Relevant bibliographies by topics / Fuzzy variance

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Fuzzy variance'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Fuzzy variance"

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Tang, Wenjing, and Yitao Chen. "Variance and Semi-Variances of Regular Interval Type-2 Fuzzy Variables." Symmetry 14, no. 2 (2022): 278. http://dx.doi.org/10.3390/sym14020278.

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In this paper, we define the variance and semi-variances of regular interval type-2 fuzzy variables (RIT2-FVs) as well as derive a calculation formula of them based on the credibility distribution. Following the relationship between the variance and the semi-variances of the regular symmetric triangular interval type-2 fuzzy variables (RSTIT2-FVs), a special type of interval type-2 fuzzy variable is discovered and proved. Furthermore, for applying the two measures, we propose the operational law for the variance and semi-variances of the linear function of mutually independent RSTIT2-FVs. Some
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Zhang, Wei-Guo, and Ying-Luo Wang. "A Comparative Analysis of Possibilistic Variances and Covariances of Fuzzy Numbers." Fundamenta Informaticae 79, no. 1-2 (2007): 257–63. https://doi.org/10.3233/fun-2007-791-212.

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In this paper, we introduce a new crisp possibilistic variance and a new crisp possibilistic covariance of fuzzy numbers, which are different from those introduced by Carlsson and Fullér. We show that the possibilistic variance and covariance preserve many properties of variance and covariance in probability theory. Furthermore, we investigate the relationship between several crisp possibilistic variances and covariances of fuzzy numbers.
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P., Senthil Kumar, and Venkatesh A. "A MATHEMATICAL MODEL FOR THE EFFECT OF CORTICOSTERONE USING FUZZY EXPONENTIAL DISTRIBUTION." International Journal of Applied and Advanced Scientific Research 1, no. 1 (2016): 224–28. https://doi.org/10.5281/zenodo.223110.

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A fuzzy mathematical model was developed and used this model to calculate the expected mean and variance of Corticosterone level in the given time interval after lights onsets Releasing Hormone treatment. Formulae of fuzzy Exponential distribution and its α-cut sets were presented. Using fuzzy Exponential distribution, we showed that if the Lower α-cut of Mean and variance are increases when different alpha values and upper α-cut of Mean and variance are increases when different alpha values with respect to the time intervals.
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Ramasubramanian, S., and P. Mahendran. "Estimation of Hazard Rate and Mean Residual Life Ordering for Fuzzy Random Variable." Abstract and Applied Analysis 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/164795.

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L2-metric is used to find the distance between triangular fuzzy numbers. The mean and variance of a fuzzy random variable are also determined by this concept. The hazard rate is estimated and its relationship with mean residual life ordering of fuzzy random variable is investigated. Additionally, we have focused on deriving bivariate characterization of hazard rate ordering which explicitly involves pairwise interchange of two fuzzy random variablesXandY.
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Zainali, Z., M. G. Akbari, and H. Alizadeh Noughabi. "Intuitionistic fuzzy random variable and testing hypothesis about its variance." Soft Computing 19, no. 9 (2014): 2681–89. http://dx.doi.org/10.1007/s00500-014-1437-z.

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Wu, Hsien-Chung. "Analysis of variance for fuzzy data." International Journal of Systems Science 38, no. 3 (2007): 235–46. http://dx.doi.org/10.1080/00207720601157997.

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Pahade, Jagdish Kumar, and Manoj Jha. "Credibilistic variance and skewness of trapezoidal fuzzy variable and mean–variance–skewness model for portfolio selection." Results in Applied Mathematics 11 (August 2021): 100159. http://dx.doi.org/10.1016/j.rinam.2021.100159.

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Dzuche, Justin, Christian Deffo Tassak, Jules Sadefo Kamdem, and Louis Aimé Fono. "The First Moments and Semi-Moments of Fuzzy Variables Based on an Optimism-Pessimism Measure with Application for Portfolio Selection." New Mathematics and Natural Computation 16, no. 02 (2020): 271–90. http://dx.doi.org/10.1142/s1793005720500167.

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Possibility, necessity and credibility measures are used in the literature in order to deal with imprecision. Recently, Yang and Iwamura [L. Yang and K. Iwamura, Applied Mathematical Science 2(46) (2008) 2271–2288] introduced a new measure as convex linear combination of possibility and necessity measures and they determined some of its axioms. In this paper, we introduce characteristics (parameters) of a fuzzy variable based on that measure, namely, expected value, variance, semi-variance, skewness, kurtosis and semi-kurtosis. We determine some properties of these characteristics and we compu
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Yang, Mingrui, Wen Huang, and Dongyi Zou. "Fuzzy Pricing of European Options Based on Constant Elasticity of Variance Process." Journal of Statistics and Economics 1, no. 2 (2024): 199–205. http://dx.doi.org/10.62517/jse.202411229.

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Based on the assumption that the stock price follows the CEV process, this article uses fuzzy mathematics theory to discuss the price of European options. As the financial market is constantly fluctuating, the parameter of the stock price following the CEV process should not be a constant. Therefore, considering fuzzy interest rates, fuzzy stock prices, and fuzzy initial volatility, under the assumption of fuzzy parameters, the price of the obtained option is a fuzzy number. This article first derives the pricing formula for European options with stock prices following the CEV process. Then, u
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Heng, Aimin, Qian Chen, and Yingshuang Tan. "Fuzzy Optimization of Option Pricing Model and Its Application in Land Expropriation." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/635898.

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Option pricing is irreversible, fuzzy, and flexible. The fuzzy measure which is used for real option pricing is a useful supplement to the traditional real option pricing method. Based on the review of the concepts of the mean and variance of trapezoidal fuzzy number and the combination with the Carlsson-Fuller model, the trapezoidal fuzzy variable can be used to represent the current price of land expropriation and the sale price of land on the option day. Fuzzy Black-Scholes option pricing model can be constructed under fuzzy environment and problems also can be solved and discussed through
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Dissertations / Theses on the topic "Fuzzy variance"

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Adewunmi, Adrian. "Selection of simulation variance reduction techniques through a fuzzy expert system." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11260/.

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In this thesis, the design and development of a decision support system for the selection of a variance reduction technique for discrete event simulation studies is presented. In addition, the performance of variance reduction techniques as stand alone and combined application has been investigated. The aim of this research is to mimic the process of human decision making through an expert system and also handle the ambiguity associated with representing human expert knowledge through fuzzy logic. The result is a fuzzy expert system which was subjected to three different validation tests, the
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Lu, I.-Chen (Jennifer). "Robust portfolio management with multiple financial analysts." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/18045.

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Portfolio selection theory, developed by Markowitz (1952), is one of the best known and widely applied methods for allocating funds among possible investment choices, where investment decision making is a trade-off between the expected return and risk of the portfolio. Many portfolio selection models have been developed on the basis of Markowitz's theory. Most of them assume that complete investment information is available and that it can be accurately extracted from the historical data. However, this complete information never exists in reality. There are many kinds of ambiguity and vaguenes
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Visa, Sofia. "Comparative Study of Methods for Linguistic Modeling of Numerical Data." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1043254774.

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Abdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering." Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.

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This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The
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Li, Xiang. "Variable modeling of fuzzy phenomena with industrial applications." Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/4383.

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Kwan, Alvin Chi Ming. "A framework for mapping constraint satisfaction problems to solution methods." Thesis, University of Essex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339436.

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Naidoo, Puramanathan. "The control of a multi-variable industrial process, by means of intelligent technology." Thesis, Port Elizabeth Technikon, 2001. http://hdl.handle.net/10948/48.

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Conventional control systems express control solutions by means of expressions, usually mathematically based. In order to completely express the control solution, a vast amount of data is required. In contrast, knowledge-based solutions require far less plant data and mathematical expression. This reduces development time proportionally. In addition, because this type of processing does not require involved calculations, processing speed is increased, since rule process is separate and all processes can be performed simultaneously. These results in improved product quality, better plant effici
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Pokorný, Tomáš. "Využití teorie fuzzy množin a jejich rozšíření v metodě TOPSIS." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-205686.

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This master's thesis deals with extensions of TOPSIS method, which is one of methods for multi-criteria evaluation of alternatives. These extensions use theory of fuzzy sets (FS) and their futher extensions to interval-valued (IVFS), intuitionistic (IFS) and hesitant (HFS) fuzzy sets and their combinations (IVIFS, IVIHFS). Significant part of this thesis explains the principle of fuzzy sets and their generalizations. Descriptions of operators for aggregations of grades of membership has very important role here. Next, very short description of multi-criteria evaluation problems and detailed de
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Nyongesa, Henry Okola. "Genetic based machine learning allied to multi-variable fuzzy control of anaesthesia." Thesis, University of Sheffield, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295759.

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Khor, Jeen Ghee. "An intelligent controller for synchronous generators." Thesis, De Montfort University, 1999. http://hdl.handle.net/2086/4125.

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Books on the topic "Fuzzy variance"

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Cao, Bing-Yuan. Optimal Models and Methods with Fuzzy Quantities. Springer-Verlag Berlin Heidelberg, 2010.

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Cao, Bing-Yuan. Optimal Models and Methods with Fuzzy Quantities. Springer Berlin / Heidelberg, 2012.

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Cao, Bing-Yuan. Optimal Models and Methods with Fuzzy Quantities. Springer, 2010.

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Gopal, M. Digital Control and State Variable Methods. Conventional and Neuro-Fuzzy Control Systems. 2nd ed. Tata McGraw-Hill Publishing Co., 2003.

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Variable-speed induction motor drives for aircraft environmental control compressors. National Aeronautics and Space Administration, 1996.

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Book chapters on the topic "Fuzzy variance"

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Buckley, James J. "Estimate μ, Variance Known." In Fuzzy Statistics. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39919-3_3.

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Buckley, James J. "Estimate μ, Variance Unknown." In Fuzzy Statistics. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39919-3_4.

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Buckley, James J. "Tests on µ, Variance Known." In Fuzzy Statistics. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39919-3_12.

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Buckley, James J. "Tests on µ, Variance Unknown." In Fuzzy Statistics. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39919-3_13.

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Jalili-Kharaajoo, Mahdi, and Farhad Besharati. "Fuzzy Variance Analysis Model." In Computer and Information Sciences - ISCIS 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39737-3_67.

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Berkachy, Rédina. "Fuzzy Analysis of Variance." In The Signed Distance Measure in Fuzzy Statistical Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76916-1_8.

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Luukka, Pasi, Jan Stoklasa, and Mikael Collan. "Transformation of Variance to Possibilistic Variance and Vice Versa." In Advances in Fuzzy Logic and Technology 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66824-6_40.

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Qin, Zhongfeng. "Fuzzy Random Mean-Variance Adjusting Model." In Uncertainty and Operations Research. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1810-7_9.

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Korotkikh, Galina. "On Understanding the Structure of Variance-Covariance Matrix for Dealing with Fuzziness in Financial Markets." In Fuzzy Logic. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1806-2_15.

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Ivani, R., S. H. Sanaei Nejad, B. Ghahraman, A. R. Astaraei, and H. Feizi. "A Practical Application of Fuzzy Analysis of Variance in Agriculture." In Fuzzy Statistical Decision-Making. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39014-7_17.

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Conference papers on the topic "Fuzzy variance"

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Aloui, Ameni, Hela Hachicha, and Ezzeddine Zagrouba. "MAAVSL-HIT2FS: Multi-Agent Based Architecture for Variable Speed Limit Decision making based on Hierarchical Interval Type 2 Fuzzy System." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611974.

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Pizzi, Nick J., Aleksander Demko, and Witold Pedrycz. "Variance analysis and biomedical pattern classification." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584204.

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Villaverde, Karen, and Gang Xiang. "Estimating variance under interval and fuzzy uncertainty: Parallel algorithms." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630496.

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Chang, Wen-Jer, and Bo-Jyun Huang. "Variance and passivity constrained fuzzy control for continuous perturbed fuzzy systems with multiplicative noises." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891570.

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Carlsson, Christer, and Robert Fuller. "Possibilistic mean value and variance of fuzzy numbers: Some examples of application." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277230.

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Rodriguez-Fdez, Ismael, Manuel Mucientes, and Alberto Bugarin. "An instance selection algorithm for regression and its application in variance reduction." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622486.

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Hasuike, Takashi. "Equilibrium pricing vector based on the hybrid mean-variance theory with investor's subjectivity." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584743.

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Swei, Sean S. M., and Mohammad A. Ayoubi. "LMI-based fuzzy optimal variance control of airfoil model subject to input constraints." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015469.

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Pouzols, Federico Montesino, Amaury Lendasse, and Angel Barriga. "Fuzzy inference based autoregressors for time series prediction using nonparametric residual variance estimation." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630432.

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Mahdi Jalili Kharaajoo, Mahdi Jalili Kharaajoo, and Hassan Ebrahimirad Hassan Ebrahimirad. "A note on fuzzy variance analysis model." In 2003 International Symposium on Signals, Circuits and Systems. IEEE, 2003. http://dx.doi.org/10.1109/scs.2003.1226960.

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Reports on the topic "Fuzzy variance"

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Seale, Maria, R. Salter, Natàlia Garcia-Reyero,, and Alicia Ruvinsky. A fuzzy epigenetic model for representing degradation in engineered systems. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45582.

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Degradation processes are implicated in a large number of system failures, and are crucial to understanding issues related to reliability and safety. Systems typically degrade in response to stressors, such as physical or chemical environmental conditions, which can vary widely for identical units that are deployed in different places or for different uses. This situational variance makes it difficult to develop accurate physics-based or data-driven models to assess and predict the system health status of individual components. To address this issue, we propose a fuzzy set model for representi
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Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4470.

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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For
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Taylor, Malcolm S., and Steven B. Boswell. An Application of a Fuzzy Random Variable to Vulnerability Modeling. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada216707.

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