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Journal articles on the topic 'Interval uncertainty'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Hwang, Yan-An, and Yu-Hsien Liao. "A Resolution Under Interval Uncertainty." Mathematics 13, no. 5 (2025): 762. https://doi.org/10.3390/math13050762.

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Traditional transferable utility (TU) games assume precise real-valued utilities for coalition outcomes, but real-world situations often involve uncertainty or imprecision. Interval TU games extend the classical framework by representing utilities and payoffs as closed intervals, leveraging interval arithmetic to address inherent ambiguities in data. This paper reviews the theoretical foundations of interval TU games and explores allocating solutions under uncertainty. Central to this study is the adaptation of consistency, a fundamental property in game-theoretical resolutions, to the interva

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Kuzmin, Evgeny A. "Logic of Interval Uncertainty." Modern Applied Science 8, no. 5 (2014): 152. http://dx.doi.org/10.5539/mas.v8n5p152.

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The scientific category of uncertainty refers to that group of terms, an interpretation of which is not unambiguous and exact. In non-eliminability of the category soft content barrier there is an objective transition to the interval uncertainty. This research is an attempt to solve the issue of estimating the interval uncertainty based on methods of a logical analysis and a comparison. The approach presented by the paper is opposed to known methods of a mechanical selection of values following a given function. In the course of the research, there has been introduced a concept of the “tenvers

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Huynh, Van-Nam, and Vladik Kreinovich. "Interval/Probabilistic Uncertainty: Editorial." International Journal of Approximate Reasoning 50, no. 8 (2009): 1149–50. http://dx.doi.org/10.1016/j.ijar.2009.06.005.

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Alparslan-Gök, S. Zeynep, Silvia Miquel, and Stef H. Tijs. "Cooperation under interval uncertainty." Mathematical Methods of Operations Research 69, no. 1 (2008): 99–109. http://dx.doi.org/10.1007/s00186-008-0211-3.

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5

YAGER, RONALD R., and VLADIK KREINOVICH. "FAIR DIVISION UNDER INTERVAL UNCERTAINTY." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 05 (2000): 611–18. http://dx.doi.org/10.1142/s0218488500000423.

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It is often necessary to divide a certain amount of money between n participants, i.e., to assign, to each participant, a certain portionwi≥0 of the whole sum (so that w1+⋯+ wn=1). In some situations, from the fairness requirements, we can uniquely determine these "weights" wi. However, in some other situations, general considerations do not allow us to uniquely determine these weights, we only know the intervals[Formula: see text] of possible fair weights. We show that natural fairness requirements enable us to choose unique weights from these intervals; as a result, we present an algorithm f

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Hend, Dawood. "On Some Algebraic and Order-Theoretic Aspects of Machine Interval Arithmetic." Online Mathematics Journal 01, no. 02 (2019): 1–13. https://doi.org/10.5281/zenodo.2656089.

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Interval arithmetic is a fundamental and reliable mathematical machinery for scientific computing and for addressing uncertainty in general. In order to apply interval mathematics to real life uncertainty problems, one needs a computerized (machine) version thereof, and so, this article is devoted to some mathematical notions concerning the algebraic system of machine interval arithmetic. After formalizing some purely mathematical ingredients of particular importance for the purpose at hand, we give formal characterizations of the algebras of real intervals and machine intervals along with des

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7

Manring, Noah D. "Measuring Pump Efficiency: Uncertainty Considerations." Journal of Energy Resources Technology 127, no. 4 (2005): 280–84. http://dx.doi.org/10.1115/1.1926311.

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The objective of this paper is to analyze the uncertainty associated with pump efficiency measurements and to determine reasonable confidence intervals for these data. In the past, many industrial sales and some pieces of academic research have been based upon the experimental data of pump efficiencies; yet few have questioned the accuracy of the experimental data and no one has provided a confidence interval which reflects the range of uncertainty in the measurement. In this paper, a method for calculating this confidence interval is presented and it is shown that substantially large confiden

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Severens, Johan L., Theo M. De Boo, and Emmy M. Konst. "UNCERTAINTY OF INCREMENTAL COST-EFFECTIVENESS RATIOS." International Journal of Technology Assessment in Health Care 15, no. 3 (1999): 608–14. http://dx.doi.org/10.1017/s0266462399153157.

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Objective: To compare different methods to estimate the confidence interval of the incremental cost-effectiveness ratio (ICER).Methods: The adequacy of Fieller intervals and three methods for calculating bootstrap intervals are compared based on a simulation of 10,000 trials, using data from one trial.Results: Both Fieller and bootstrap methods lead to unsatisfactory results when the difference in effectiveness is approximately zero. Where this difference is significant, the four methods for calculating confidence intervals for ICER do not give very different results, but Fieller's interval pe

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Entani, Tomoe, and Kazutomi Sugihara. "Uncertainty index based interval assignment by Interval AHP." European Journal of Operational Research 219, no. 2 (2012): 379–85. http://dx.doi.org/10.1016/j.ejor.2012.01.010.

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Aminifar, Sadegh, and Arjuna Marzuki. "Uncertainty in Interval Type-2 Fuzzy Systems." Mathematical Problems in Engineering 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/452780.

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This paper studies uncertainty and its effect on system response displacement. The paper also describes how IT2MFs (interval type-2 membership functions) differentiate from T1MFs (type-1 membership functions) by adding uncertainty. The effect of uncertainty is modeled clearly by introducing a technique that describes how uncertainty causes membership degree reduction and changing the fuzzy word meanings in fuzzy logic controllers (FLCs). Several criteria are discussed for the measurement of the imbalance rate of internal uncertainty and its effect on system behavior. Uncertainty removal is int

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11

BRANZEI, R., S. TIJS, and S. Z. ALPARSLAN GÖK. "HOW TO HANDLE INTERVAL SOLUTIONS FOR COOPERATIVE INTERVAL GAMES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 02 (2010): 123–32. http://dx.doi.org/10.1142/s0218488510006441.

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Uncertainty accompanies almost every situation in our lives and it influences our decisions. On many occasions uncertainty is so severe that we can only predict some upper and lower bounds for the outcome of our (collaborative) actions, i.e., payoffs lie in some intervals. Cooperative interval games have been proved useful for solving reward/cost sharing problems in situations with interval data in a cooperative environment. In this paper we propose two procedures for cooperative interval games. Both transform an interval allocation, i.e., a payoff vector whose components are compact intervals

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12

Jalal-Kamali, Ali, and Vladik Kreinovich. "Estimating correlation under interval uncertainty." Mechanical Systems and Signal Processing 37, no. 1-2 (2013): 43–53. http://dx.doi.org/10.1016/j.ymssp.2012.12.003.

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Mareček, Jakub, Peter Richtárik, and Martin Takáč. "Matrix completion under interval uncertainty." European Journal of Operational Research 256, no. 1 (2017): 35–43. http://dx.doi.org/10.1016/j.ejor.2016.07.014.

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14

Hansen, Bruce E. "Interval forecasts and parameter uncertainty." Journal of Econometrics 135, no. 1-2 (2006): 377–98. http://dx.doi.org/10.1016/j.jeconom.2005.07.030.

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Levin, V. I. "Nonlinear optimization under interval uncertainty." Cybernetics and Systems Analysis 35, no. 2 (1999): 297–306. http://dx.doi.org/10.1007/bf02733477.

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Løhre, Erik, Marie Juanchich, Miroslav Sirota, Karl Halvor Teigen, and Theodore G. Shepherd. "Climate Scientists’ Wide Prediction Intervals May Be More Likely but Are Perceived to Be Less Certain." Weather, Climate, and Society 11, no. 3 (2019): 565–75. http://dx.doi.org/10.1175/wcas-d-18-0136.1.

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Abstract The use of interval forecasts allows climate scientists to issue predictions with high levels of certainty even for areas fraught with uncertainty, since wide intervals are objectively more likely to capture the truth than narrow intervals. However, wide intervals are also less informative about what the outcome will be than narrow intervals, implying a lack of knowledge or subjective uncertainty in the forecaster. In six experiments, we investigate how laypeople perceive the (un)certainty associated with wide and narrow interval forecasts, and find that the preference for accuracy (s

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Fujita, Takaaki, and Florentin Smarandache. "Interval Graphs and Proper Interval Graphs in Fuzzy and Neutrosophic Graphs." Information Sciences with Applications 5 (January 28, 2025): 11–32. https://doi.org/10.61356/j.iswa.2025.5476.

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Interval graphs represent vertices as intervals on the real line, with edges denoting overlapping intervals, while proper interval graphs prevent one interval from being fully contained within another. This paper explores interval and proper interval graphs within the frameworks of fuzzy, neutrosophic, and Turiyam Neutrosophic graphs. We examine how these types of graphs can represent relationships involving uncertainty and imprecision, focusing on their properties and relationships.

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Sotiropoulos, Dimitris G., and Konstantinos Tserpes. "Interval-Based Computation of the Uncertainty in the Mechanical Properties and the Failure Analysis of Unidirectional Composite Materials." Mathematical and Computational Applications 27, no. 3 (2022): 38. http://dx.doi.org/10.3390/mca27030038.

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An interval-based method is presented to evaluate the uncertainty in the computed mechanical properties and the failure assessment of composite unidirectional (UD) laminates. The method was applied to two composite laminates: a carbon/epoxy and a glass/epoxy. The mechanical properties of the UD lamina were derived using simplified micromechanical equations. An uncertainty level of ±5% was assumed for the input properties of the constituents. The global minimum and maximum values of the properties were computed using an interval branch-and-bound algorithm. Interval arithmetic operations were us

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Uhlig, Steffen, Bertrand Colson, and Petra Gowik. "Measurement uncertainty interval in case of a known relationship between precision and mean." F1000Research 12 (August 17, 2023): 996. http://dx.doi.org/10.12688/f1000research.139111.1.

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Background: Measurement uncertainty is typically expressed in terms of a symmetric interval y±U, where y denotes the measurement result and U the expanded uncertainty. However, in the case of heteroscedasticity, symmetric uncertainty intervals can be misleading. In this paper, a different approach for the calculation of uncertainty intervals is introduced. Methods: This approach is applicable when a validation study has been conducted with samples with known concentrations. In a first step, test results are obtained at the different known concentration levels. Then, on the basis of precision e

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Kahraman, Cengiz, Basar Oztaysi, and Sezi Cevik Onar. "Interval-Valued Intuitionistic Fuzzy Confidence Intervals." Journal of Intelligent Systems 28, no. 2 (2019): 307–19. http://dx.doi.org/10.1515/jisys-2017-0139.

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Abstract Confidence intervals are useful tools for statistical decision-making purposes. In case of incomplete and vague data, fuzzy confidence intervals can be used for decision making under uncertainty. In this paper, we develop interval-valued intuitionistic fuzzy (IVIF) confidence intervals for population mean, population proportion, differences in means of two populations, and differences in proportions of two populations. The developed IVIF intervals can be used in cases of both finite and infinite population sizes. The developed fuzzy confidence intervals are equivalent decision-making

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Stepanov, Vladimir, and Scott Ferson. "Agent-based models under uncertainty." F1000Research 12 (July 14, 2023): 834. http://dx.doi.org/10.12688/f1000research.135249.1.

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Background: Monte Carlo (MC) is often used when trying to assess the consequences of uncertainty in agent-based models (ABMs). However, this approach is not appropriate when the uncertainty is epistemic rather than aleatory, that is, when it represents a lack of knowledge rather than variation. The free-for-all battleship simulation modelled here is inspired by the children’s battleship game, where each battleship is an agent. Methods: The models contrast an MC implementation against an interval implementation for epistemic uncertainty. In this case, our epistemic uncertainty is in the form of

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Stepanov, Vladimir, and Scott Ferson. "Agent-based models under uncertainty." F1000Research 12 (March 14, 2024): 834. http://dx.doi.org/10.12688/f1000research.135249.3.

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Background Monte Carlo (MC) is often used when trying to assess the consequences of uncertainty in agent-based models (ABMs). However, this approach is not appropriate when the uncertainty is epistemic rather than aleatory, that is, when it represents a lack of knowledge rather than variation. The free-for-all battleship simulation modelled here is inspired by the children’s battleship game, where each battleship is an agent. Methods The models contrast an MC implementation against an interval implementation for epistemic uncertainty. In this case, our epistemic uncertainty is in the form of a

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Stepanov, Vladimir, and Scott Ferson. "Agent-based models under uncertainty." F1000Research 12 (August 31, 2023): 834. http://dx.doi.org/10.12688/f1000research.135249.2.

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Background: Monte Carlo (MC) is often used when trying to assess the consequences of uncertainty in agent-based models (ABMs). However, this approach is not appropriate when the uncertainty is epistemic rather than aleatory, that is, when it represents a lack of knowledge rather than variation. The free-for-all battleship simulation modelled here is inspired by the children’s battleship game, where each battleship is an agent. Methods: The models contrast an MC implementation against an interval implementation for epistemic uncertainty. In this case, our epistemic uncertainty is in the form of

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Yuan, Meng, Xu Lin, Junzo Watada, and Vladik Kreinovich. "Minimax Portfolio Optimization Under Interval Uncertainty." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 5 (2015): 575–80. http://dx.doi.org/10.20965/jaciii.2015.p0575.

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In the 1950s, Markowitz proposed to combine different investment instruments to design a portfolio that either maximizes the expected return under constraints on volatility (risk) or minimizes the risk under given expected return. Markowitz’s formulas are still widely used in financial practice. However, these formulas assume that we know the exact values of expected return and variance for each instrument, and that we know the exact covariance of every two instruments. In practice, we only know these values with some uncertainty. Often, we only know the lower and upper bounds on these values

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Nazin, Sergey A., and Boris T. Polyak*. "Interval parameter estimation under model uncertainty." Mathematical and Computer Modelling of Dynamical Systems 11, no. 2 (2005): 225–37. http://dx.doi.org/10.1080/138950500069243.

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Wong, S. K. M., L. S. Wang, and Y. Y. Yao. "ON MODELING UNCERTAINTY WITH INTERVAL STRUCTURES." Computational Intelligence 11, no. 2 (1995): 406–26. http://dx.doi.org/10.1111/j.1467-8640.1995.tb00041.x.

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Mohamed Amine, Benhari, and Kaicer Mohammed. "Analysis of uncertainty in the Leontief model by interval arithmetic." Statistics, Optimization & Information Computing 13, no. 5 (2025): 2011–26. https://doi.org/10.19139/soic-2310-5070-2279.

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This paper presents an innovative strategy to enhance the precision of economic projections through the integration of interval arithmetic into the Leontief model. We emphasise the utilisation of the Gauss-Seidel method for solving linear systems with interval coefficients. In this paper, we present a method that use the Gauss-Seidel approach to effectively solve linear systems consisting of interval coefficients. This technique enhances traditional methods by incorporating potential value intervals, in addition to exact numerical values. The result is a more precise reflection of uncertainty

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QU, XiaoZhang, GuiPing LIU, Xu HAN, and JiChu YANG. "Uncertain optimum design of aerodynamic performance of fan with interval uncertainty." SCIENTIA SINICA Technologica 47, no. 9 (2017): 955–64. http://dx.doi.org/10.1360/n092016-00250.

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El Obadi, Saadia, and Silvia Miquel. "Uncertainty in Information Market Games." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, Supp01 (2020): 11–29. http://dx.doi.org/10.1142/s0218488520400024.

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A new product can be produced and sold in a market thanks to the entrance of a patent holder into the market. This market is divided into submarkets controlled by only some firms and the profit attainable in each submarket is uncertain. In this paper, this situation is studied by means of cooperative games under interval uncertainty. We consider different ways of allocating the interval profit among the firms. One of these is the interval [Formula: see text]-value, which is defined for interval games satisfying some conditions. Efficient interval solutions in terms of the market data are provi

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Zhu, Lixuan, Ping Ju, and Yiping Yu. "Analytic Interval Prediction of Power System Dynamic under Interval Uncertainty." Journal of Physics: Conference Series 2427, no. 1 (2023): 012031. http://dx.doi.org/10.1088/1742-6596/2427/1/012031.

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Abstract With widespread access to renewable energy sources and active loads such as electric vehicles, uncertainty problems have gradually become a prominent problem in the power system. However, the conventional stochastic differential equation (SDE) model is not comprehensive in describing the randomness of disturbances, and the solution of novel models generally relies on numerical calculations. To improve the modeling accuracy and the calculation effectiveness, this paper utilizes intervals to model stochastic continuous disturbances and proposes an analytic method based on Taylor series

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Jiang, Chao, Chun-Ming Fu, Bing-Yu Ni, and Xu Han. "Interval arithmetic operations for uncertainty analysis with correlated interval variables." Acta Mechanica Sinica 32, no. 4 (2015): 743–52. http://dx.doi.org/10.1007/s10409-015-0525-3.

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Zaman, Kais, and Saraf Anika Kritee. "An Optimization-Based Approach to Calculate Confidence Interval on Mean Value with Interval Data." Journal of Optimization 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/768932.

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In this paper, we propose a methodology for construction of confidence interval on mean values with interval data for input variable in uncertainty analysis and design optimization problems. The construction of confidence interval with interval data is known as a combinatorial optimization problem. Finding confidence bounds on the mean with interval data has been generally considered an NP hard problem, because it includes a search among the combinations of multiple values of the variables, including interval endpoints. In this paper, we present efficient algorithms based on continuous optimiz

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Karimanzira, Divas. "Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration." Knowledge 4, no. 4 (2024): 462–80. http://dx.doi.org/10.3390/knowledge4040025.

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In this study, we extend our previous work on a two-dimensional convolutional neural network (2DCNN) for spatial prediction of groundwater nitrate, focusing on improving uncertainty quantification. Our enhanced model incorporates a fully probabilistic Bayesian framework and a structure aimed at optimizing both specific value predictions and predictive intervals (PIs). We implemented the Prediction Interval Validation and Estimation Network based on Quality Definition (2DCNN-QD) to refine the accuracy of probabilistic predictions and reduce the width of the prediction intervals. Applied to a mo

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Stefopoulos, Georgios, Stylianos Rigas, Panagiotis Tsirikoglou, and Anestis I. Kalfas. "Evaluation of pressure and species concentration measurement using uncertainty propagation." E3S Web of Conferences 345 (2022): 02008. http://dx.doi.org/10.1051/e3sconf/202234502008.

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This paper presents a probabilistic uncertainity evaluation method as described in the Guide to the Expression of Uncertainty in Measurements (GUM) and its application to probe measurements on pressure and fuel concentration. All sources of unceratinties are expressed as probability distributions. Consequently, the overall standard uncertainty of the quantity can be calculated using the Gaussian error propagation formula. The result of the uncertainty evaluation yields the most probable value of the measurand and describes its distribution in terms of rectangular (standard uncertainty) or gaus

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Pan, Yalin, Jun Huang, Feng Li, and Chuxiong Yan. "Aerodynamic robust optimization of flying wing aircraft based on interval method." Aircraft Engineering and Aerospace Technology 89, no. 3 (2017): 491–97. http://dx.doi.org/10.1108/aeat-09-2016-0145.

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Purpose The purpose of this paper is to propose a robust optimization strategy to deal with the aerodynamic optimization issue, which does not need a large sum of information on the uncertainty of input parameters. Design/methodology/approach Interval numbers were adopted to describe the uncertain input, which only requires bounds and does not necessarily need probability distributions. Based on the method, model outputs were also regarded as intervals. To identify a better solution, an order relation was used to rank interval numbers. Findings Based on intervals analysis method, the uncertain

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Imholz, Maurice, Dirk Vandepitte, and David Moens. "Analysis of the Effect of Uncertain Clamping Stiffness on the Dynamical Behaviour of Structures Using Interval Field Methods." Applied Mechanics and Materials 807 (November 2015): 195–204. http://dx.doi.org/10.4028/www.scientific.net/amm.807.195.

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In uncertainty calculation, the inability of interval parameters to take into account mutual dependency is a major shortcoming. When parameters with a geometric perspective are involved, the construction of a model using intervals at discrete locations not only increases the problem dimensionality unnecessarily, but it also assumes no dependency whatsoever, including unrealistic parameter combinations leading to results that probably overestimate the true uncertainty. The concept of modelling uncertainty with a geometric aspect using interval fields eliminates this problem by defining basis fu

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Shary, Sergey P. "Data fitting problem under interval uncertainty in data." Industrial laboratory. Diagnostics of materials 86, no. 1 (2020): 62–74. http://dx.doi.org/10.26896/1028-6861-2020-86-1-62-74.

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We consider the data fitting problem under uncertainty, which is not described by probabilistic laws, but is limited in magnitude and has an interval character, i.e., is expressed by the intervals of possible data values. The most general case is considered when the intervals represent the measurement results both in independent (predictor) variables and in the dependent (criterial) variables. The concepts of weak and strong compatibility of data and parameters of functional dependence are introduced. It is shown that the resulting formulations of problems are reduced to the study and estimati

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Karunakar, Perumandla, and Snehashish Chakraverty. "Solution of interval shallow water wave equations using homotopy perturbation method." Engineering Computations 35, no. 4 (2018): 1610–24. http://dx.doi.org/10.1108/ec-12-2016-0449.

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Purpose This paper aims to present solutions of uncertain linear and non-linear shallow water wave equations. The uncertainty has been taken as interval and one-dimensional interval shallow water wave equations have been solved by homotopy perturbation method (HPM). In this study, basin depth and initial conditions have been taken as interval and the single parametric concept has been used to handle the interval uncertainty. Design/methodology/approach HPM has been used to solve interval shallow water wave equation with the help of single parametric concept. Findings Previously, few authors fo

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Jusot, Jean-François. "An update of serial interval estimates for COVID-19: a meta-analysis." 4open 5 (2022): 16. http://dx.doi.org/10.1051/fopen/2022017.

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Background: Serial interval (SI) is one of the most important parameter for COVID-19 modelling purposes as it is related to the reproduction rate of the infection. The first meta-analysis of serial interval were performed with a range of uncertainty in the estimate. This meta-analysis aimed to reduce the uncertainty estimates by assessing publications over a longer period. Methods: A literature search was performed for articles published between 1st December 2019 and 15th February 2022. It retrieved 117 eligible studies containing some 80 for 90 serial interval estimates. A random effects mode

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Levin, Sara B., and William H. Farmer. "Evaluation of Uncertainty Intervals for Daily, Statistically Derived Streamflow Estimates at Ungaged Basins across the Continental U.S." Water 12, no. 5 (2020): 1390. http://dx.doi.org/10.3390/w12051390.

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Streamflow estimation methods that transfer information from an index gage to an ungauged site are commonly used; however, uncertainty in daily streamflow estimates are often not adequately quantified. In this study, daily streamflow was simulated at 1331 validation streamgauges across the continental United States using four transfer-based streamflow estimation methods. Empirical 95 percent uncertainty intervals were computed for estimated daily streamflows. Uncertainty intervals were evaluated for reliability, sharpness, and overall ability to accurately quantify the uncertainty inherent in

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Orlecka-Sikora, Beata, and Stanislaw Lasocki. "Interval Estimation of Seismic Hazard Parameters." Pure and Applied Geophysics 174 (November 1, 2016): 779–91. https://doi.org/10.1007/s00024-016-1419-4.

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The paper considers Poisson temporal occurrence of earthquakes and presents a way to integrate uncertainties of the estimates of mean activity rate and magnitude cumulative distribution function in the interval estimation of the most widely used seismic hazard functions, such as the exceedance probability and the mean return period. The proposed algorithm can be used either when the Gutenberg–Richter model of magnitude distribution is accepted or when the nonparametric estimation is in use. When the Gutenberg–Richter model of magnitude distribution is used the interval estimation o

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Su, Jing Bo, Hong Bing Liu, Hui De Zhao, and Dong Zhang. "Research on Interval Reversible Inverse Analysis Method Based on Interval Parameters." Advanced Materials Research 706-708 (June 2013): 556–59. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.556.

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In this paper, the interval analysis method is introduced and an uncertainty inverse analysis method is presented. The intervals of unknown parameters can be obtained by the input of measured data. Even for few measured data, the analysis results can be also obtained by the inverse analysis method. And the analysis results can be applied to appraise the uncertainty in interval. Based on parameter perturbation, the reversible inverse analysis model is proposed for linear-elastic problems. A numerical example is given to illustrate the validity of the present method. The influence is illustrated

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Aminifar, Sadegh. "Uncertainty Avoider Interval Type II Defuzzification Method." Mathematical Problems in Engineering 2020 (July 8, 2020): 1–16. http://dx.doi.org/10.1155/2020/5812163.

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One of the IT2FS (interval type-2 fuzzy system) defuzzification methods uses the iterative KM algorithm. Because of the iterative nature of KM-type reduction, it may be a computational bottleneck for the real-time applications of IT2FSs. There are several other interval type-2 defuzzification methods suffering from lack of meaningful relationship between membership function uncertainties and changing of system output due to lack of clearly defined variables related to uncertainty in their methods. In this paper, a new approach for IT2FS defuzzification is presented by reconfiguring interval ty

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Liao, Yu-Hsien. "Solutions, Potentializability and Axiomatizations under Interval Uncertainty." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 04 (2014): 605–13. http://dx.doi.org/10.1142/s0218488514500305.

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In the framework of interval transferable-utility (TU) games, we propose an equivalence theorem to characterize the family of all interval solutions that admit a potential. Further, we also provide several axiomatizations of the interval Shapley value based on this equivalence theorem.

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Vasuki, B., M. Umapathy, and A. R. Senthilkumarr. "UNCERTAINTY ANALYSIS OF STRAIN GAGE CIRCUITS: INTERVAL METHOD AND INTERVAL ALGORITHM." International Journal on Smart Sensing and Intelligent Systems 2, no. 3 (2009): 477–89. http://dx.doi.org/10.21307/ijssis-2017-362.

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BRANZEI, RODICA, DINKO DIMITROV, STEFAN PICKL, and STEF TIJS. "HOW TO COPE WITH DIVISION PROBLEMS UNDER INTERVAL UNCERTAINTY OF CLAIMS?" International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 02 (2004): 191–200. http://dx.doi.org/10.1142/s021848850400276x.

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The paper deals with division situations where individual claims can vary within closed intervals. Uncertainty of claims is removed by weighting in a consistent way the upper and lower bounds of the claim intervals. Deterministic division problems with the obtained compromise claims are then considered and classical division rules from the bankruptcy literature are used to generate several procedures leading to efficient and reasonable rules for division problems under interval uncertainty of claims.

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La, Van N. T., and David D. L. Minh. "Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation." International Journal of Molecular Sciences 24, no. 20 (2023): 15074. http://dx.doi.org/10.3390/ijms242015074.

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We compare several different methods to quantify the uncertainty of binding parameters estimated from isothermal titration calorimetry data: the asymptotic standard error from maximum likelihood estimation, error propagation based on a first-order Taylor series expansion, and the Bayesian credible interval. When the methods are applied to simulated experiments and to measurements of Mg(II) binding to EDTA, the asymptotic standard error underestimates the uncertainty in the free energy and enthalpy of binding. Error propagation overestimates the uncertainty for both quantities, except in the si

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Ben-Tal, Aharon, and Arkadi Nemirovski. "On Tractable Approximations of Uncertain Linear Matrix Inequalities Affected by Interval Uncertainty." SIAM Journal on Optimization 12, no. 3 (2002): 811–33. http://dx.doi.org/10.1137/s1052623400374756.

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Zhou, Changcong, Chenghu Tang, Fuchao Liu, and Wenxuan Wang. "A Probabilistic Representation Method for Interval Uncertainty." International Journal of Computational Methods 15, no. 05 (2018): 1850038. http://dx.doi.org/10.1142/s021987621850038x.

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In this work, we consider the interval uncertainty from the probabilistic point of view, focusing on the establishment of probabilistic representation of interval uncertainty. A model-free sampling technique is first introduced, which can be used to produce a considerably larger sample from a given small sample. To make sure the local statistical characteristics of these two samples coincide, an improved model-free sampling technique is introduced based on probability weighted moments. The improved model-free sampling technique is then applied to obtain a large sample based on interval data, o

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Jinlai, Lv, Zhang Hui, Fan Wenlei, and Du Xiaoping. "Uncertainty Measures in Interval Ordered Information Systems." Journal of Applied Sciences 13, no. 17 (2013): 3522–27. http://dx.doi.org/10.3923/jas.2013.3522.3527.

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