Готові списки джерел за темами / Fuzzy set estimation

Добірка наукової літератури з теми "Fuzzy set estimation"

Автор: Grafiati

Опубліковано: 11 листопада 2022

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Статті в журналах з теми "Fuzzy set estimation":

Fajardo, Jesús A. "A Criterion for the Fuzzy Set Estimation of the Regression Function." Journal of Probability and Statistics 2012 (2012): 1–18. http://dx.doi.org/10.1155/2012/593036.

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We propose a criterion to estimate the regression function by means of a nonparametric and fuzzy set estimator of the Nadaraya-Watson type, for independent pairs of data, obtaining a reduction of the integrated mean square error of the fuzzy set estimator regarding the integrated mean square error of the classic kernel estimators. This reduction shows that the fuzzy set estimator has better performance than the kernel estimations. Also, the convergence rate of the optimal scaling factor is computed, which coincides with the convergence rate in classic kernel estimation. Finally, these theoretical findings are illustrated using a numerical example.

Pham, T. D. "Grade estimation using fuzzy- set algorithms." Mathematical Geology 29, no. 2 (June 1997): 291–305. http://dx.doi.org/10.1007/bf02769634.

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Nedosekin, Alexey, Zinaida Abdoulaeva, Evgenii Konnikov, and Alexander Zhuk. "Fuzzy Set Models for Economic Resilience Estimation." Mathematics 8, no. 9 (September 4, 2020): 1516. http://dx.doi.org/10.3390/math8091516.

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(1) Presented models are proposed for analyzing the resilience of an economic system in a framework of a 4 × 6 matrix, the core of which is a balanced scorecard (BSC). Matrix rows present strategic perspectives, matrix columns present strategic maps. (2) Resilience assessment models are based on fuzzy logic and soft computing, combined with systemic-cybernetic approaches to building presented models. The simplest models are Zadeh linguistic variables that describe key performance indicators (KPIs). The BSC model is an acyclic graph with fuzzy links that are calibrated based on special rules. The information obtained during the simulation is aggregated through a matrix aggregate calculator (MAC). (3) The BSC model was used to assess the economic resilience of a small electrical enterprise in Russia, numbering 2000 people with revenue of approximately 100 million euros per year. The BSC model included about 70 KPIs and 200 fuzzy links. Also, the presented MAC model was applied to obtain linguistic classifiers in five basic industries, using the example of a comparative analysis of 82 international industrial companies. (4) The proposed models allow not only to describe the economic system and its external environment, but also solutions aimed at increasing resilience, within the unified framework.

Kim, Sung min, Gyeong-hun Do, Junkeon Ahn, and Juneyoung Kim. "Quantitative ASIL Estimation Using Fuzzy Set Theory." International Journal of Automotive Technology 21, no. 5 (October 2020): 1177–84. http://dx.doi.org/10.1007/s12239-020-0111-y.

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Bershtein, Leonid, Alexander Bozhenyuk, and Margarita Knyazeva. "Definition of Cliques Fuzzy Set and Estimation of Fuzzy Graphs Isomorphism." Procedia Computer Science 77 (2015): 3–10. http://dx.doi.org/10.1016/j.procs.2015.12.353.

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HONG, DUG HUN, and CHANGHA HWANG. "RIDGE REGRESSION PROCEDURES FOR FUZZY MODELS USING TRIANGULAR FUZZY NUMBERS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 02 (April 2004): 145–59. http://dx.doi.org/10.1142/s0218488504002746.

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This paper presents a new method of estimating fuzzy multivariable linear and nonlinear regression models using triangular fuzzy numbers. This estimation method is obtained by implementing a dual version of the ridge regression procedure for linear models. It allows us to perform fuzzy nonlinear regression by constructing a fuzzy linear regression in a high dimensional feature space for the data set with crisp inputs and fuzzy output. Experimental results are then presented, which indicate the performance of this algorithm.

Meeden, Glen, and Siamak Noorbaloochi. "Hypotheses Testing as a Fuzzy Set Estimation Problem." Communications in Statistics - Theory and Methods 42, no. 10 (May 15, 2013): 1806–20. http://dx.doi.org/10.1080/03610926.2011.599005.

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YOSHIDA, YUJI. "PERCEPTION-BASED ESTIMATIONS OF FUZZY RANDOM VARIABLES: LINEARITY AND CONVEXITY." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, supp01 (April 2008): 71–87. http://dx.doi.org/10.1142/s021848850800525x.

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A set of perceived random events is given by a fuzzy random variable, and an estimation of real random variables is represented by a functional on real random variables. The perception-based extension of estimation regarding random events is introduced, extending the functional to a functional of fuzzy random variables. This paper discusses some conditions and various properties of the extended estimations, for example, monotonicity, continuity, linearity, sub-additivity/super-additivity, convexity/concavity. Several examples of the perception-based extended estimations are investigated. This paper analyzes the general cases, where the estimations do not have monotone properties, from the viewpoint of convexity/concavity. The results can be applicable to other estimations in engineering, economics and so on.

Taheri, S. Mahmoud. "Trends in Fuzzy Statistics." Austrian Journal of Statistics 32, no. 3 (April 3, 2016): 239–57. http://dx.doi.org/10.17713/ajs.v32i3.459.

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After introducing and developing fuzzy set theory, a lot of studies have been done to combine statistical methods and fuzzy set theory. Thisworks, called fuzzy statistics, have been developed in some branches.In this article we review essential works on fuzzy estimation, fuzzy hypotheses testing, fuzzy regression, fuzzy Bayesian statistics, and some relevant fields.

Alharbi, Yasser S., and Amr R. Kamel. "Fuzzy System Reliability Analysis for Kumaraswamy Distribution: Bayesian and Non-Bayesian Estimation with Simulation and an Application on Cancer Data Set." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 19 (June 7, 2022): 118–39. http://dx.doi.org/10.37394/23208.2022.19.14.

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This paper proposes the fuzzy Bayesian (FB) estimation to get the best estimate of the unknown parameters of a two-parameter Kumaraswamy distribution from a frequentist point of view. These estimations of parameters are employed to estimate the fuzzy reliability function of the Kumaraswamy distribution and to select the best estimate of the parameters and fuzzy reliability function. To achieve this goal we investigate the efficiency of seven classical estimators and compare them with FB proposed estimation. Monte Carlo simulations and cancer data set applications are performed to compare the performances of the estimators for both small and large samples. Tierney and Kadane approximation is used to obtain FB estimates of traditional and fuzzy reliability for the Kumaraswamy distribution. The results showed that the fuzziness is better than the reality for all sample sizes and the fuzzy reliability at the estimates of the FB proposed estimated is better than other estimators, it gives the lowest Bias and root mean squared error.

Більше джерел

Дисертації з теми "Fuzzy set estimation":

Han, Sedat. "Estimation Of Cost Overrun Risk In Interrnational Project By Using Fuzzy Set Theory." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606032/index.pdf.

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In the global construction market, most construction companies are willing to undertake international projects in order to maximise their profitability by taking advantage of attractive emerging markets and minimise dependence on unfavorable domestic market conditions. In order to be awarded a contract in highly competitive global construction market, companies should excel in choosing the most attractive markets and prepare winning bids for the selected construction projects in those markets. While preparing bids, the major concern of companies is to offer an optimum price that will enable them to earn enough profits and win the contract at the same time, where profit making ability is strongly correlated with proper estimation of a risk premium that is added onto the estimated cost of the project. Due to the nature of construction works, there are lots of uncertainties associated with the project, market and country conditions. Therefore, how the profitability of the project changes with occurrence of various risk events, in other words, the sensitivity of project costs to risk events, should be estimated by bidders realistically. In this study, fuzzy set theory is used to estimate cost overrun risk in international projects at the bidding stage. The objective is to propose a methodology which can be used by bidders to quantify cost overrun risk so that a realistic risk premium may be determined. A fuzzy risk rating approach is proposed to quantify cost overrun risk rating, which takes into account of risks characterised in international construction projects. For this purpose, risk sources have been identified and a risk model is put forward by using influence diagramming method. Based on this risk model, a fuzzy risk rating algorithm has been defined and software has been developed to conduct fuzzy risk rating calculations easily. After a decision-maker inserts the necessary inputs related with project and country risk factors, the output of the software is a rating that takes into account of all factors that may affect cost overrun risk in international construction projects. The reliability of the algorithm and developed software have been tested by an application on a real construction project. The proposed methodology and decision support tool have been proved to be reliable for the estimation of cost overrun risk while giving bidding decisions in international markets.

Mohamed, Hamad O. "Land suitability evaluation, improving accuracy of assessments with a new paradigm based on geostatistical estimation and fuzzy set theory." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0015/MQ57975.pdf.

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Pitarch, Pérez José Luis. "Contributions to fuzzy polynomial techniques for stability analysis and control." Doctoral thesis, Universitat Politècnica de València, 2014. http://hdl.handle.net/10251/34773.

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The present thesis employs fuzzy-polynomial control techniques in order to improve the stability analysis and control of nonlinear systems. Initially, it reviews the more extended techniques in the field of Takagi-Sugeno fuzzy systems, such as the more relevant results about polynomial and fuzzy polynomial systems. The basic framework uses fuzzy polynomial models by Taylor series and sum-of-squares techniques (semidefinite programming) in order to obtain stability guarantees. The contributions of the thesis are: ¿ Improved domain of attraction estimation of nonlinear systems for both continuous-time and discrete-time cases. An iterative methodology based on invariant-set results is presented for obtaining polynomial boundaries of such domain of attraction. ¿ Extension of the above problem to the case with bounded persistent disturbances acting. Different characterizations of inescapable sets with polynomial boundaries are determined. ¿ State estimation: extension of the previous results in literature to the case of fuzzy observers with polynomial gains, guaranteeing stability of the estimation error and inescapability in a subset of the zone where the model is valid. ¿ Proposal of a polynomial Lyapunov function with discrete delay in order to improve some polynomial control designs from literature. Preliminary extension to the fuzzy polynomial case. Last chapters present a preliminary experimental work in order to check and validate the theoretical results on real platforms in the future.
Pitarch Pérez, JL. (2013). Contributions to fuzzy polynomial techniques for stability analysis and control [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34773
TESIS

Mao, Hongwei. "Estimating labour productivity using fuzzy set theory." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0019/MQ47065.pdf.

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FREISSINET, CATHERINE. "Estimation des imprécisions dans la modélisation du devenir des produits phytosanitaires dans les sols : une méthode fondée sur la logique floue." Université Joseph Fourier (Grenoble), 1997. http://www.theses.fr/1997GRE10068.

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Le travail presente dans ce memoire a pour principal objectif d'etablir une methodologie permettant d'evaluer les imprecisions sur les resultats de modelisation et de l'appliquer a l'estimation de la vulnerabilite de la ressource en eau face a des pollutions diffuses d'origine agricole. Dans une premiere partie, nous presentons les principaux phenomenes physiques lies au devenir des produits phytosanitaires dans les sols (transport, retention, transformation, volatilisation, reprise par les plantes. . . ) et nous ne manquerons pas de rappeler les differentes imprecisions concernant les caracteristiques chimiques des produits phytosanitaires, les proprietes du sol et les equations utilisees pour modeliser ces differents phenomenes. Puis, apres avoir mis en evidence l'importance de la prise en compte de ces differentes imprecisions pour evaluer celles sur les resultats de modelisation, et avoir montre les limites des methodes classiques d'analyse d'incertitude, nous developpons une methode fondee sur les sous-ensembles flous et sur la logique floue. Cette methode permet de quantifier les imprecisions sur les resultats issus des modeles mathematiques et d'evaluer la confiance que l'on peut accorder a ces derniers. Plusieurs applications, utilisant des modeles simples de diagnostic, sur le bassin versant du grand morin (region parisienne) et sur celui de pearl harbor (hawai), permettent de comparer les imprecisions sur les resultats obtenues d'une part avec des methodes classiques et d'autre part avec la methode fondee sur les sous-ensembles flous. Ces comparaisons mettent en avant les avantages de cette derniere par rapport aux methodes classiques, presentent differents indices d'evaluation de la vulnerabilite (drastic, af) et du potentiel a la lixiviation (rf) pour deux produits phytosanitaires (atrazine et diuron), et soulignent a nouveau l'importance de la prise en compte des imprecisions dans les resultats de modelisation. La derniere partie de ce travail consiste a valider notre methodologie fondee sur la logique floue sur une application consistant a calculer le flux convectif d'atrazine dans la zone non saturee du bassin du grand morin en utilisant le modele mathematique leachmp. Il ressort de cette application, qu'il est fondamental d'associer a la valeur moyenne issue d'un modele mathematique, un intervalle de variation de cette valeur defini par une coupe de niveau alpha et le degre d'appartenance minimum des valeurs de l'intervalle au sous-ensemble flou des solutions.

Tillet, Joris. "Safe localization and control of a towed sensor." Thesis, Brest, École nationale supérieure de techniques avancées Bretagne, 2021. http://www.theses.fr/2021ENTA0013.

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L’exploration des océans devient de plus en plus accessible, notamment grâce aux avancées en robotique. Les applications pour les robots sous-marins sont nombreuses. Dans cette thèse, on s’intéresse particulièrement à la recherche d’épaves, telle que celle de La Cordelière, qui a coulé dans la Rade de Brest en 1512. Le système robotique proposé consiste à tracter un magnétomètre susceptible de détecter les matériaux ferromagnétiques de l’épave. Le capteur ne peut pas être directement embarqué car il est sensible aux perturbations du robot. C’est pourquoi il est déporté. Deux problématiques sont alors étudiées pour appréhender ce système. La première est liée au contrôle de la position du magnétomètre alors que l’on ne peut agir que sur le robot tractant. Une méthode de linéarisation par bouclage est alors utilisée pour construire un contrôleur. Ce contrôleur est ensuite validé sous certaines contraintes d’état en utilisant des outils d’analyse par intervalles. La seconde problématique concerne la localisation sous l’eau de manière fiable. Sont alors étudiés des moyens d’appréhender les incertitudes et les données aberrantes collectées par un capteur acoustique. L’analyse par intervalles permet d’obtenir des premiers résultats, et la logique floue vient compléter l’approche en donnant plus de souplesse dans la priorisation des contraintes. Finalement, des expérimentations sont présentées avec différents robots, et notamment la localisation d’un ROV dans une piscine
The oceans’ exploration becomes more and more reachable, especially thanks to robotics progress. Applications for underwater robots are plentiful. In this thesis, we particularly focus on the search of wrecks, as the Cordelière, which sank in the Bay of Brest (France) in 1512. The proposed robotic system consists of towing a magnetometer likely to detect the ferromagnetic materials of the wreck. The sensor cannot be directly embedded because it is sensitive to the perturbations from the robot. This is why it is deported. Two issues are studied to approach this system. The first one is linked to the control of the magnetometer’s position, whereas we can only act on the towing robot. A feedback linearization method is used to design a controller. Then, this controller is validated under some state constraints by using tools from interval analysis. The second issue relates to underwater localization in a reliable manner. Ways to approach uncertainties and outliers gathered by acoustic sensors are studied. The interval analysis allows to obtain first results, and the fuzzy logic completes the approach by giving more suppleness in the prioritization of the constraints. Finally, some expérimentations are presented with different robots, and especially the localization of a ROV in a pool

Antunes, João Francisco Gonçalves 1965. "Aplicação de logica fuzzy para estimativa de area plantada da cultura de soja utilizando imagens AVHRR-NOAA." [s.n.], 2005. http://repositorio.unicamp.br/jspui/handle/REPOSIP/257216.

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Orientador: Jurandir Zullo Junior
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola
Made available in DSpace on 2018-08-05T08:07:53Z (GMT). No. of bitstreams: 1 Antunes_JoaoFranciscoGoncalves_M.pdf: 7524504 bytes, checksum: e36a3c933615dc4ef031bf119f6c09ff (MD5) Previous issue date: 2005
Resumo: A estimativa precisa com antecedência à época da colheita de áreas plantadas com culturas agrícolas, como a soja, é de fundamental importância para a economia brasileira. A previsão do escoamento e comercialização da produção agrícola é estratégica para o Brasil, pois estão diretamente relacionados com o planejamento, custos e preço. Com o recente avanço tecnológico na obtenção de dados por sensoriamento remoto orbital é possível melhorar a previsão de safras, diminuindo cada vez mais o nível de subjetividade. Embora designadas para fins meteorológicos, as imagens AVHRR-NOAA de elevada repetitividade temporal, têm sido utilizadas para o monitoramento agrícola. Porém, a sua baixa resolução espacial faz com que possa ocorrer a mistura espectral das classes de cobertura do solo dentro de um mesmo pixel e isso pode acarretar problemas de imprecisão na estimativa de área plantada de uma cultura agrícola. O objetivo principal do trabalho foi desenvolver uma metodologia de classificação automática com a aplicação de lógica fuzzy para o reconhecimento de padrões em imagens AVHRR-NOAA, utilizando índices de vegetação para estimar a área plantada de soja no nível sub-pixel. Para oito municípios produtores de soja da região oeste do Estado do Paraná, foi possível obter a estimativa de área no final de janeiro de 2004, com antecedência em relação à época da colheita, ao contrário dos levantamentos oficiais que se estendem até o final da safra, além de utilizarem dados subjetivos vindos do campo. As estimativas de área de soja baseadas em classificação fuzzy mostraram-se altamente correlacionadas com as estimativas de área de referência obtidas a partir da máscara de soja e por expansão direta, sendo um indicativo de boa precisão. E também apresentaram alta correlação, balizadas com as estimativas oficiais da SEAB/DERAL e do IBGE. Em ambas comparações, o nível de erro relativo geral foi aceitável. O sistema desenvolvido para processamento e geração de produtos das imagens AVHRR-NOAA mostrou-se uma ferramenta fundamental de infra-estrutura, por aliar automação e precisão a metodologia do trabalho
Abstract: An early accurate estimation of agricultural crop areas, such as soybean, is fundamental for the Brazilian economy. The draining forecast and the estimation of agricultural production commercialization are strategic to Brazil, since they are directly related to planning, costs and price. Recent technological progress of data acquisition from orbital remote sensing makes possible to improve harvest forecast, reducing more and more the level of subjectivity. Although designed for meteorological aims, the AVHRR-NOAA images of high temporal resolution, have been used for the crop monitoring. However, its low spatial resolution might cause the spectral mixture of the different land cover classes within the same pixel and it can lead to accuracy problems on crop area estimation. The main objective of the work was to develop an automatic classification methodology with the application of fuzzy logic for pattern recognition in AVHRR-NOAA images, using vegetation indices to estimate the soybean crop areas at sub-pixel level. For eight soybean producer counties in the West region of the Paraná State, it was possible to obtain the crop area estimation at the end of january 2004, prior to the harvest period, on the contrary of the official surveys that extend until the end of the harvest, besides using subjective data collected on the field. The soybean crop area estimation based on fuzzy classification showed to be highly correlated with the reference area estimation obtained from the soybean mask and by direct expansion, being an indicative of good accuracy. And also presented high correlation, marked out with the official estimations from SEAB/DERAL and IBGE. In both comparisons, the level of general relative error was acceptable. The system developed for processing and products generation of AVHRR-NOAA images had proved to be a fundamental infrastructure tool, due to its capacity to combine automation and accuracy to the work methodology
Mestrado
Planejamento e Desenvolvimento Rural Sustentável
Mestre em Engenharia Agrícola

Yang, Wei-Yang, and 楊維揚. "A Study of Fuzzy Set Theory in Triangulation Position Estimation–for Zigbee." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/44319520970676495630.

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碩士
東海大學
工業工程與經營資訊學系
96
This study demonstrates the use of fuzzy set theory in triangulation algorithm, using distance estimation (lateration) method, to estimate locations of objects, dependent on the measurement of signal strengths. The locations are taken as fuzzy numbers. The degree of the membership function of the fuzzy numbers attached describes the level of confidence for an object at an estimated position, so that it reduces the impact of positioning error caused by the variability and uncertainty of the received signal strength. Previous researches indicate that most of the studies apply data training method to reduce the positioning estimation error considering the characteristic value of the signal probability distribution. They seldom discuss the study regarding the fuzzy set theory used for location estimation. Furthermore, the use of fuzzy set theory in the study of location estimation was mainly presenting the fuzzy rules based on the fuzzy logic to infer the determination of the location but not directly estimating the locations which are fuzzy numbers from the signal strength fuzzy number. This study defines the signal strength fuzzy numbers according to the link quality indications and the time based on dataset operations. The distance fuzzy numbers between the objective and the known location are calculated, and then the distance fuzzy numbers in the lateration method of triangulation algorithms are applied to estimating the location and the scope of possible region containing the real location of the objective. In order to evaluate the effectiveness of this method for reducing the impact of positioning error caused by the uncertain changes of the received signal strength, it was experimented with practical data to demonstrate the operation and to statistics the encountered triangulation forms caused by the distortion of the received signals during positioning. In the experiments, fuzzifiered received signal strength was compared with crisp received signal strength used in triangulation location estimation. The results showed that the stability of the fuzzifiered received signal strength method is better, and that the estimation error is less.

"Estimation of Cost overrun Risk in Ýnterrnational Project by Using Fuzzy Set Theory." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606032/index.pdf.

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Salah, Ahmad. "Fuzzy Set-Based Contingency Estimating And Management." Thesis, 2012. http://spectrum.library.concordia.ca/973994/7/Salah_MASc_F2012.pdf.

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Contingency estimating and management are critical and necessary functions for successful delivery of construction projects. Considering such importance, academics and industry professionals proposed a wide range of methods for risk quantification and accordingly for contingency estimating. Considerably less work was directed to contingency management including its depletion to mitigate risk over project durations. Generally, there are two types of risks; 1) known risks which can be identified, evaluated, planned and budgeted for and 2) unknown risks which may occurred. These two categories of risks required a cost and time contingency, even if they weren’t planned for, in order to mitigate their impact in an orderly manner. In this respect, the importance of contingency management become critical in view of increasing project complexity and difficulty of estimating and/or allocating sufficient contingencies to mitigate risks encountered during project execution. This thesis focuses on the contingency management from two perspectives; estimating and depletion of contingency over project durations. A new method is developed using fuzzy sets theory, along with a set of measures, indices, and ratios to model the uncertainty inherent in this process and estimate cost contingencies. The uncertainties are expressed in the developed model using a set of measures and indicators including possibility measure, agreement index, fussiness measure, ambiguity measure, quality fuzzy number index, fuzziness expected value ratio, and ambiguity expected value ratio. These measures, indices, and ratios provide not only the possibility of having adequate contingency but also address issues of precision and vagueness associated with the uncertainty involved in a generic computational platform. The thesis, also, presents a comparison between fuzzy existing methods, Monte Carlo Simulation, PERT, and a proposed direct fuzzy set-based method. As to depletion, the thesis presents a management procedure focusing on depletion of the contingency. The developed procedure makes use of policies and procedures followed by leading construction organizations and owners of major constructed facilities. The developed method and its computational platform were coded using VB.net Programming. Two project examples drawn from the literature are analysed to demonstrate the use of developed method and to illustrate its capabilities beyond those of traditional Methods.

Книги з теми "Fuzzy set estimation":

Vasil'eva, Natal'ya. Mathematical models in the management of copper production: ideas, methods, examples. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014071.

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Presents the current status in modelling of metallurgical processes considered by the model the mathematical model used in the description of the processes of copper production and their classification. Set out a system of methods and models in the field of mathematical modeling of technological processes, including balance sheet, statistics, optimization models, forecasting models and predictive models. For specific technological processes are developed: the model of the balance of the cycle of pyrometallurgical production of copper, polynomial model for prediction of matte composition on the basis of the passive experiment, predictive model of quantitative estimation of the copper content in the matte based on fuzzy logic. Of interest to students, postgraduates, teachers of technical universities, engineers and research workers who use mathematical methods for processing of data of laboratory and industrial experiments.

Shu, Chang. Estimation régionale des débits de crues par la méthode ANFIS. Québec: INRS, Eau, terre et environnement, 2007.

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Ślusarski, Marek. Metody i modele oceny jakości danych przestrzennych. Publishing House of the University of Agriculture in Krakow, 2017. http://dx.doi.org/10.15576/978-83-66602-30-4.

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The quality of data collected in official spatial databases is crucial in making strategic decisions as well as in the implementation of planning and design works. Awareness of the level of the quality of these data is also important for individual users of official spatial data. The author presents methods and models of description and evaluation of the quality of spatial data collected in public registers. Data describing the space in the highest degree of detail, which are collected in three databases: land and buildings registry (EGiB), geodetic registry of the land infrastructure network (GESUT) and in database of topographic objects (BDOT500) were analyzed. The results of the research concerned selected aspects of activities in terms of the spatial data quality. These activities include: the assessment of the accuracy of data collected in official spatial databases; determination of the uncertainty of the area of registry parcels, analysis of the risk of damage to the underground infrastructure network due to the quality of spatial data, construction of the quality model of data collected in official databases and visualization of the phenomenon of uncertainty in spatial data. The evaluation of the accuracy of data collected in official, large-scale spatial databases was based on a representative sample of data. The test sample was a set of deviations of coordinates with three variables dX, dY and Dl – deviations from the X and Y coordinates and the length of the point offset vector of the test sample in relation to its position recognized as a faultless. The compatibility of empirical data accuracy distributions with models (theoretical distributions of random variables) was investigated and also the accuracy of the spatial data has been assessed by means of the methods resistant to the outliers. In the process of determination of the accuracy of spatial data collected in public registers, the author’s solution was used – resistant method of the relative frequency. Weight functions, which modify (to varying degree) the sizes of the vectors Dl – the lengths of the points offset vector of the test sample in relation to their position recognized as a faultless were proposed. From the scope of the uncertainty of estimation of the area of registry parcels the impact of the errors of the geodetic network points was determined (points of reference and of the higher class networks) and the effect of the correlation between the coordinates of the same point on the accuracy of the determined plot area. The scope of the correction was determined (in EGiB database) of the plots area, calculated on the basis of re-measurements, performed using equivalent techniques (in terms of accuracy). The analysis of the risk of damage to the underground infrastructure network due to the low quality of spatial data is another research topic presented in the paper. Three main factors have been identified that influence the value of this risk: incompleteness of spatial data sets and insufficient accuracy of determination of the horizontal and vertical position of underground infrastructure. A method for estimation of the project risk has been developed (quantitative and qualitative) and the author’s risk estimation technique, based on the idea of fuzzy logic was proposed. Maps (2D and 3D) of the risk of damage to the underground infrastructure network were developed in the form of large-scale thematic maps, presenting the design risk in qualitative and quantitative form. The data quality model is a set of rules used to describe the quality of these data sets. The model that has been proposed defines a standardized approach for assessing and reporting the quality of EGiB, GESUT and BDOT500 spatial data bases. Quantitative and qualitative rules (automatic, office and field) of data sets control were defined. The minimum sample size and the number of eligible nonconformities in random samples were determined. The data quality elements were described using the following descriptors: range, measure, result, and type and unit of value. Data quality studies were performed according to the users needs. The values of impact weights were determined by the hierarchical analytical process method (AHP). The harmonization of conceptual models of EGiB, GESUT and BDOT500 databases with BDOT10k database was analysed too. It was found that the downloading and supplying of the information in BDOT10k creation and update processes from the analyzed registers are limited. An effective approach to providing spatial data sets users with information concerning data uncertainty are cartographic visualization techniques. Based on the author’s own experience and research works on the quality of official spatial database data examination, the set of methods for visualization of the uncertainty of data bases EGiB, GESUT and BDOT500 was defined. This set includes visualization techniques designed to present three types of uncertainty: location, attribute values and time. Uncertainty of the position was defined (for surface, line, and point objects) using several (three to five) visual variables. Uncertainty of attribute values and time uncertainty, describing (for example) completeness or timeliness of sets, are presented by means of three graphical variables. The research problems presented in the paper are of cognitive and application importance. They indicate on the possibility of effective evaluation of the quality of spatial data collected in public registers and may be an important element of the expert system.

Частини книг з теми "Fuzzy set estimation":

Gu, Xingsheng, and Dazhong Sun. "A Soft Sensor Model Based on Rough Set Theory and Its Application in Estimation of Oxygen Concentration." In Fuzzy Systems and Knowledge Discovery, 1271–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539506_160.

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Tsataltzinos, T., L. Iliadis, and S. Spartalis. "A Generalized Fuzzy-Rough Set Application for Forest Fire Risk Estimation Feature Reduction." In IFIP Advances in Information and Communication Technology, 332–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23960-1_40.

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Oglu, Alekperov Ramiz Balashirin, and Salahli Vuqar Mamadali Oglu. "Estimation of Potential Locations of Trade Objects on the Basis of Fuzzy Set Theory." In Advances in Intelligent Systems and Computing, 228–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51156-2_28.

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Crespo, F. Javier, and Óscar Marbán. "On the Use of Tools Based on Fuzzy Set Theories in Parametric Software Cost Estimation." In Modeling Decisions for Artificial Intelligence, 129–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11681960_14.

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Baranov, Alexander, Elena Muzyko, and Victor Pavlov. "The Development of Methodology for Innovative Project Effectiveness Parameter Estimation in Direction of Fuzzy Set Application." In Emerging Issues in the Global Economy, 23–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71876-7_3.

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Kreinovich, Vladik. "Towards Faster Estimation of Statistics and ODEs Under Interval, P-Box, and Fuzzy Uncertainty: From Interval Computations to Rough Set-Related Computations." In Lecture Notes in Computer Science, 3–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21881-1_2.

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Kuo, Hsun-Chih, and Yu-Jau Lin. "The Optimal Estimation of Fuzziness Parameter in Fuzzy C-Means Algorithm." In Rough Sets, 566–75. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60837-2_45.

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Joronen, Tero. "Computational Theory of Meaning Articulation: A Human Estimation Approach to Fuzzy Arithmetic." In Views on Fuzzy Sets and Systems from Different Perspectives, 115–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-93802-6_6.

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Ren, Qun, Luc Baron, Marek Balazinski, and Krzysztof Jemielniak. "Reliable Tool Life Estimation with Multiple Acoustic Emission Signal Feature Selection and Integration Based on Type-2 Fuzzy Logic." In Advances in Type-2 Fuzzy Sets and Systems, 203–17. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6666-6_13.

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Yuan, Xinrui, Hairong Wang, and Jun Wang. "3D Single Person Pose Estimation Method Based on Deep Learning." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200726.

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In view of the significant effects of deep learning in graphics and image processing, research on human pose estimation methods using deep learning has attracted much attention, and many method models have been produced one after another. On the basis of tracking and in-depth study of domestic and foreign research results, this paper concentrates on 3D single person pose estimation methods, contrasts and analyzes three methods of end-to-end, staged and hybrid network models, and summarizes the characteristics of the methods. For evaluating method performance, set up an experimental environment, and utilize the Human3.6M data set to test several mainstream methods. The test results indicate that the hybrid network model method has a better performance in the field of human pose estimation.

Тези доповідей конференцій з теми "Fuzzy set estimation":

Oh, S. "Distributed spectral estimation using fuzzy set theory." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150150.

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Zhu, Weiping. "Loss rate estimation with incomplete data set." In 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2014. http://dx.doi.org/10.1109/fskd.2014.6980973.

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Xintao, Xia. "System Parameter Estimation and Performance Prediction Using Fuzzy-Set Theory." In 2007 8th International Conference on Electronic Measurement and Instruments. IEEE, 2007. http://dx.doi.org/10.1109/icemi.2007.4350605.

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Wang, Gang, Zhicheng Wang, Yufei Chen, Weidong Zhao, and Xianhui Liu. "Fuzzy Correspondences and Kernel Density Estimation for Contaminated Point Set Registration." In 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.338.

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He Qing and Zhang Jing. "Nonlinear state estimation in mobile robot using fuzzy set membership filter." In 2008 Chinese Control Conference (CCC). IEEE, 2008. http://dx.doi.org/10.1109/chicc.2008.4605477.

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Shao, Shili, He Zhang, and Changqing Liu. "Reachable Set Estimation for Fuzzy Cellular Neural Networks with Bounded Disturbances." In International Conference on Communication and Electronic Information Engineering (CEIE 2016). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/ceie-16.2017.86.

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Xia, Yingjie, Zhoumin Ye, Yiwen Fang, and Ting Zhang. "Parallelized extraction of traffic state estimation rules based on bootstrapping rough set." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6233736.

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Zeng, Xiaohui, and Huanglin Zeng. "A New Method of Qualitative Attributes estimation based on Fuzzy Rough Set." In 2nd International Conference on Computer Application and System Modeling. Paris, France: Atlantis Press, 2012. http://dx.doi.org/10.2991/iccasm.2012.372.

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Wittich, Felix, and Andreas Kroll. "Approximation of the Feasible Parameter Set in Bounded-Error Parameter Estimation of Takagi-Sugeno Fuzzy Models for Large Problems by Using a Ray Shooting Method." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882729.

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Wei Chai, Junfei Qiao, and Heng Wang. "Robust fault detection using set membership estimation and T-S fuzzy neural network." In 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6579949.

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Звіти організацій з теми "Fuzzy set estimation":

Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, June 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.