Relevant bibliographies by topics / Fuzzy randomness

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Fuzzy randomness'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Fuzzy randomness"

1

Möller, B. "Fuzzy randomness - a contribution to imprecise probability." ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 84, no. 10-11 (October 8, 2004): 754–64. http://dx.doi.org/10.1002/zamm.200410153.

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Cui, Nan, Shengli Liu, Dawu Gu, and Jian Weng. "Robustly reusable fuzzy extractor with imperfect randomness." Designs, Codes and Cryptography 89, no. 5 (March 22, 2021): 1017–59. http://dx.doi.org/10.1007/s10623-021-00843-1.

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Lee, Backjin, Akimasa Fujiwara, Yoriyasu Sugie, and Moon Namgung. "A Sequential Method for Combining Random Utility Model and Fuzzy Inference Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 7, no. 2 (June 20, 2003): 200–206. http://dx.doi.org/10.20965/jaciii.2003.p0200.

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In the analysis of choice behavior problem, uncertainty can be divided into two different types: randomness and vagueness. Random utility model and fuzzy inference model have been widely used to consider the randomness and the vagueness, respectively. Despite the necessity of simultaneously considering both uncertainties in choice behavior analysis, few literatures have tried to combine the two types of choice behavior models. Therefore, the aim of this paper is to suggest a model combining the randomness and the vagueness in the context of driver’s route choice behavior under traffic information. To estimate the combined model, a sequential method is suggested as follows: First, a latent class multinomial logit model (LCML) is developed to consider the randomness of route choice behaviors and to analyze the heterogeneity among drivers. Second, a fuzzy inference model is developed to consider the vagueness. Finally, the combined model is established by combining the estimation results of the LCML and the fuzzy inference models. The empirical results in this paper show that the combined model can contribute to enhance the explanatory power of the LCML model by effectively incorporating the randomness and the vagueness uncertainty in the choice behavior model.
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Hašková, Simona. "Randomness vs. fuzziness in managerial decision-making." SHS Web of Conferences 61 (2019): 01002. http://dx.doi.org/10.1051/shsconf/20196101002.

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Managers often deal with uncertainty of a different nature in their decision processes. They can encounter uncertainty in terms of randomness or fuzziness (i.e., mist, obscurity, inaccuracy or vagueness). In the first case (randomness), it can be described, for example, by probability distribution, in the second case (fuzziness) it cannot be characterized in such a way. The methodological part of the paper presents basic tools for dealing with the uncertainty of both of these types, which are techniques of probability theory and fuzzy approach technique. The original contribution of the theoretical part is the interpretation of these different techniques based on the existence of fundamental analogies between them. These techniques are then applied to the problem of the project valuation with its “internal” value. In the first case, the solution is the point value of the statistical E[PV], in the second case the triangular fuzzy number of the subjective E[PV]. The comparison of the results of both techniques shows that the fuzzy approach extends the standard outcome of a series useful information. This informative “superstructure” of the fuzzy approach compared to the standard solution is another original benefit of the paper.
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Barik, S. K., and M. P. Biswal. "Probabilistic Quadratic Programming Problems with Some Fuzzy Parameters." Advances in Operations Research 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/635282.

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We present a solution procedure for a quadratic programming problem with some probabilistic constraints where the model parameters are either triangular fuzzy number or trapezoidal fuzzy number. Randomness and fuzziness are present in some real-life situations, so it makes perfect sense to address decision making problem by using some specified random variables and fuzzy numbers. In the present paper, randomness is characterized by Weibull random variables and fuzziness is characterized by triangular and trapezoidal fuzzy number. A defuzzification method has been introduced for finding the crisp values of the fuzzy numbers using the proportional probability density function associated with the membership functions of these fuzzy numbers. An equivalent deterministic crisp model has been established in order to solve the proposed model. Finally, a numerical example is presented to illustrate the solution procedure.
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Jiang, Yizhang, Fu-Lai Chung, and Shitong Wang. "Enhanced fuzzy partitions vs data randomness in FCM." Journal of Intelligent & Fuzzy Systems 27, no. 4 (2014): 1639–48. http://dx.doi.org/10.3233/ifs-141130.

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Attarzadeh, Meghdad, David K. H. Chua, Michael Beer, and Ernest L. S. Abbott. "Fuzzy Randomness Simulation of Long-Term Infrastructure Projects." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 3, no. 3 (September 2017): 04017002. http://dx.doi.org/10.1061/ajrua6.0000902.

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Guo, R., and E. Love. "Reliability Modelling with Fuzzy Covariates." International Journal of Reliability, Quality and Safety Engineering 10, no. 02 (June 2003): 131–57. http://dx.doi.org/10.1142/s0218539303001056.

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In this research, we focus on covariate modelling to explore the interactions between industrial system and its enviroment in terms of the modelling fundamental characteristic — random and fuzzy uncertaity with an intention to decrease the fatal weakness of the modern dissection methodology. We extend the additive and multiplicative covariate models from these considering randomness alone into these considering both randomness and fuzziness in the sense as a mathematical extension to the existing covariate modelling. In terms of the form of logical function an engineering oriented fuzzy reliability model which could potentially count all the aspects associated with an operating system and its environment is proposed. Statistical estimation on the parameters of system fuzzy reliability is considered based on the general theory of the point processes. The impacts on the optimal plant maintenance from the engineering oriented fuzzy reliability modelling is also discussed. Finally we use an industrial example to illustrate the main theoretical developments.
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Li, Ling Ling, Fen Fen Zhu, Chun Wen Yang, and Zhi Gang Li. "Research on the Credibility of Fuzzy Reliability." Applied Mechanics and Materials 48-49 (February 2011): 984–88. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.984.

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According to the situation of randomness and fuzziness existing in the actual project, this paper proposed a reliability-credibility model based on fuzzy theory, possibility theory and credibility theory. At firstly, a reliability-possibility distribution curve was constructed by the traditional stress-strength interference model, then, a reliability distribution function was established based on this. This model built a bridge between credibility and reliability in handling randomness and fuzziness. The research results show that the model can hand the situation of random information and fuzzy information coexistence well, and the acceptable level of reliability and the most credible reliability value can be reflected directly in the reliability-credibility distribution curve. Compared with other methods, it obtains more reliability information, accords with the actual engineering situation and guides reliability engineering design much better.
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Möller, Bernd, Wolfgang Graf, and Michael Beer. "Safety assessment of structures in view of fuzzy randomness." Computers & Structures 81, no. 15 (July 2003): 1567–82. http://dx.doi.org/10.1016/s0045-7949(03)00147-0.

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Dissertations / Theses on the topic "Fuzzy randomness"

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Sickert, Jan-Uwe, Frank Steinigen, Steffen Freitag, Stephan Pannier, Andreas Hoffmann, Wolfgang Graf, and Michael Kaliske. "Sicherheitsbeurteilung und Entwurf von Tragwerken - numerische Analyse mit unscharfen Größen." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-78038.

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Im Beitrag werden Forschungsergebnisse zum numerischen Entwurf textilbewehrter Verstärkungsschichten zusammengefasst. Die Ergebnisse resultieren im Wesentlichen aus den Arbeiten der Teilprojekte D2-Numerische Simulation, E3-Sicherheitsbeurteilung und E4-Numerische Langzeitprognose des Sonderforschungsbereichs 528. Zusätzlich wird auf Transferleistungen verwiesen
The paper provides a summary of research results concerning numerical design approaches for textile reinforced structures. The outcome mainly results from the work done in the subprojects of the Collaborative Research Centre 528: D2-Numerical Simulation, E3-Reliability Assessment und E4-Numerical Long-term Prognosis. Further, the paper also points out the transfer potential
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Steinigen, Frank, Wolfgang Graf, Andreas Hoffmann, and Michael Kaliske. "Nachträglich textilverstärkte Stahlbetontragwerke — Strukturanalyse mit unscharfen Daten." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1244047124333-78222.

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Mit der Fuzzy-Stochastischen Finite-Elemente-Methode (FSFEM) kann die nachgewiesene stochastische und nichtstochastische Datenunschärfe des stahlbewehrten Altbetons und des Textilbeton bei der Strukturanalyse berücksichtigt werden. Die für die deterministische Analyse textilverstärkter Tragwerke auf der Basis des Multi-Referenzebenen-Modells (MRM) entwickelten finiten MRM-Elemente wurden zu FSMRM-Elementen weiterentwickelt. Das Stoffmodell des mit AR-Glas bewehrten Feinbetons wurde für textile Gelege aus Carbon erweitert. Die entwickelten Modelle und Algorithmen werden zur fuzzystochastischen Tragwerksanalyse textilverstärkter Tragwerke eingesetzt.
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Yi-JuLai and 賴薏如. "Constructing Fuzzy Regression Models by Aggregating the Concepts of Randomness and Fuzziness." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/33228339752997840863.

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碩士
國立成功大學
工業與資訊管理學系碩博士班
101
Regression analysis is one of the most important decision making tools allowing decision makers to analyze the relationship between input variables and output variables. Statistical regressions are expected to determine the relationships among a group of variables using probability distributions, assuming the uncertainty of data is due to randomness. In contrast, fuzzy regressions deal with imprecise data or indefinite relationships between variables, viewing this kind of uncertainty as fuzziness. Both statistical regressions and fuzzy regressions are widely applied in specific applicable fields. However, in a real world with complicated information, data is often accompanied with randomness and fuzziness simultaneously. Yet, there have been few studies of regression models that have discussed these two types of uncertainty at the same time. In this research, the concepts of randomness and fuzziness are aggregated and a fuzzy regression model concerning two types of uncertainty is built. First, fuzzy random variables (FRV) are represented as data with twofold uncertainty, viewing the most likely value of FRVs as randomness and the spread of FRVs as fuzziness. Then, two approaches for constructing a regression model are proposed. The first approach uses weighted fuzzy arithmetic to estimate the sum of deviations between predicted values and the observed values. Subsequently, regression coefficients are obtained under the least-squares criterion. The second approach is called the goal programming method. This approach uses distance criterion for error estimation between a predicted value and an observed value and constructs objective function and constraints, respectively. Furthermore, a fuzzy adjustment term is added in the proposed fuzzy regression model in this research in order to increase generalization and to reduce the total estimation error of the model.
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Chih-YiYeh and 葉至毅. "Constructing Control Charts with Fuzzy Random Variables through Concepts of Randomness and Fuzziness." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/73029474647856867774.

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碩士
國立成功大學
工業與資訊管理學系碩博士班
100
The control chart approach is a frequently used method for Statistical Process Control (SPC). By monitoring the manufacturing process using statistical concepts, the control chart is capable of detecting abnormalities during the process, allowing the supervisor to react and locate the causes of these before severe quality defects occur. However, with regard to the measuring of quality characteristics, there remain many uncertainties beyond the manufacturing process, such as the accuracy of the measurement tools or the subjective judgment of the operator. These uncertainties may be solved through the application of fuzzy theory, which represents quality characteristics using fuzzy numbers. This representation can include the information and phenomenon of fuzziness. In the past, many scholars have used fuzzy numbers derived from quantitative quality characteristics to develop fuzzy control charts, but no control charts that separate randomness and fuzziness have been documented in the literature. Therefore, this study takes uses various methods of developing fuzzy control charts from the literature and designs a randomness-fuzziness separated fuzzy control chart. This study begins by calculating the random values of fuzzy quality characteristics using the concept of defuzzification, and specifically uses the center of gravity and mean of maxima methods to calculate the representational values of randomness. Through the use of traditional control charts, these values are applied to monitor the randomness of the manufacturing process. The method used to calculate method the left and right spreads is improved, and then the revised values of the left and right spreads are combined to represent a fuzzy number. Again, using traditional control charts, the fuzzy numbers are used monitor the fuzziness of the manufacturing process. By using these two control charts to identify two different sources of uncertainties, the supervisor is able to distinguish between abnormalities in the manufacturing process that come from chance causes or the bias of quality measurements.
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Books on the topic "Fuzzy randomness"

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Möller, Bernd, and Michael Beer. Fuzzy Randomness. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07358-2.

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Möller, Bernd. Fuzzy Randomness: Uncertainty in Civil Engineering and Computational Mechanics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

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Möller, Bernd, and Michael Beer. Fuzzy Randomness: Uncertainty in Civil Engineering and Computational Mechanics (Engineering Online Library). Springer, 2004.

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Book chapters on the topic "Fuzzy randomness"

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Möller, Bernd, and Michael Beer. "Fuzzy and Fuzzy Stochastic Structural Analysis." In Fuzzy Randomness, 135–227. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07358-2_5.

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Möller, Bernd, and Michael Beer. "Introduction." In Fuzzy Randomness, 1–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07358-2_1.

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Möller, Bernd, and Michael Beer. "Mathematical Basics for the Formal Description of Uncertainty." In Fuzzy Randomness, 19–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07358-2_2.

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Möller, Bernd, and Michael Beer. "Description of Uncertain Structural Parameters as Fuzzy Variables." In Fuzzy Randomness, 90–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07358-2_3.

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Möller, Bernd, and Michael Beer. "Description of Uncertain Structural Parameters as Fuzzy Random Variables." In Fuzzy Randomness, 109–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07358-2_4.

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Möller, Bernd, and Michael Beer. "Fuzzy Probabilistic Safety Assessment." In Fuzzy Randomness, 228–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07358-2_6.

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Möller, Bernd, and Michael Beer. "Structural Design Based on Clustering." In Fuzzy Randomness, 273–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07358-2_7.

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Buckley, James J., and Leonard J. Jowers. "Tests for Randomness." In Monte Carlo Methods in Fuzzy Optimization, 43–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76290-4_5.

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Goodman, Irwin R., and Hung T. Nguyen. "Fuzziness and randomness." In Statistical Modeling, Analysis and Management of Fuzzy Data, 3–21. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1800-0_1.

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Gil, María Ángeles, and Pedro Gil. "Randomness and Fuzziness: Combined Better than Unified." In Enric Trillas: A Passion for Fuzzy Sets, 213–22. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16235-5_16.

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Conference papers on the topic "Fuzzy randomness"

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Gurevich, Yuri. "Impugning alleged randomness." In 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC). IEEE, 2015. http://dx.doi.org/10.1109/nafips-wconsc.2015.7284118.

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Ariffin, Suriyani, and Nor Azeala Mohd Yusof. "Randomness analysis on 3D-AES block cipher." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8393289.

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YOSHIDA, YUJI. "FUZZY STOPPING OF A SYSTEM WITH RANDOMNESS AND FUZZINESS." In Proceedings of the 4th International FLINS Conference. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812792631_0006.

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Liu, June. "Moment inequalities for hybrid events with fuzziness and randomness." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019515.

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Sun, Binxuan, Jiarong Luo, Shuangbao Shu, and Nan Yu. "Introduce randomness into AdaBoost for robust performance on noisy data." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569438.

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Beer, Michael, and Matthias Stein. "Bayesian Update With Fuzzy Information." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-62424.

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A realistic quantification of all input information is a basic requirement in order to obtain useful results from engineering analyses. The concept of quantification and the associated uncertainty model have to be selected in agreement with the amount and quality of the available information. For inconsistent information, a distinction between probabilistic and non-probabilistic characteristics is beneficial. In this distinction, uncertainty refers to probabilistic characteristics and non-probabilistic characteristics are summarized as imprecision. When uncertainty and imprecision occur simultaneously, the uncertainty model fuzzy randomness appears useful. In this paper, the fuzzy probabilistic model is utilized in a Bayesian approach to take account of imprecision in data and in prior expert knowledge. The propagation of imprecision and uncertainty is investigated for selected cases. The Bayesian approach extended to inconsistent information is demonstrated by means of an example.
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Zhang, Hong, Rui Hou, Lei Yi, Juan Meng, Zhisong Pan, and Yuhuan Zhou. "Encrypted data stream identification using randomness sparse representation and fuzzy Gaussian mixture model." In First International Workshop on Pattern Recognition, edited by Xudong Jiang, Guojian Chen, Genci Capi, and Chiharu Ishll. SPIE, 2016. http://dx.doi.org/10.1117/12.2242369.

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Son, Ji-Hwan, and Hyo-Sung Ahn. "Fuzzy Reward-Based Cooperative Reinforcement Learning for Bio-Insect and Artificial Robot Interaction." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86689.

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In this paper, we address our on-going research that is for interaction between artificial robots and a bio-insect. The research motivation and research goal were introduced in [1]. In order to report a progress of this project, this paper contains advanced framework and simulation results. When we did experiments using real bio-insects, their movement showed a little randomness. For this reason, fuzzy logic is employed to drive the model-free bio-insect towards a desired point. The framework formulated in this paper is based on fuzzy reward system and fuzzy expertise measurement system. Fuzzy reward system uses three inputs and an output resulting in numerical value within −1 to 1. Fuzzy expertise measurement system is inspired by area of expertise. In area of expertise method, it uses expertise measurement equation for finding expert agent. Based on area of expertise method, our method uses three expertise measurements to calculate score of individual agent. Based on this score, agents can share their intelligences with weighted scores. Simulation results demonstrate the validity of the framework established in this research.
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Li, Bing, Don R. Metzger, and Tim J. Nye. "Reliability Analysis of the Tube Hydroforming Process Using Fuzzy Sets Theory." In ASME/JSME 2004 Pressure Vessels and Piping Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/pvp2004-2999.

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Tube hydroforming currently enjoys increasingly widespread application in industry, especially in the automotive industries, because of several advantages over traditional methods. Reliability analysis as a probabilistic method to deal with the probability of the failure of the structure or the system has been widely used in industry. A new reliability analysis approach for the tube hydroforming process using the fuzzy sets theory is presented in this paper. The stress of the hydroformed tube is related to several parameters, such as geometry, material properties, and process parameters. In most cases, it is difficult to express in a mathematical formula, and its relative parameters are not random variables, but the uncertain variables that have not only randomness but also fuzziness. In this paper, the finite element method is applied as a numerical experiment tool to find the statistical property of the stress directly by a fuzzy linear regression method. Based on the fuzzy stress-random strength interference model, the fuzzy reliability of the tube hydroforming process can be evaluated. A tube hydroforming process for cross-extrusion is then introduced as an example to illustrate the approach. The result shows that this approach can be extended to a wide range of practical tube hydroforming process.
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GEORGESCU, VASILE. "ON HYBRIDIZATION OF PROBABILISTIC AND FUZZY APPROACHES TO PROPAGATE RANDOMNESS AND EPISTEMIC UNCERTAINTY IN RISK ASSESSMENT MODELS." In Proceedings of the MS'10 International Conference. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814324441_0006.

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