Journal articles: 'Fuzzy randomness'

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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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Graf, Wolfgang, Christian Jenkel, Stephan Pannier, Jan Uwe Sickert, and Frank Steinigen. "Numerical structural monitoring with the uncertainty model fuzzy randomness." International Journal of Reliability and Safety 3, no. 1/2/3 (2009): 218. http://dx.doi.org/10.1504/ijrs.2009.026842.

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Chen, Liang-Hsuan, and Chia-Jung Chang. "Approaches for Measurement System Analysis Considering Randomness and Fuzziness." International Journal of Fuzzy System Applications 9, no. 2 (April 2020): 98–131. http://dx.doi.org/10.4018/ijfsa.2020040105.

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For some quality inspection practices, subjective judgements based on the inspectors' experience and knowledge, such as visual inspection, may be required for some particular quality characteristics. This kind of measurement system, including its associated randomness and fuzziness, should be assessed by Measurement system analysis (MSA) before its application. For such purpose, this article represents observations with randomness and fuzziness from MSAs as fuzzy random variables, and then two pairs of descriptive parameters, i.e., expected value and variance, are derived. Then, the relationship of the total sum of squares of factors is proven to hold, so that fuzzy analysis of variance (FANOVA) in terms of gauge repeatability and reproducibility can be developed. The proposed approach has the advantage that FANOVA is developed based on the relationship of the total sum of squares of factors, considering randomness and fuzziness. A real case in the semiconductor packaging industry is used to demonstrate the applicability of the proposed approaches to MSA.

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Georgescu, Vasile. "Joint propagation of ontological and epistemic uncertainty across risk assessment and fuzzy time series models." Computer Science and Information Systems 11, no. 2 (2014): 881–904. http://dx.doi.org/10.2298/csis121215048g.

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This paper discusses hybrid probabilistic and fuzzy set approaches to propagating randomness and imprecision in risk assessment and fuzzy time series models. Stochastic and Computational Intelligence methods, such as Probability bounds analysis, Fuzzy a-levels analysis, Fuzzy random vectors, Wavelets decomposition and Wavelets Networks are combined to capture different kinds of uncertainty. Their most appropriate applications are probabilistic risk assessments carried out in terms of probability distributions with imprecise parameters and stochastic processes modeled in terms of fuzzy time series.

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Wang, Ya Jun, and Wo Hua Zhang. "Super Gravity Dam Generalized Damage Study." Advanced Materials Research 479-481 (February 2012): 421–25. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.421.

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Fuzzy sub-space, with analysis on generalized uncertainty of damage, is setup in this paper when topological consistency of damage fuzzy and randomness on [0,1] scale being demonstrated deeply. Furthermore, deduced under fuzzy characteristics translation are three fuzzy analytical models of damage functional, namely, half depressed distribution, swing distribution, combined swing distribution, by which, fuzzy extension territory on damage evolution is formulated here. With the representation of damage variable ß probabilistic distribution as well as formulation on stochastic sub-space of damage variable, expended on the basis of extension criterion and fuzzy probability is damage model defined within generalized uncertain space, by which, introduced is fuzzy probabilistic integral algorithm of generalized uncertain damage variable that could be simulated by the forthcoming fuzzy stochastic damage constitution model based on three fuzzy functional models before. Moreover, in order to realize the joint of fuzzy input and output procedure on generalized uncertain damage variable calculation, fuzzy self-adapting stochastic damage reliability algorithm is, with the update on fuzzy stochastic finite element method within standard normal distribution probabilistic space by the help of foregoing fuzzy stochastic damage constitution model, offered in this paper on the basis of equivalent-normalization and orthogonal design theory. 3-dimension fuzzy stochastic damage mechanical status of numerical model of Longtan Rolled-Concrete Dam is researched here by fuzzy stochastic damage finite element method program under property authority. Random field parameters’ statistical dependence and non-normality are considered comprehensively in fuzzy stochastic damage model of this paper, by which, damage uncertainty’s proper development and conception expansion as well as fuzzy and randomness of mechanics are hybridized overall in fuzzy stochastic damage analysis process.

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Huang, Han-Chen, and Xiaojun Yang. "A Comparative Investigation of Type-2 Fuzzy Sets, Nonstationary Fuzzy Sets and Cloud Models." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 02 (April 2016): 213–27. http://dx.doi.org/10.1142/s0218488516500112.

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Since Zadeh introduced fuzzy sets, a lot of extensions of this concept have been proposed, such as type-2 fuzzy sets, nonstationary fuzzy sets, and cloud models, to represent higher levels of uncertainty. This paper provides a comparative investigation of type-2 fuzzy sets, nonstationary fuzzy sets, and cloud models. Type-2 fuzzy sets study the fuzziness of the membership function (MF) using primary MF and secondary MF based on analytic mathematical methods; nonstationary fuzzy sets study the randomness of the MF using primary MF and variation function based on type-1 fuzzy sets theory; cloud models study the randomness of the distribution of samples in the universe and generate random membership grades (MGs) using two random variables based on probability and statistic mathematical methods. They all concentrate on dealing with the uncertainty of the MF or the MG which type-1 fuzzy sets do not consider, and thus have many similarities. Moreover, we find out that, the same qualitative concept “moderate amount” can be represented by an interval type-2 fuzzy set, a nonstationary fuzzy set or a normal cloud model, respectively. Then, we propose a unified mathematical expression for the interval type-2 fuzzy set, nonstationary fuzzy set and normal cloud model. On the other hand, we also find out that, the theory fundament and underlying motivations of these models are quite different. Therefore, We summarize detailed comparisons of distinctive properties of type-2 fuzzy sets, nonstationary fuzzy sets, and cloud models. Further, we study their diverse characteristics of distributions of MGs across vertical slices. The comparative investigation shows that these models are complementary to describe the uncertainty from different points of view. Thus, this paper provides a fundamental contribution and makes a basic reference for knowledge representation and other applications with uncertainty.

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Nematian, Javad, and Seyed Salar Ghotb. "Mathematical Programming for Modelling Green Supply Chains Under Randomness and Fuzziness." International Journal of Fuzzy System Applications 6, no. 1 (January 2017): 56–85. http://dx.doi.org/10.4018/ijfsa.2017010104.

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Nowadays by growing concerns about environmental problems, businesses and industries are under pressure to decrease their negative impact on environment, consequently firms and industries have to reconsider about their activities and make their business compatible with environment. So industries should green their supply chains to optimize economic and environmental concerns, but because of uncertainty in the real world like inconsistency of world economy, the process of greening supply chains can be more complex. To optimize total costs and the unfavourable sides of supply chains simultaneously in an uncertain situation, this paper presents a multi-objective mixed integer programming with fuzzy random variables (FRVs) and by using fuzzy theory and fuzzy random chance-constrained programming (FRCCP), the proposed model is converted to deterministic model. This paper can be also suitable for decision making with optimistic, pessimistic and realistic notion. Finally, a numerical example is presented to illustrate the model.

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QIN, ZHONGFENG, DAVID Z. W. WANG, and XIANG LI. "MEAN-SEMIVARIANCE MODELS FOR PORTFOLIO OPTIMIZATION PROBLEM WITH MIXED UNCERTAINTY OF FUZZINESS AND RANDOMNESS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, supp01 (July 2013): 127–39. http://dx.doi.org/10.1142/s0218488513400102.

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In practice, security returns cannot be accurately predicted due to lack of historical data. Therefore, statistical methods and experts' experience are always integrated to estimate future security returns, which are hereinafter regarded as random fuzzy variables. Random fuzzy variable is a powerful tool to deal with the portfolio optimization problem including stochastic parameters with ambiguous expected returns. In this paper, we first define the semivariance of random fuzzy variable and prove its several properties. By considering the semivariance as a risk measure, we establish the mean-semivariance models for portfolio optimization problem with random fuzzy returns. We design a hybrid algorithm with random fuzzy simulation to solve the proposed models in general cases. Finally, we present a numerical example and compare the results to illustrate the mean-semivariance model and the effectiveness of the algorithm.

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Dmitry Kurtener, Dmitry, and Paul Sukhanov. "Evaluation of Agricultural Soil Resources Using Fuzzy Modeling." Journal of Agricultural Science 6, no. 4 (March 15, 2014): 199. http://dx.doi.org/10.5539/jas.v6n4p199.

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Assessment of agricultural soil resources is a very difficult because exists knowledge is much fuzzy. Developed fuzzy model is effective tool for dealing with randomness and uncertainties. This model is based on a two-level system of fuzzy indicators. The first level - is individual fuzzy indicators (IFI), reflecting the assessment of individual characteristics. Second level - is combined fuzzy indicators (CFI), reflecting a combination of individual indicators. IFI are developed for the four characteristics of the soil (humus, amount absorbed cations, acidity (pH) and physical clay content). The proposed method is illustrated with a simple example.

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Waliv, Rahul, and Hemant Umap. "Fuzzy stochastic inventory model for deteriorating item." Yugoslav Journal of Operations Research 27, no. 1 (2017): 91–97. http://dx.doi.org/10.2298/yjor150330010w.

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A multi item profit maximization inventory model is developed in fuzzy stochastic environment. Demand is taken as Stock dependent demand. Available storage space is assumed to be imprecise and vague in nature. Impreciseness has been expressed by linear membership function. Purchasing cost and investment constraint are considered to be random and their randomness is expressed by normal distribution. The model has been formulated as a fuzzy stochastic programming problem and reduced to corresponding equivalent fuzzy linear programming problem. The model has been solved by using fuzzy linear programming technique and illustrated numerically.

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He, Xiao Song. "Adaptive Fuzzy PID Control for Cutter." Applied Mechanics and Materials 310 (February 2013): 506–9. http://dx.doi.org/10.4028/www.scientific.net/amm.310.506.

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It is very difficult to accurately control the speed of paper roller and sword roller of the paper cutting machine, and the proportions of two paces control have greater randomness. Alterable fuzzy-PID (proportion integral derivative) control arithmetic is put forward based on PLC. The alterable fuzzy controller and the PI adjustor are realized by PLC (Programmable Logic Controller). The satisfied results are achieved with PID control and adaptive fuzzy control. The simulation results show that the two schemes are satisfying. And the two schemes have their own characteristics.

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Liu, Xiao. "Reliability Analysis of Engineering Structures." Applied Mechanics and Materials 333-335 (July 2013): 2262–65. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.2262.

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Introduces the concept and content of engineering structural reliability and reliability and reliable indexes, and considering the engineering structure reliability analysis of randomness and fuzziness, the fuzzy random reliability analysis model was established

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Liu, Yian-Kui, and Baoding Liu. "Expected Value Operator of Random Fuzzy Variable and Random Fuzzy Expected Value Models." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, no. 02 (April 2003): 195–215. http://dx.doi.org/10.1142/s0218488503002016.

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Random fuzzy variable is mapping from a possibility space to a collection of random variables. This paper first presents a new definition of the expected value operator of a random fuzzy variable, and proves the linearity of the operator. Then, a random fuzzy simulation approach, which combines fuzzy simulation and random simulation, is designed to estimate the expected value of a random fuzzy variable. Based on the new expected value operator, three types of random fuzzy expected value models are presented to model decision systems where fuzziness and randomness appear simultaneously. In addition, random fuzzy simulation, neural networks and genetic algorithm are integrated to produce a hybrid intelligent algorithm for solving those random fuzzy expected valued models. Finally, three numerical examples are provided to illustrate the feasibility and the effectiveness of the proposed algorithm.

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Sampath, S. "Hybrid Binomial Distribution." International Journal of Fuzzy System Applications 2, no. 4 (October 2012): 64–75. http://dx.doi.org/10.4018/ijfsa.2012100104.

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Buckley and Eslami (2003) introduced a discrete distribution, namely Fuzzy Binomial distribution, that is intended to suit situations wherein impreciseness and randomness coexist. Using the approach of Liu (2008), a hybrid (combination of imprecision and randomness) version of Binomial distribution is developed. With the help of genetic algorithms the process of computing the chance distributions, expectation and variance of the distribution that is developed in this paper are illustrated. Illustrative examples are given to justify the usefulness of the distribution.

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Xu, Shi Lian, Qi Ying Pan, Gui Ping Hu, and Ren Ping Xu. "The Fuzzy Judgment for the Wearing Life Distrituion of the Rolling Bearing." Advanced Materials Research 146-147 (October 2010): 257–61. http://dx.doi.org/10.4028/www.scientific.net/amr.146-147.257.

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the wearing of the rolling bearing is actually associated with both randomness and fuzziness. In this paper, we analyze the fuzziness of the experimental data of the wearing life of the rolling bearing and discuss its fuzzy subsets, membership function and fuzzy identification. We take the wearing life of the 102 rolling bearing for example, study the normal fuzzy set and three-parameter Weibull fuzzy set and conduct the identification for the probability distribution. In particular, we also develop an innovative method to identify the experimental data of the wearing life of the rolling bearing.

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Figueroa Garcia, Juan Carlos, and Jhoan Sebastian Tenjo García. "FRand: MATLAB Toolbox for Fuzzy Random Number Simulation." Ingeniería 25, no. 1 (March 12, 2020): 38–49. http://dx.doi.org/10.14483/23448393.15620.

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Context: This paper presents a MATLAB code implementation and the GUI (General User Interface) for fuzzy random variable generation. Based on previous theoretical results and applications, a MATLAB toolbox has been developed and tested for selected membership functions. Method: A two–step methodology was used: i) a MATLAB toolbox was implemented to be used as interface and ii) all .m functions are available to be used as normal code. The main goal is to provide graphical and code–efficient tools to users. Results: The main obtained results are the MATLAB GUI and code. In addition, some experiments were ran to evaluate its capabilities and some randomness statistical tests were successfully performed. Conclusions: Satisfactory results were obtained from the implementation of the MATLAB code/toolbox. All randomness tests were accepted and all performed experiments shown stability of the toolbox even for large samples (>10.000). Also, the code/toolbox are available online. Acknowledgements: The authors would like to thank to the Prof. M Sc. Miguel Melgarejo and Prof. Jos´e Jairo Soriano–Mendez sincerely for their interest and invaluable support, and a special gratefulness is given to all members of LAMIC.

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Liu, Ying, Xiaozhong Li, and Jianbin Li. "Reliability Analysis of Random Fuzzy Unrepairable Systems." Discrete Dynamics in Nature and Society 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/625985.

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The lifetimes of components in unrepairable systems are considered as random fuzzy variables since randomness and fuzziness are often merged with each other. Then we establish the fundamental mathematical models of random fuzzy unrepairable systems, including series systems, parallel systems, series-parallel systems, parallel-series systems, and cold standby systems with absolutely reliable conversion switches. Furthermore, the expressions of reliability and mean time to failure (MTTF) are given for the above five random fuzzy unrepairable systems, respectively. Finally, numerical examples are given to show the application in a lighting lamp system and a hi-fi system.

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Türkşen, Özlem. "Analysis of Response Surface Model Parameters with Bayesian Approach and Fuzzy Approach." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 01 (February 2016): 109–22. http://dx.doi.org/10.1142/s0218488516500069.

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Response Surface Methodology (RSM) is a group of mathematical and statistical methods used for exploring the optimum operating conditions through statistical design of experiments. Obtaining the suitable analytical model between input variables and one or more responses is the main stage in RSM studies. When the response has replications, these replicated values may cause error. The proper modeling approach should be preferred according to the source of error, which is randomness or measurement error. In this study, Bayesian approach and fuzzy approach are used to estimate the model parameters in which the error becomes randomness and measurement error, respectively. The novelty of this study is analysis of response surface model parameters, which are obtained by using Bayesian approach and fuzzy approach, through interval analysis. The interpretation of model parameters and the comparison of modeling approaches are evaluated by interval arithmetic metrics. The suggested approaches are applied on replicated response measured two data sets and the results are discussed.

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Wanke, P., C. P. Barros, and A. Emrouznejad. "A comparison between stochastic DEA and fuzzy DEA approaches: revisiting efficiency in Angolan banks." RAIRO - Operations Research 52, no. 1 (January 2018): 285–303. http://dx.doi.org/10.1051/ro/2016065.

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Performance analysis has become a vital part of the management practices in the banking industry. There are numerous applications using DEA models to estimate efficiency in banking, and most of them assume that inputs and outputs are known with absolute precision. Here, we compare Stochastic-DEA and Fuzzy-DEA models to assess, respectively, how the underlying randomness and fuzziness impact efficiency levels. The proposed models have been demonstrated using an application in Angolan banks. Findings reveal that conclusions with respect to the ranking of DMUs may vary substantially depending upon the type of the model chosen, although efficiency scores are similar to some extent when compared within the ambits of Stochastic-DEA and Fuzzy-DEA models. Additionally, modeling choices on fuzziness, rather than on randomness, appears to be the most critical source for variations in efficiency rankings. Managerial implications for Angolan banks are also explored.

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Wu, Liang, Yaming Zhuang, and Xiaojing Lin. "Credit Derivatives Pricing Model for Fuzzy Financial Market." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/879185.

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With various categories of fuzziness in the market, the factors that influence credit derivatives pricing include not only the characteristic of randomness but also nonrandom fuzziness. Thus, it is necessary to bring fuzziness into the process of credit derivatives pricing. Based on fuzzy process theory, this paper first brings fuzziness into credit derivatives pricing, discusses some pricing formulas of credit derivatives, and puts forward a One-Factor Fuzzy Copula function which builds a foundation for portfolio credit products pricing. Some numerical calculating samples are presented as well.

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Graf, Wolfgang, and Jan Uwe Sickert. "Time-Dependent Fuzzy Stochastic Reliability Analysis of Structures." Applied Mechanics and Materials 104 (September 2011): 45–54. http://dx.doi.org/10.4028/www.scientific.net/amm.104.45.

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The paper reviews the development of reliability assessment in structural analysis under consideration of the non-traditional uncertainty model fuzzy randomness. Starting froma discussion of sources of variability and imprecision, uncertainty models are introduced. On this basis, numerical approaches are displayed for uncertain structural analysis and reliability assessment. Thereby, variations in time are considered which results in a time-dependent reliability measure. Capacity and applicability of the approaches are demonstrated by means of an example.

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Meng, Qing Bo, Yi Xin Yin, and Gui Ling Qiao. "Multi-Model Adaptation Fuzzy Control for the Deep Sea Walking Hydraulic Control System." Advanced Materials Research 383-390 (November 2011): 558–64. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.558.

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To accommodate deep sea walking wheel the complex characteristics of deep sea environment such as randomness, non-linear and variability, the algorithm of Multi-Model-Reference Adaptation Fuzzy Control is presented to run the walking wheel system steadily. This control method incorporates the multiple reference models, fuzzy control and the conventional PID control; it runs efficiently by the control compensation deduced by the error of different phase-plane zones with the guidance of reference models.

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Mei, Wei. "Bridging Probability and Possibility via Bayesian Theorem." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 04 (August 2014): 615–26. http://dx.doi.org/10.1142/s0218488514500317.

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The connection between probability and possibility has been studied for decades. Several studies have intended to provide a unified interpretation for these concepts; other studies attempt to discover their transformation relationship. This paper addresses these problems using a unified perspective. By extending the viewpoint of interpreting the grade of membership as a conditional probability, we introduce the conditional probability mass function and likelihood mass function to describe randomness and fuzziness, respectively. We draw the conclusion that conditional probability is undetermined itself and can be used for describing either randomness or fuzziness, depending on how it is interpreted. A fuzzy Bayesian theorem is presented based on the fuzziness interpretation of conditional probability. Additionally, a probability-possibility conversion is derived by assigning different interpretations for conditional probability of the Bayesian theorem, which is notably similar to Klir's normalized transformation. An example of target recognition demonstrates that the fuzzy Bayesian classifier outperforms the usual Bayesian classifier.

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Gao, Sudi, Yueying Luo, and Tan Yang. "Research on River Water Environmental Capacity Based on Triangular Fuzzy Technology." E3S Web of Conferences 236 (2021): 03018. http://dx.doi.org/10.1051/e3sconf/202123603018.

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Based on the randomness and ambiguity characteristics of the river water environment system, as well as the lack and inaccuracy of data information, the water environment system parameters are defined as triangular fuzzy numbers. On this basis, by fuzzing the parameters of the conventional deterministic model, a fuzzy model for calculating river water environmental capacity is established. According to this model, the river water environment capacity in the form of triangular fuzzy numbers can be calculated. According to the requirements of a given level of credibility, the water environment capacity can be further converted from triangular fuzzy numbers to interval values. Research shows that compared with conventional deterministic methods, the results obtained are more scientific and reasonable

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Wang, Lei, Ya Fei Ma, and Jian Ren Zhang. "Probabilistic Analysis of Corrosion-Induced Resistance Degradation of Reinforced Concrete Bridge Beam under Incomplete Information." Advanced Materials Research 163-167 (December 2010): 3193–99. http://dx.doi.org/10.4028/www.scientific.net/amr.163-167.3193.

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The influencing information of structural resistance cannot be exactly inspected due to limitation of the experimental means, time and space. The study must be conducted by incomplete information and the uncertainty is enhanced. The uncertainty consists of fuzziness, randomness, and faultiness of knowledge. The faultiness of knowledge is the weak uncertainty, and can be incorporated into fuzziness and randomness. A novel probabilistic analysis method of corrosion-induced resistance degradation subject to fuzziness and randomness is developed in this paper. The reinforcing bar corrosion is induced by chloride ion attack in reinforced concrete (RC) bridge. The relationship between steel area corrosion rate and yield strength is presented based on the experimental investigation on mechanical property of corroded reinforcement. The fuzzy time-variant probabilistic analysis of resistance degradation is illustrated by an example problem of RC bridge beam. The result can be used to time-variant reliability-based evaluation for reinforced concrete.

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Wang, Zhao Hong. "Normal Distribution Data Generating Method Based on Cloud Model." Advanced Materials Research 171-172 (December 2010): 385–88. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.385.

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The similar normal distribution is used wildly in the natural science and social science, fuzzy membership degree function which is accurately established seriously reduces the forecast accuracy of such data. Cloud model compare randomness and fuzziness organically, it reveal the relevance between randomness and fuzziness with digital expectations, entropy and hyper entropy, forecast algorithm based on normal cloud model relaxed the requirements of a normal distribution prerequisite and replaced the accurate membership degree function with the membership degree distribution expectation function, it is more easier and simpler than the joint distribution, Comparative experiment showed it is more general, can complete the data forecast accurately and directly.

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Zhang, Jin Hua. "The Design and Optimization of Fuzzy Controller Based on Vibrating Mill Granularity." Applied Mechanics and Materials 232 (November 2012): 635–38. http://dx.doi.org/10.4028/www.scientific.net/amm.232.635.

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Granularity is the main parameter of evaluating materials, from the analysis of powder producing system that made of vibration mill, the material’s size can be controlled through controlling the speed of motor. Focus on the complex nonlinear in the processing of ground breaking, the two dimensional controller is designed. Due to the subjectivity and randomness in the designing method of classic fuzzy controller, so genetic algorithm is used to put fuzzy controller some learning function in order to obtain better control effect of the system.

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Ren, Weina, Chengdong Li, and Peng Wen. "A novel purification machine and fuzzy inference method based hybrid model for wind speed forecasting." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4059–70. http://dx.doi.org/10.3233/jifs-200205.

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As one kind of readily available renewable energy sources, wind is widely used in power generation where wind speed plays an important role. Generally speaking, we need to forecast the wind speed for improving the controllability of wind power generation. However, there exists considerable randomness and instabilities in wind speed data so that it is difficult to obtain accurate forecasting results. In this paper, we propose a novel fuzzy inference method based hybrid model for accurate wind speed forecasting. In this hybrid model, we adopt two strategies to enhance the estimation performance. On one hand, we propose the purification machine which utilize the Irregular Information Reduction Module (IIRM) and the Irrelevant Variable Reduction Module (IVRM) to reduce the randomness and instabilities of the data and to eliminate the variables with zero or negative effect in the wind speed time series. On the other hand, we adopt the developed Single-Input-Rule-Modules based Fuzzy Inference System (SIRM-FIS), the functionally weighted SIRM-FIS (FWSIRM-FIS) to realize the prediction of wind speed. This FWSIRM-FIS utilizes the multi-variable functional weights to dynamically measure the importance of the input variables so that the input-output mapping can be strengthened and more accurate forecasting results can be achieved. Furthermore, detailed experiments and comparisons are given. Experimental results demonstrate that the proposed FWSIRM-FIS and purification machine contributes greatly to deal with the randomness and instability in the wind speed data and yield more accurate forecasting results than those existing excellent forecasting models.

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Lin, Bin, Zhi Bo Chen, and Qing Fang. "Study on Determination Method for Parameters of Rock’s Shear Strength through Least Absolute Linear Regression Based on Symmetric Triangular Fuzzy Numbers." Applied Mechanics and Materials 170-173 (May 2012): 804–7. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.804.

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In view of the randomness and fuzziness of the parameters of rock’s shear strength, first, use symmetric triangular fuzzy numbers which can reflect the interval features of parameters express the parameters of rock’s shear strength. Second, put forward to a new determination method for parameters of rock’s shear strength through least absolute linear regression based on symmetric triangular fuzzy numbers according to criteria of least absolute. Finally, the analysis of practical engineering computation and comparison to other methods shows that the new method is reasonable.

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Xie, Zi Qing, and Bin Liu. "The Application of Fuzzy Control in the Intelligent Crossroad Traffic Control." Applied Mechanics and Materials 331 (July 2013): 366–69. http://dx.doi.org/10.4028/www.scientific.net/amm.331.366.

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Considering the complexity, uncertainty, randomness of the city crossroad traffic, it is very difficult to structure precise mathematic model, the traditional traffic theory and method applied in the city traffic control cannot deal with it efficiently. However the fuzzy control can handle it intelligently without precise mathematic model, as it can make decision just like people thinking in brain. Based on the practical experience, it has a big application prospect in intelligent city traffic control. According to the development of Fuzzy Control, we summarized the basic composing and design approach of Fuzzy Control. Finally we introduced the design of the intelligent crossroad traffic control. Compared with the simulation of fixed control, we found that the performance and parameter were improved based on the Fuzzy Control.

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Wang, Yi Jun, Cheng Lu, and Dian Wen Li. "Short Term Load Forecasting Based on Fuzzy Clustering." Applied Mechanics and Materials 672-674 (October 2014): 1413–20. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1413.

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This paper presents selected similar days of short-term power load forecasting model based on fuzzy clustering, the method first meteorological factors subdivided into temperature, barometric pressure, wind speed, rain, etc., and then type the week, date, type of day together constitute similar factors, fuzzy coefficient feature mapping table through fuzzy rules, not only to achieve quantitative impact factors and facilitate real-time to add a new rule. On this basis, the use of fuzzy clustering method for classification, based on the level of clustering similar day is selected to reduce the number of samples to accelerate the selected speed. The model takes into account the full impact of weather and other factors on load forecasting, further weakening the load of randomness. Simulation results show that the method has higher prediction accuracy.

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Wang, Ya Jun, and Wo Hua Zhang. "Rock Slope Fuzzy Stochastic Damage Study." Advanced Materials Research 524-527 (May 2012): 337–40. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.337.

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Fuzzy sub-space, with analysis on generalized uncertainty of damage, is setup in this paper when topological consistency of damage fuzzy and randomness on [0,1] scale being demonstrated deeply. Furthermore, deduced under fuzzy characteristics translation are three fuzzy analytical models of damage functional, namely, half depressed distribution, swing distribution, combined swing distribution, by which, fuzzy extension territory on damage evolution is formulated here. With the representation of damage variable β probabilistic distribution as well as formulation on stochastic sub-space of damage variable, expended on the basis of extension criterion and fuzzy probability is damage model defined within generalized uncertain space, by which, introduced is fuzzy probabilistic integral algorithm of generalized uncertain damage variable that could be simulated by the forthcoming fuzzy stochastic damage constitution model based on three fuzzy functional models before. Moreover, in order to realize the joint of fuzzy input and output procedure on generalized uncertain damage variable calculation, fuzzy self-adapting stochastic damage reliability algorithm is, with the update on fuzzy stochastic finite element method within standard normal distribution probabilistic space by the help of foregoing fuzzy stochastic damage constitution model, offered in this paper on the basis of equivalent-normalization and orthogonal design theory.

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Wu, Liang, Xian-bin Mei, and Jian-guo Sun. "A New Default Probability Calculation Formula and Its Application under Uncertain Environments." Discrete Dynamics in Nature and Society 2018 (August 1, 2018): 1–9. http://dx.doi.org/10.1155/2018/3481863.

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In the real world, corporate defaults will be affected by both external market shocks and counterparty risks. With this in mind, we propose a new default intensity model with counterparty risks based on both external shocks and the internal contagion effect. The effects of the external shocks and internal contagion on a company cannot, however, be observed, as uncertainty in the real world contains both randomness and fuzziness. This prevents us from determining the size of the shocks accurately. In this study, fuzzy set theory is utilized to study a looping default credit default swap (CDS) pricing model under uncertain environments. Following this, we develop a new fuzzy form pricing formula for CDS, the simulation analysis of which shows that all kinds of fuzziness in the market have a significant impact on credit spreads, and that the credit spreads, relative to the degree of external shock fuzziness, are much more sensitive. Nevertheless, for a certain degree of fuzziness in the market, credit spreads, relative to changes in counterparty risk, are much more sensitive. Using random analysis and fuzzy numbers, one can think of even more uncertain sources at play than the processes of looping default and investor subjective judgment on the financial markets, and this broadens the scope of possible credit spreads. Compared to the existing related literature, our new fuzzy form CDS pricing model with counterparty risk can consider more factors that influence default and is closer to the reality of the complexity of the dynamics of default. It can also employ the membership function to describe the fuzzy phenomenon, enable the fuzzy phenomenon to be estimated in two kinds of state, and can simultaneously reflect both the fuzziness and randomness in financial markets.

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ZEEPHONGSEKUL, P. "ON A MEASURE OF RELIABILITY BASED ON POINT PROCESSES WITH RANDOM FUZZY MARKS." International Journal of Reliability, Quality and Safety Engineering 13, no. 03 (June 2006): 237–55. http://dx.doi.org/10.1142/s0218539306002239.

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This paper considers the problem faced by a reliability analyst who has detected faults in a system, which were caused by a combination of random and systematic factors. Such faults usually occur at random points in space or time. The analyst's challenge is to accurately gauge the scale of the problem, accepting the fact that an appropriate reliability estimate must also take into consideration the subjective nature of her assessment. In this paper, we introduce a reliability measure, which we called a gauge measure, which incorporates the randomness due to the locations of the faults as well as the imprecision caused by the subjective nature of the assessment. The randomness of the locations will be modeled by a point process and the subjective assessment of each fault by a random fuzzy mark. The mean and variance of a gauge measure will be obtained. Finally, through a generic example and several real world applications, we show how this measure can be applied to measure the reliability of many occurring working systems, especially software systems.

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Li, Chunquan, and Jianhua Jin. "A Scalar Expected Value of Intuitionistic Fuzzy Random Individuals and Its Application to Risk Evaluation in Insurance Companies." Mathematical Problems in Engineering 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/8319859.

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Randomness and uncertainty always coexist in complex systems such as decision-making and risk evaluation systems in the real world. Intuitionistic fuzzy random variables, as a natural extension of fuzzy and random variables, may be a useful tool to characterize some high-uncertainty phenomena. This paper presents a scalar expected value operator of intuitionistic fuzzy random variables and then discusses some properties concerning the measurability of intuitionistic fuzzy random variables. In addition, a risk model based on intuitionistic fuzzy random individual claim amount in insurance companies is established, in which the claim number process is regarded as a Poisson process. The mean chance of the ultimate ruin is investigated in detail. In particular, the expressions of the mean chance of the ultimate ruin are presented in the cases of zero initial surplus and arbitrary initial surplus, respectively, if individual claim amount is an exponentially distributed intuitionistic fuzzy random variable. Finally, two illustrated examples are provided.

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Hu, Jian Jun, Yue Liang Chen, Xiao Ming Tan, Gui Xue Bian, and Li Xu. "Investigation on Random Parameters of Aircraft Structural Reliability Model." Advanced Materials Research 378-379 (October 2011): 51–55. http://dx.doi.org/10.4028/www.scientific.net/amr.378-379.51.

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By studying on reliability model of aircraft structure holistic life assessment, several random parameters are chosen to analyze the randomness of aircraft structure life in this document, the sensitivity of random parameters is investigated to determine the important parameters. The fuzzy random parameters are also studied in this document, the influence of these parameters to aircraft structure reliability is analyzed in the final.

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Kashani, M., M. Arashi, and M. R. Rabiei. "Resampling in Fuzzy Regression via Jackknife-after-Bootstrap (JB)." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 04 (August 2021): 517–35. http://dx.doi.org/10.1142/s0218488521500227.

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In fuzzy regression modeling, when the sample size is small, resampling methods are appropriate and useful for improving model estimation. However, in the commonly used bootstrap method, the standard errors of estimates are also random because of randomness existing in samples. This paper investigates the use of Jackknife-after-Bootstrap (JB) in fuzzy regression modeling to address this problem and produce estimates with smaller mean prediction errors. Performance analysis is carried out through some numerical illustrations and some interactive graphs to illustrate the superiority of the JB method compared to the bootstrap. Moreover, it is demonstrated that using the JB method, we have a significant model, with some sense; however, this is not the case using the bootstrap method.

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Willner, K. "B. Möller, M. Beer: Fuzzy Randomness. Uncertainty in Civil Engineering and Computational Mechanics." Computational Mechanics 36, no. 1 (June 2005): 83. http://dx.doi.org/10.1007/s00466-004-0643-4.

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Möller, Bernd, Michael Beer, Wolfgang Graf, and Jan-Uwe Sickert. "Time-dependent reliability of textile-strengthened RC structures under consideration of fuzzy randomness." Computers & Structures 84, no. 8-9 (March 2006): 585–603. http://dx.doi.org/10.1016/j.compstruc.2005.10.006.

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Zhang, Tao, Zhiwu Han, Xiaojuan Chen, and Wanzhong Chen. "Quantifying randomness and complexity of a signal via maximum fuzzy membership difference entropy." Measurement 174 (April 2021): 109053. http://dx.doi.org/10.1016/j.measurement.2021.109053.

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Jiang, Jing, and Xue Song Zhang. "Fuzzy Comprehensive Evaluation Method for Electric Safety in Plants and Mines." Applied Mechanics and Materials 40-41 (November 2010): 398–403. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.398.

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In this paper, the analytic hierarchy theory and fuzzy mathematics method from ergonomics perspective were used to research reliability of the electrical safety evaluation system from coal mining enterprises. In view of ambiguity and randomness of the evaluation system, the multi-level electrical hazard evaluation system was established and the objective weights were calculated on expert consultation method. Finally, the evaluation procedure and evaluation model from a large coal mining enterprises was given. The experimental results show that the evaluation system has a certain engineering value.

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Journal articles on the topic 'Fuzzy randomness'

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