former is dealt with probability, and the latter with possibility. Although the ideas of probability and possibility are different, the constructions are similar. We will make a simple comparision of these two in Chapter 2 and introduce the fuzzy variable which is defined on possibility space. Then we propose the new idea " double fuzzy variable " in Chapter 3 and also present its properties. The combination of statistics and fuzzy data produces fuzzy statistics; the combination of fuzzy theory and fuzzy data produces the possibility analysis for fuzzy data. In Chapter 3, we intepret the implicit features of double level fuzziness and define the double fuzzy variable (d.f.v.). As a result, double fuzzy variable becomes the means of handling fuzzy data in possibility space. Furthermore, we define the possibility distributions and fuzzy modal values of double fuzzy variables. The topic in Chapter 4 is about parameter estimation. Similar to the maximum likelihood principle in statistics, we provide the maximum possibility likelihood principle to estimate the unknown fuzzy parameter. Finally, we take the normal possibility distribution as an example and estimate its fuzzy parameters.
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Ko, Cheng Hsiu, and 柯政秀. "On Cluster-Wise Fuzzy Regression Analysis." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/42995305129031859293.
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碩士中原大學
應用數學研究所
82
Since Tanaka et al. proposed a study in linear regression analysis with fuzzy model, fuzzy regression analysis has been widely studied and applied in a variety of substantive areas. We know that the regression analysis in the case of heterogeneity of observations are commonly presented in practice. In this paper, the main goal is to apply fuzzy clustering techniques to fuzzy regression analysis. The fuzzy clustering is used to overcome the heterogeneous problem in fuzzy regression model. We combine both together and call it the cluster-wise fuzzy regression analysis.
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Ruey-Chyn, Tsaur, and 曹銳勤. "MODELING AND ANALYSIS in FUZZY REGRESSIONS." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/72502514904086592087.
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博士國立清華大學
工業工程與工程管理學系
87
ABSTRACT Fuzzy regression was first introduced by Tanaka et al [30] in 1982 to be an alternative to evaluate the relation between input variables and output variable. According to the simulation results proposed by Kim, Moskowitz and Koksalan showed that the predicted performance in statistical regression is better than fuzzy regression when the collected data is large. However, fuzzy regression performance becomes relative better as the size of data set diminished and the aptness of the regression model deteriorated. Since fuzzy regression plays the positive role in performance when the collected data is small and the relation between input variables and output variable is vague. Based on the motivation to research a new predicted tool, in this thesis, we will major in Tanaka''s fuzzy regression model. Although, fuzzy regression provides the information for prediction when the systems are indefinite, it has been criticized by the following problems classified into three categories: I. Data Analysis (1) Proper interpretation about the fuzzy regression interval is not discussed [14]. (2) Tanaka''s model only considers the linear model but not intrinsically linear models [36]. (3) Variable selection method is still lack of discussion [36] II. Modeling (4) What is the difference between Tanaka''s model and the fuzzy least square model [38] ? (5) The confidence level h is hard to decide for deriving a fuzzy regression interval [20][40]. III. Applications (6) The original Tanaka''s model was extremely sensitive to the outliers [5][23][41]. (7) Issues of forecasting have never been addressed [39]. In our study, we tried to overcome these problems and presented the results in this thesis with the following structure. In Chapter 3 based on the viewpoint of data analysis, we try to find the best estimated value in a fuzzy regression interval to overcome the problem (1). For problem (2), the properties of an intrinsically linear function were considered. By applying residual analysis, the validity of a transformed the transferred nonlinear fuzzy regression model can be checked. Furthermore, for problem (3), for a fuzzy regression equation with fuzzy relation between input and output variable was first considered. By the concept of Error Sum of Square, we defined an Index of IC to select input variables to the fuzzy regression equation. For the crisp-input and fuzzy-output fuzzy regression, we applied Error Sum of Square and Regression Sum of Square to be two criteria and applied Branch-and-Bound algorithm to select input variables. For Modeling problems, in Chapter 4, we derived a more efficient and better predictability fuzzy regression model by combining Tanaka''s model and fuzzy least square model. Besides, from the concept of fuzzy goal programming, we construct a fuzzy regression model which provides the best satisfactory level h by trading off all of the collected data. For Application issues, in Chapter 5, we proposed a method to identify an outliers in the collected data and applied fuzzy linear programming model to reduce the effect of outliers. Finally, in Chapter 6, we proposed a fuzzy linear programming model to enlarge the feasible region in order to find the best satisfactory solution in prediction and forecasting.
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Yang, Shi-Qi, and 楊士奇. "Decision Analysis of Fuzzy Project Scheduling." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/71800983468662193513.
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Liu, Hsien-Hsiung, and 劉賢雄. "Data Analysis of Conical Fuzzy Vectors." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/79340169041450710266.
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Wang, Chih Shioung, and 王志雄. "Fuzzy Analysis of a Competence Set." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/88711028479721416440.
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碩士國立清華大學
工業工程研究所
82
Competence set, which is a Habitual Domain (HD), is a collection of knowledge, idea, and skills. When we face a problem, we use these knowledge, idea and skills to help us to solve the problem. Over time unless we pay special effort to acquire new knowledge, our competence set will be stablized in a certain domain. When we face a problem which beyonds our current competence set (CS), how can we expand our CS effectively so that we can solve the problem effectively is the focus of this study. Yu and Zhang, Yu and Li have developed expansion models of CS, by an Minimun Spanning Tree and Integer Programming methods respectively to find the optimal expansion process. Because of the fact that vague boundaries of competence set usually exist in practice, we introduce fuzzy set theory to handle this problem. We observe that in schools students, knowledge or competences are obtained in certain order. This observation leads us to use a new concept of background competences to construct a competence set expansion model, of which the level of impact are discussed. Finally, a systematic process to model competence expansion is presented. With an illustrative example, a solution procedure is developed to find the optimal expansion path.
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Manna, Sukanya. "Evidence based fuzzy single document analysis." Phd thesis, 2010. http://hdl.handle.net/1885/150610.
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Human beings can extract meaningful information from single documents and can even summarize them depending on their interest. Computers on the other hand are used in increasingly large number of documents to process them. Even with vast number of documents we are drowning in, there will always be need of important documents which occur singly or in small numbers. For example, it is unlikely that a statistically significant number of airplanes will collide with tall buildings. So, to analyze and extract significant information from reports or documents related to this kind of scenario, it requires subjective analysis of data. This thesis uses structural fuzzy technology, subjective logic and higher order singular value decomposition to extract information from single documents, or from a small collection of documents. The idea is to analyze the language and syntax used in the document to remove uncertainty, increase confidence, and improve the reliability of decision-making which can have many applications including in the media and intelligence gathering. This is illustrated through the generation of extractive summaries using these techniques. The results are good, and validated by comparing document summaries using my techniques with human generated summaries and other machine generated summaries. My summaries are more similar to human summaries than the rest, and this is the major result captured in this thesis.
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黃大偉. "Event Tree Analysis Using Fuzzy Concept." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/11517306333510215915.
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碩士國立清華大學
工業工程研究所
85
Event tree analysis (ETA) method is a straightforward and simple approach for risk assessment. It can be used to identify various sequences and their causes, and also to give the analyst the clear picture about which top event dominates the safety of the system. The traditional ETA uses a single probability to represent each top event. However, it is unreasonable to evaluate the occurrence of an event by using a crisp value without considering the inherent uncertainty and imprecision a state has. Since fuzzy set theory provides a framework for dealing with this kind of phenomena, this tool is used in this study. The main purpose of this study is to make an effort in constructing an easy methodology to evaluate the human error and integrates it into ETA by using fuzzy concept. In addition, a systematic FETA algorithm is developed to evaluate the risk of a large scale system. A practical example of an ATWS event in a nuclear power plant is used to demonstrate the procedure. The fuzzy outcomes will be defuzzified by using the total integral value in terms of the degree of optimism the decision maker has. At last, more information about the importance and uncertainty of top events will be provided by using the two indices.
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Rau, Min-Zong, and 饒旻宗. "Fuzzy Clustering with Principal Component Analysis." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/98871081495872381063.
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碩士國立中山大學
電機工程學系研究所
98
We propose a clustering algorithm which incorporates a similarity-based fuzzy clustering and principal component analysis. The proposed algorithm is capable of discovering clusters with hyper-spherical, hyperellipsoidal, or oblique hyper-ellipsoidal shapes. Besides, the number of the clusters need not be specified in advance by the user. For a given dataset, the orientation, locations, and the number of clusters obtained can truthfully reflect the characteristics of the dataset. Experimental results, obtained by running on datasets generated synthetically, show that our method performs better than other methods.
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He, Guan-Sian, and 何冠賢. "Stability Analysis of Polynomial Fuzzy Systems." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/76065397030027969171.
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碩士國立中正大學
光機電整合工程研究所
100
This study presents a polynomial fuzzy model and a path controller design for a nonlinear four-wheeled omnidirectional mobile robot (ODMR) using polynomial fuzzy systems. A polynomial controller was designed according to the parallel distributed compensation (PDC) from the given polynomial fuzzy model of the ODMR. This proposed controller is capable of driving the closed-loop system states of the ODMR to follow reference trajectory commands. We used stability conditions that were represented by the sum of squares (SOS) to guarantee global stability. In addition, we derived the limitation conditions represented in term of SOS for control input and output using a polynomial Lyapunov function. The stable polynomial controller satisfied the constraint on the control input and output. These proposed SOS-based constraint conditions are more general and relaxed than are current linear matrix inequality (LMI)-based constraint conditions. This study focuses on developing methods for stability analysis and stabilization based on the SOS approach and that depend on the size of the time-delay. A polynomial Lyapunov function was applied to derive the stability and stabilization time-delay conditions of the nonlinear time-delay systems, and contained quadratic Lyapunov functions as a special case. Finally, computer simulations showed that the SOS-based approaches were more effective than were the LMI-based approaches.
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Shie, Yi Ting, and 謝逸婷. "Using Fuzzy Ranking Analysis in Data Envelopment Analysis models." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/37817448927487285064.
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碩士南台科技大學
工業管理研究所
93
The Data Envelopment Analysis (DEA) was a methodology to measure the relative efficiency of the decision making units (DMUs) with the same input and output items. Since 1978, Charnes, Cooper and Rhodes proposed DEA method, many researchers were engaged in related researches. Traditionally, the coefficients of DEA models were assumed to be crisp values. However, it was invalid in some cases. Based on Zadeh’s fuzzy theory, some researchers developed the fuzzy DEA (FDEA). This paper is intended to give an another approach to FDEA model. First of all, we set strict criteria and select fitted fuzzy ranking methods. The fuzzy inputs and fuzzy outputs of the DMUs can be transformed into crisp values of α-level. And then, the relative efficiency in α-level of a particular DMU can be solved by the traditional DEA model. Finally, we can compare the relative efficiency by applying area measurement method. It follows from the result, the method we proposed in this study is not only valid but also more efficient than existing methods.
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Wei, Shih-Hua, and 魏世驊. "New Methods for Fuzzy Risk Analysis Based on Similarity Measures between Fuzzy Numbers." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/am43ea.
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碩士國立臺灣科技大學
資訊工程系
95
In recent years, the task of measuring the degree of similarity between fuzzy numbers plays an important role in fuzzy decision making, information fusion and fuzzy risk analysis. In this thesis, we present two similarity measures for generalized fuzzy numbers and interval-valued fuzzy numbers. It combines the concepts of geometric distance, the perimeter, height and center of gravity point of generalized fuzzy numbers and interval-valued fuzzy number, respectively. Moreover, we also presented an interval-valued fuzzy number adjusting algorithm. Based on proposed similarity measures, we propose two new methods for handling fuzzy risk analysis problems. The proposed fuzzy risk analysis methods can overcome the drawback of existing methods. They can deal with fuzzy risk analysis in a more intelligent and flexible manner.
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(9863570), J. Zajaczkowski. "Analysis of the hierarchical fuzzy control using evolutionary algorithms." Thesis, 2010. https://figshare.com/articles/thesis/Analysis_of_the_hierarchical_fuzzy_control_using_evolutionary_algorithms/13462052.
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"The research presented in this thesis examines the construction of a fuzzy logic controller for complex nonlinear system by control system decomposition into hierarchial fuzzy logic subsystems ... evolutionary algorithm (EA) based methods are proposed to determine the control system for the hieracrchical fuzzy system (HFS)"--Abstract.
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Lee, Sheng-Wei, and 李昇威. "Study and Analysis of Fuzzy Time Series." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/642ufe.
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碩士國立臺北教育大學
數學暨資訊教育學系(含數學教育碩士班)
98
Since Song proposed fuzzy time series model – hypothesis on the undefined time series, in 1993, many scholars have come up with related research, and they got good results. In this study, I used fuzzy time series to predict the number of special education students – through the method that S.R. Singh (2007), Hwang (1998), and Ching-Hsue Cheng (2009) proposed. I used three interval quantity to predict the three methods, and used MSE(Mean Square Error) and M APE(Mean Absolute Percent Error) to check the accuracy of the prediction. They indicate the main points and results through analysis and comparision of the experimental steps and experimental methods. The main steps of the research can be divided into five steps: (1) introduce three methods of the research; (2) use the three methods to predict the data I collected – the number of special education students; (3) use two different groups of interval length and interval quantity to predict with these three methods; (4) compare the best results of the three methods; (5) find out more specific prediction of the best method. The best result of the three is the method Hwang (1998) proposed, and based on the method to select the best group of interval length and degree to make the prediction more accurate.
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Lo, Chien-Chih, and 羅堅秩. "Fuzzy Linear Modeling for Load Characteristics Analysis." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/51368262719104230250.
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碩士國立海洋大學
電機工程學系
87
Load characteristics are most important information of a power system and of which the analysis is a critical task for a utility company. The main purpose of this project is to investigate the application of fuzzy modeling to the analysis of load characteristics. The method of fuzzy modeling is suitable for the analysis of load characteristics due to the inherent nonlinearity of load curves. A fuzzy model is a set of fuzzy IF-THEN rules for describing system behaviors.This thesis constructs fuzzy load models based on measured load data to set up the basis for load study. , The fuzzy model is employed in the analysis of load charactersitics for depicting the relationship among load demand and time as well as other variables(temperature and humidity), for each customer. The load fuzzy model is capable of providing both quantitative and qualitative descriptions for the customer under study and hence will be helpful to the task of analyzing load characteristics.
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Xu, Jia Yuan, and 許嘉元. "Fuzzy analysis and forecasting in time series." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/02051032700206938986.
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