Journal articles: 'Fuzzy set estimation'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Song, Yafei, and Xiaodan Wang. "Probability Estimation in the Framework of Intuitionistic Fuzzy Evidence Theory." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/412045.

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Intuitionistic fuzzy (IF) evidence theory, as an extension of Dempster-Shafer theory of evidence to the intuitionistic fuzzy environment, is exploited to process imprecise and vague information. Since its inception, much interest has been concentrated on IF evidence theory. Many works on the belief functions in IF information systems have appeared. Although belief functions on the IF sets can deal with uncertainty and vagueness well, it is not convenient for decision making. This paper addresses the issue of probability estimation in the framework of IF evidence theory with the hope of making rational decision. Background knowledge about evidence theory, fuzzy set, and IF set is firstly reviewed, followed by introduction of IF evidence theory. Axiomatic properties of probability distribution are then proposed to assist our interpretation. Finally, probability estimations based on fuzzy and IF belief functions together with their proofs are presented. It is verified that the probability estimation method based on IF belief functions is also potentially applicable to classical evidence theory and fuzzy evidence theory. Moreover, IF belief functions can be combined in a convenient way once they are transformed to interval-valued possibilities.

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Xia, Xintao, Zhongyu Wang, and Yongsheng Gao. "Estimation of non-statistical uncertainty using fuzzy-set theory." Measurement Science and Technology 11, no. 4 (March 10, 2000): 430–35. http://dx.doi.org/10.1088/0957-0233/11/4/314.

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Hussian, M. A., and Essam A. Amin. "Fuzzy reliability estimation based on exponential ranked set samples." International Journal of Contemporary Mathematical Sciences 12 (2017): 31–42. http://dx.doi.org/10.12988/ijcms.2017.612158.

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AbouRizk, Simaan M., and Anil Sawhney. "Subjective and interactive duration estimation." Canadian Journal of Civil Engineering 20, no. 3 (June 1, 1993): 457–70. http://dx.doi.org/10.1139/l93-060.

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Scheduling of construction projects with an uncertainty content requires that the scheduler's subjective knowledge of various factors that might influence the duration of the activities comprising the project is incorporated. Depending on the participating factors and their significance, a different duration outcome is often observed for each activity. To include this uncertainty in the schedule, a statistical distribution is frequently used. This paper presents a system based on the premise that part of the information available, when duration distribution is being estimated, exists in a subjective form. We present an automated system that requires the modeler to specify the activity's minimum and maximum times and a set of linguistic descriptors of the external factors that are suspect of influencing the duration. The lower and upper end point estimates are often available from familiarity with the technology used, physical and logical constraints, or a combination of these situations. The subjective information collected by the modeler is modeled as fuzzy parameters and is quantified using fuzzy set theory. The result of the fuzzy set analysis is a sample of activity durations from the underlying distribution, which is then used to characterize the first two moments of that distribution. Since earlier research has shown that the beta distribution provides an adequate representation for construction durations, the end points specified by the user and the two moments resulting from the fuzzy set analysis are used to fit a beta distribution. The system then allows the user to visually assess the quality of the fit and modify the shape of the beta density using the visual interactive beta estimation system. The paper also presents a practical construction scheduling application to demonstrate the use of the developed system. Key words: construction management, scheduling, fuzzy sets, subjective duration estimation, beta distribution, linguistic variables.

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Zhao, Feng, Hao Hao, and Hanqiang Liu. "Robust intuitionistic fuzzy clustering with bias field estimation for noisy image segmentation." Intelligent Data Analysis 26, no. 5 (September 5, 2022): 1403–26. http://dx.doi.org/10.3233/ida-216058.

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The concept of intuitionistic fuzzy set has been found to be highly useful to handle vagueness in data. Based on intuitionistic fuzzy set theory, intuitionistic fuzzy clustering algorithms are proposed and play an important role in image segmentation. However, due to the influence of initialization and the presence of noise in the image, intuitionistic fuzzy clustering algorithm cannot acquire the satisfying performance when applied to segment images corrupted by noise. In order to solve above problems, a robust intuitionistic fuzzy clustering with bias field estimation (RIFCB) is proposed for noisy image segmentation in this paper. Firstly, a noise robust intuitionistic fuzzy set is constructed to represent the image by using the neighboring information of pixels. Then, initial cluster centers in RIFCB are adaptively determined by utilizing the frequency statistics of gray level in the image. In addition, in order to offset the information loss of the image when constructing the intuitionistic fuzzy set of the image, a new objective function incorporating a bias field is designed in RIFCB. Based on the new initialization strategy, the intuitionistic fuzzy set representation, and the incorporation of bias field, the proposed method preserves the image details and is insensitive to noise. Experimental results on some Berkeley images show that the proposed method achieves satisfactory segmentation results on images corrupted by different kinds of noise in contrast to conventional fuzzy clustering algorithms.

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Carvalho, Mariana, Eusébio Nunes, and José Telhada. "Fuzzy maintenance costs of a wind turbine pitch control device." International Journal of Production Management and Engineering 3, no. 2 (July 10, 2015): 103. http://dx.doi.org/10.4995/ijpme.2015.3318.

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This paper deals with the problem of estimation maintenance costs for the case of the pitch controls system of wind farms turbines. Previous investigations have estimated these costs as (traditional) “crisp” values, simply ignoring the uncertainty nature of data and information available. This paper purposes an extended version of the estimation model by making use of the Fuzzy Set Theory. The results alert decision-makers to consequent uncertainty of the estimations along with their overall level, thus improving the information given to the mainte-nance support system.

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Chaturvedi, Ankita, Sanjay Kumar Singh, and Umesh Singh. "Statistical Inferences of Type-II Progressively Hybrid Censored Fuzzy Data with Rayleigh Distribution." Austrian Journal of Statistics 47, no. 3 (May 27, 2018): 40–62. http://dx.doi.org/10.17713/ajs.v47i3.752.

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This article presents the procedures for the estimation of the parameter of Rayleighdistribution based on Type-II progressive hybrid censored fuzzy lifetime data. Classicalas well as the Bayesian procedures for the estimation of unknown model parameters has been developed. The estimators obtained here are Maximum likelihood (ML) estimator, Method of moments (MM) estimator, Computational approach (CA) estimator and Bayes estimator. Highest posterior density (HPD) credible intervals of the unknown parameter are obtained by using Markov Chain Monte Carlo (MCMC) technique. For numerical illustration, a real data set has been considered.

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Thongwan. "An Estimation of Rainfall using Fuzzy Set-Genetic Algorithms Model." American Journal of Engineering and Applied Sciences 4, no. 1 (January 1, 2011): 77–81. http://dx.doi.org/10.3844/ajeassp.2011.77.81.

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Wang, Shun Yuan, Chwan Lu Tseng, Shou Chuang Lin, Jen Hsiang Chou, and Chih Chen Chen. "Takagi-Sugeno Fuzzy Estimator Design for Adaptive Vector Control Systems." Applied Mechanics and Materials 284-287 (January 2013): 2337–40. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2337.

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This paper presents an adaptive pseudo reduced-order Takagi-Sugeno (T-S) fuzzy flux estimator for the induction motor direct field orientation control system. The estimator gain can be obtained by solving a set of linear matrix inequalities (LMIs) to estimate the rotor flux accurately. It is well known that, because of changes in temperature, variations of stator and rotor resistances affect the accuracy of rotor flux estimation. To resolve this problem, a cerebellar model articulation proportional integral controller (CMAPIC) is proposed to estimate the stator and rotor resistances during temperature variations. These estimated quantities, including stator and rotor resistances, are taken as the T-S fuzzy flux estimator inputs, so that the flux estimation is uninfluenced by these parameter variations. Thus the estimators enhance the robustness of the system. Moreover, this work uses a cerebellar model articulation controller to estimate the rotor speed, which is fed back to the adaptive supervisory fuzzy cerebellar model articulation speed controller (ASFCMAC) to achieve the speed sensor-less control.

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Mohamed, Rania A. H., Ahlam H. Tolba, Ehab M. Almetwally, and Dina A. Ramadan. "Inference of Reliability Analysis for Type II Half Logistic Weibull Distribution with Application of Bladder Cancer." Axioms 11, no. 8 (August 6, 2022): 386. http://dx.doi.org/10.3390/axioms11080386.

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The estimation of the unknown parameters of Type II Half Logistic Weibull (TIIHLW) distribution was analyzed in this paper. The maximum likelihood and Bayes methods are used as estimation methods. These estimators are used to estimate the fuzzy reliability function and to choose the best estimator of the fuzzy reliability function by comparing the mean square error (MSE). The simulation’s results showed that fuzziness is better than reality for all sample sizes, and fuzzy reliability at Bayes predicted estimates is better than the maximum likelihood technique. It produces the lowest average MSE until a sample size of n = 50 is obtained. A simulated data set is applied to diagnose the performance of the two techniques applied here. A real data set is used as a practice for the model discussed and developed the maximum likelihood estimate alternative model of TIIHLW as Topp Leone inverted Kumaraswamy, modified Kies inverted Topp–Leone, Kumaraswamy Weibull–Weibull, Marshall–Olkin alpha power inverse Weibull, and odd Weibull inverted Topp–Leone. We conclude that the TIIHLW is the best distribution fit for this data.

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Meharie, Meseret Getnet, Zachary C. Abiero Gariy, Raphael Ngumbau Ndisya Mutuku, and Wubshet Jekale Mengesha. "An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects." Advances in Civil Engineering 2019 (June 25, 2019): 1–14. http://dx.doi.org/10.1155/2019/4092549.

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Accurate cost estimates are vital to the effective realisation of construction projects. Extended knowledge, wide-ranging information, substantial expertise, and continuous improvement are required to attain accurate cost estimation. Cost estimation at the preliminary phase of the project is always a challenge as only limited information is available. Hence, rational selection of input variables for preliminary cost estimation could be imperative. A systematic input variable selection approach for preliminary estimating using an integrated methodology of factor analysis and fuzzy AHP is presented in this paper. First, the factor analysis is used to classify and reduce the input variables and their variable coefficients are determined. Second, fuzzy AHP based on the geometric mean method is employed to determine the weights of input variables in a fuzzy environment where the subjectivity and vagueness are handled with natural language expressions parameterized by triangular fuzzy numbers. Then, the input variables are suggested to be selected starting with those having high coefficient and high importance weight. A set of three variables, one from each group, can be added to the estimating model at a time so that the problem of collinearity can vanish and good accuracy of the estimate can be ensured. The proposed approach enables cost estimators to better understand the complete input variable selection process at the early stage of project development and provide a more accurate, rational, and systematic decision support tool.

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TSUNAKI, Ryosuke, Hiroyuki YOSHIMATSU, and Jiro OURA. "Estimation of Slope Failures due to Earthquake by Fuzzy Set Theory." Landslides 27, no. 3 (1990): 19–25. http://dx.doi.org/10.3313/jls1964.27.3_19.

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Woodcock, Curtis E., and Sucharita Gopal. "Fuzzy set theory and thematic maps: accuracy assessment and area estimation." International Journal of Geographical Information Science 14, no. 2 (March 13, 2000): 153–72. http://dx.doi.org/10.1080/136588100240895.

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Fajardo, Jesús A. "A criterion for the fuzzy set estimation of the density function." Brazilian Journal of Probability and Statistics 28, no. 3 (August 2014): 301–12. http://dx.doi.org/10.1214/12-bjps208.

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Meriem, Bouhadjar, Ahmed M. Gemeay, Ehab M. Almetwally, Zeghdoudi Halim, Etaf Alshawarbeh, Alanazi Talal Abdulrahman, M. M. Abd El-Raouf, and Eslam Hussam. "The Power XLindley Distribution: Statistical Inference, Fuzzy Reliability, and COVID-19 Application." Journal of Function Spaces 2022 (June 7, 2022): 1–21. http://dx.doi.org/10.1155/2022/9094078.

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The power XLindley (PXL) distribution is introduced in this study. It is a two-parameter distribution that extends the XLindley distribution established in this paper. Numerous statistical characteristics of the suggested model were determined analytically. The proposed model’s fuzzy dependability was statistically assessed. Numerous estimation techniques have been devised for the purpose of estimating the proposed model parameters. The behaviour of these factors was examined using randomly generated data and developed estimation approaches. The suggested model seems to be superior to its base model and other well-known and related models when applied to the COVID-19 data set.

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Sari, F. A. K., and Y. Latief. "Safety Cost Estimation of Building Construction with Fuzzy Logic and Artificial Neural Network." Journal of Physics: Conference Series 1803, no. 1 (February 1, 2021): 012020. http://dx.doi.org/10.1088/1742-6596/1803/1/012020.

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Abstract The number of cases of construction work accidents continues to increase every year. To prevent work accidents, especially the fatality rate, Occupational Health and Safety (OHS) must be implemented in the construction project. Implementation of OHS can be a success if the availability of the budget is allocated explicitly for implementing OHS in construction projects. However, respondents’ current OHS budget is currently insufficient when referring to the guidelines regarding OHS costs applied in Indonesia. This condition will increase the initial budget and cause financial losses. So it is necessary to develop a cost estimation model that can estimate costs quickly and accurately. The results from this study will help estimator make OHS cost estimates quickly and accurately so that an estimator does not need a long time to do cost estimations at the beginning of the project, and the results of the estimated cost estimates are accurate. The fuzzy method uses to classify the contract value. The output of the fuzzy process will use as input in learning with the neural network. The Mean Absolute Percentage Error of tested data set for the adapted model is highly accurate (9.906%). The model obtained has a better MAPE value than the estimated cost by using regression analysis.

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Barrios, José Ángel, Gerardo Maximiliano Méndez, and Alberto Cavazos. "Hybrid-Learning Type-2 Takagi–Sugeno–Kang Fuzzy Systems for Temperature Estimation in Hot-Rolling." Metals 10, no. 6 (June 6, 2020): 758. http://dx.doi.org/10.3390/met10060758.

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Entry temperature estimation is a major concern for finishing mill set-up in hot strip mills. Variations in the incoming bar conditions, frequent product changes and measurement uncertainties may cause erroneous estimation, and hence, an incorrect mill set-up causing a faulty bar head-end. In earlier works, several varieties of neuro-fuzzy systems have been tested due to their adaptation capabilities. In order to test the combination of the simplicity offered by Takagi–Sugeno–Kang systems (also known as Sugeno systems) and the modeling power of type-2 fuzzy, in this work, hybrid-learning type-2 Sugeno fuzzy systems are evaluated and compared with the results presented earlier. Systems with both empirically and fuzzy c-means-generated rules as well as purely fuzzy systems and grey-box models are tested. Experimental data were collected from a real-life mill; datasets for rule-generation, training, and validation were randomly drawn. Two of the grey-box models presented here reach 100% of bars with 20 °C or less prediction error, while two of the purely fuzzy systems improved performance with respect to purely fuzzy systems presented elsewhere, however it was only a slight improvement.

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Ray, Kumar S. "Pattern Recognition Based on Fuzzy Set and Genetic Algorithm." International Journal of Image and Graphics 14, no. 03 (July 2014): 1450009. http://dx.doi.org/10.1142/s0219467814500090.

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In this paper, we consider a soft computing approach to pattern classification. Our basic tools for soft computing are fuzzy relational calculus (FRC) and genetic algorithm (GA). We introduce a new interpretation of multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. We also consider the notion of a fuzzy pattern vector to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space. The construction of the pattern classifier is essentially based on the estimate of a fuzzy relation Ri between the antecedent clause and consequent clause of each one-dimensional fuzzy implication. For the estimation of Ri we use floating point representation of GA. Thus a set of fuzzy relations is formed from the new interpretation of MFI. This set of fuzzy relations is termed as the core of the pattern classifier. Once the classifier is constructed the non-fuzzy features of a test pattern can be classified. The performance of the proposed scheme is tested on synthetic data. Subsequently, we use the proposed scheme for the vowel classification problem of an Indian language. Finally, a benchmark of performance is established by considering multiplayer perception (MLP), support vector machine (SVM) and the present method. The Abalone, Hosse Colic and Pima Indians data sets, obtained from UCL database repository are used for the said benchmark study. This new tool for pattern classification is very effective for classification of patterns under vegue and imprecise environment. It can provide multiple classification under overlapped classes.

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CHAKRABORTY, CHANDAN, and DEBJANI CHAKRABORTY. "FUZZY LINEAR AND POLYNOMIAL REGRESSION MODELLING OF ‘IF-THEN’ FUZZY RULEBASE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 02 (April 2008): 219–32. http://dx.doi.org/10.1142/s0218488508005145.

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In developing so called fuzzy expert systems, fuzzy rule bases have been considered with greater importance. In fact, a fuzzy rule base is a knowledgebase that models human cognitive factors. Fuzzy rules are linguistic ‘IF-THEN’ constructions where ‘IF’ part consists of a set of fuzzy variables and ‘THEN’ part includes a dependent fuzzy variable. In order to identify the underlying mathematical structure in the fuzzy rule base, we develop fuzzy linear and fuzzy polynomial regression techniques in this paper. And the estimation of model parameters is also shown using least-square approach. Finally, examples are illustrated to demonstrate the proposed model.

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Parichha, P., K. Basu, A. Bandyopadhyay, and P. Mukhopadhyay. "Development of Efficient Estimation Technique for Population Mean in Two Phase Sampling Using Fuzzy Tools." Journal of Applied Mathematics, Statistics and Informatics 13, no. 2 (December 20, 2017): 5–28. http://dx.doi.org/10.1515/jamsi-2017-0006.

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Abstract The present investigation deals with the problem of estimation of population mean in two-phase (double) sampling. Utilizing information on two auxiliary variables, one chain exponential ratio and regression type estimator has been proposed and its properties are studied under two different structures of twophase sampling. To make the estimator practicable, unbiased version of the proposed strategy has also been developed. The dominance of the suggested estimator over some contemporary estimators of population mean has been established through numerical illustrations carried over the data set of some natural population and artificially generated population. Categorization of the dominance ranges of the proposed estimation strategies are deployed through defuzzification tools, which are followed by suitable recommendations.

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Meharie, Meseret Getnet, Zachary C. Abiero Gariy, Raphael Ngumbau Ndisya Mutuku, and Wubshet Jekale Mengesha. "Prioritizing Key Duration Estimation Accuracy Factors in Highway Infrastructure Projects Using Fuzzy AHP." Open Civil Engineering Journal 13, no. 1 (August 31, 2019): 92–108. http://dx.doi.org/10.2174/1874149501913010092.

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Background: At the early phase of project development, highway engineering estimators seek to determine the duration of highway construction projects for the purpose of construction planning and administration. Thus, it is vital to study and analyze the estimation accuracy factors of highway construction project duration. In this regard, several studies have been conducted to identify and analyze the estimation accuracy factors of project duration in various ways to improve the estimation and management performance of all the contracting parties. However, very less effort has been devoted to evaluating the duration estimation accuracy factors in the case of the highway construction industry under fuzzy environment. Objective: This paper aims to analyze and prioritize the critical factors that potentially affect the duration estimation accuracy of the highway construction projects in Ethiopia under fuzzy environment. Methods: An extensive review and discussions with highway engineering experts were carried out to explore and identify the duration estimation accuracy factors. The study data collection process consists of two stages. The first stage is to conduct a questionnaire survey. Whereas, the second stage is to carry out the pair-wise comparison matrix to capture the imprecision and vagueness in subjective responses. Then, a λ-cut set method to reduce the initial list of factors and exploratory factor analysis was used to classify the reduced set of factors into smaller groups. Finally, a fuzzy hierarchy process algorithm with the use of triangular fuzzy numbers was presented for prioritizing critical factors. Results: A cut -off value, λ = 0.95, was verified which resulted in the identification of critical accuracy factors. Accordingly, 12 critical factors were opted and categorized as a cluster of similar items into 5 groups. Finally, the analytical results obtained from fuzzy AHP algorithm revealed that project complexity, project size, bridge type and complexity were found to be the four top-ranked factors based on the global priority weight. Conclusion: These factors must be a serious concern in estimating and administering the contract and the duration of highway construction projects at the early phases of project development so that the time deviation upon the completion of the project can be minimized.

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LIN, LONG-SHUH. "DEMANDS ESTIMATION OF NEW TELECOMMUNICATION SERVICES IN FUZZY ENVIRONMENT." International Journal of Information Technology & Decision Making 02, no. 02 (June 2003): 333–47. http://dx.doi.org/10.1142/s0219622003000653.

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With the uncertain influential factors of demands and the lack of required historical data, demand estimation for new telecommunication services have generally relied just on marketing survey and analysis. However, the data collected from marketing survey are usually expressed in human linguistic forms and hence are fuzzy in nature. That means the estimation method derived from traditional sampling theory cannot fully represent such fuzzy data and thus biased consequences caused often. Therefore, in this study, to completely capture the uncertainty of the surveyed data, we adopt a series of analytical methods based on fuzzy set theory to construct a fuzzy estimation model. Based on the proposed model, a solution procedure is developed to aid users to acquire the demands of new telecommunication services. Finally, the solution procedure is employed to estimate demands of mobile phone service within one year in Taiwan with satisfactory results.

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Murugesan, Senthil Kumar, and Chidhambara Rajan Balasubramanian. "An Accurate FFPA-PSR Estimator Algorithm and Tool for Software Effort Estimation." Scientific World Journal 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/919825.

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Software companies are now keen to provide secure software with respect to accuracy and reliability of their products especially related to the software effort estimation. Therefore, there is a need to develop a hybrid tool which provides all the necessary features. This paper attempts to propose a hybrid estimator algorithm and model which incorporates quality metrics, reliability factor, and the security factor with a fuzzy-based function point analysis. Initially, this method utilizes a fuzzy-based estimate to control the uncertainty in the software size with the help of a triangular fuzzy set at the early development stage. Secondly, the function point analysis is extended by the security and reliability factors in the calculation. Finally, the performance metrics are added with the effort estimation for accuracy. The experimentation is done with different project data sets on the hybrid tool, and the results are compared with the existing models. It shows that the proposed method not only improves the accuracy but also increases the reliability, as well as the security, of the product.

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Aguilar Cruz, Karen Alicia, José de Jesús Medel Juárez, José Luis Fernández Muñoz, and Midory Esmeralda Vigueras Velázquez. "Neural Net Gains Estimation Based on an Equivalent Model." Computational Intelligence and Neuroscience 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/1690924.

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A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrixAand the proper gainKinto the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB) the factors based on the functional error and the reference signal built with the past information of the system.

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Kobayashi, Takahiro, and Tetsuji Tani. "Application of Cooperative Control to Petroleum Plants Using Fuzzy Supervisory Control and Model Predictive Multi-variable Control." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 6 (November 20, 2001): 333–37. http://dx.doi.org/10.20965/jaciii.2001.p0333.

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This paper describes hierarchical control with fuzzy supervisory control and model predictive multivariable control (MPC) in a petroleum plant. MPC is effective in time delay, interference, and handling constraints. Fuzzy logic controllers are effective for plants with large time delay and non-linearity. Our proposed hierarchical control combines their advantages. Fuzzy supervisory control, which determines set points for MPC, consists of an estimation block and a compensation block. We use a statistical model with multi-regression analysis for the estimation block to estimate parameters of plant operation, and fuzzy logic for the compensation block to correct output of the statistical model. Hierarchical control has been applied to an actual plant in an oil refinery, and showed satisfactory performance.

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AKIYAMA, Takamasa, Tsuna SASAKI, and Yoji ARIKURA. "Estimation Model of Diversion Rate on Urban Expressway with Fuzzy Set Theory." INFRASTRUCTURE PLANNING REVIEW 7 (1989): 259–66. http://dx.doi.org/10.2208/journalip.7.259.

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Reddy, Ch Satyananda, and KVSVN Raju. "Improving the Accuracy of Effort Estimation through Fuzzy Set Representation of Size." Journal of Computer Science 5, no. 6 (June 1, 2009): 451–55. http://dx.doi.org/10.3844/jcssp.2009.451.455.

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Han-Ching Kuo and Yuan-Yih Hsu. "Distribution system load estimation and service restoration using a fuzzy set approach." IEEE Transactions on Power Delivery 8, no. 4 (1993): 1950–57. http://dx.doi.org/10.1109/61.248307.

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Deyi, Feng, and M. Ichikawa. "Quantitative estimation of time-variable earthquake hazard by using fuzzy set theory." Tectonophysics 169, no. 1-3 (November 1989): 175–96. http://dx.doi.org/10.1016/0040-1951(89)90192-3.

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Feng, Zhiguang, Wei Xing Zheng, and Ligang Wu. "Reachable Set Estimation of T–S Fuzzy Systems With Time-Varying Delay." IEEE Transactions on Fuzzy Systems 25, no. 4 (August 2017): 878–91. http://dx.doi.org/10.1109/tfuzz.2016.2586945.

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PAPATHEOCHAROUS, EFI, and ANDREAS S. ANDREOU. "A HYBRID SOFTWARE COST ESTIMATION APPROACH UTILIZING DECISION TREES AND FUZZY LOGIC." International Journal of Software Engineering and Knowledge Engineering 22, no. 03 (May 2012): 435–65. http://dx.doi.org/10.1142/s0218194012500106.

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Software cost estimation (SCE) is one of the critical activities in software project management. During the past decades various models have been proposed for SCE. However, developing accurate and useful models is limited in practice despite the considerable financial gain they could offer to software stakeholders. Traditional techniques, such as regression, by-analogy and machine learning, face the difficulty of handling the dynamic nature of the software process and the problematic nature of the public data available. This paper addresses the issue of SCE proposing an alternative approach that combines robust decision tree structures with fuzzy logic. Fuzzy decision trees are generated using the CHAID and CART algorithms in a systematic manner, while development effort is treated as the dependent variable against two subsets of factors: The first contains selected attributes from the ISBSG, COCOMO and DESHARNAIS datasets and the second contains a subset of the available factors that can be measured early in the development cycle. The association rules obtained from the trees are then merged and defuzzified through a Fuzzy Implication System (FIS). The fuzzy framework is utilized to perform effort estimations. Experimental results indicate that the proposed approach is promising as it yields quite accurate estimations in most dataset cases considered. Finally, our evaluation suggests that accurate estimations may be produced, even when using only a small set of factors that can be measured early in the development cycle, thus increasing the practical value of the proposed cost model.

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Mahdaoui, Rafik, Leila Hayet Mouss, Amar Haboussi, Ouahiba Chouhal, Hichem Haouassi, and Toufik Messoud Maarouk. "A Temporal Neuro-Fuzzy System for Estimating Remaining Useful Life in Preheater Cement Cyclones." International Journal of Reliability, Quality and Safety Engineering 26, no. 03 (May 7, 2019): 1950012. http://dx.doi.org/10.1142/s0218539319500128.

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Fault prognosis in industrial plants is a complex problem, and time is an important factor for the resolution of this problem. The main indicator for the task of fault prognosis is the estimate of remaining useful life (RUL), which essentially depends on the predicted time to failure. This paper introduces a temporal neuro-fuzzy system (TNFS) for performing the fault prognosis task and exactly estimating the RUL of preheater cyclones in a cement plant. The main component of the TNFS is a set of temporal fuzzy rules that have been chosen for their ability to explain the behavior of the entire system, the components’ degradation, and the RUL estimation. The benefit of introducing time in the structure of fuzzy rules is that a local memory of the TNFS is created to capture the dynamics of the prognostic task. More precisely, the paper emphasizes improving the performance of TNFSs for prediction. The RUL estimation process is broken down into four generic processes: building a predictive model, selecting the most critical parameters, training the TNFS, and predicting RUL through the generated temporal fuzzy rules. Finally, the performance of the proposed TNFS is evaluated using a real preheater cement cyclone dataset. The results show that our TNFS produces better results than classical neuro-fuzzy systems and neural networks.

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Kasie, Fentahun Moges, and Glen Bright. "Integrating fuzzy case-based reasoning, parametric and feature-based cost estimation methods for machining process." Journal of Modelling in Management 16, no. 3 (January 18, 2021): 825–47. http://dx.doi.org/10.1108/jm2-05-2020-0123.

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Purpose This paper aims to propose an intelligent system that serves as a cost estimator when new part orders are received from customers. Design/methodology/approach The methodologies applied in this study were case-based reasoning (CBR), analytic hierarchy process, rule-based reasoning and fuzzy set theory for case retrieval. The retrieved cases were revised using parametric and feature-based cost estimation techniques. Cases were represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space. Findings The proposed cost estimator retrieves historical cases that have the most similar cost estimates to the current new orders. Further, it revises the retrieved cost estimates based on attribute differences between new and retrieved cases using parametric and feature-based cost estimation techniques. Research limitations/implications The proposed system was illustrated using a numerical example by considering different lathe machine operations in a computer-based laboratory environment; however, its applicability was not validated in industrial situations. Originality/value Different intelligent methods were proposed in the past; however, the combination of fuzzy CBR, parametric and feature-oriented methods was not addressed in product cost estimation problems.

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Kim, Donggil, and Dongik Lee. "Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter." Applied Sciences 9, no. 16 (August 13, 2019): 3329. http://dx.doi.org/10.3390/app9163329.

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Early detection and diagnosis of wind turbine faults is critical for applying a possible maintenance and control strategy to avoid catastrophic incidents. This paper presents a novel method to estimate the parameter of faults in a wind turbine. In this work, the estimation of fault parameters is reformulated as the state estimation problem by augmenting the parameters as an additional state. The novelty of the proposed method lies in the use of an adaptive fuzzy fading algorithm for the adaptive Kalman filter so that the convergence property during the estimation of fault parameter can be improved. The performance of the proposed method is evaluated through a set of numerical simulations with both linear and non-linear models.

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Relich, Marcin. "An evaluation of project completion with application of fuzzy set theory." Management 16, no. 1 (May 1, 2012): 216–29. http://dx.doi.org/10.2478/v10286-012-0016-6.

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An evaluation of project completion with application of fuzzy set theoryThe project management contains such elements as management of time, cost, communications, procurement, quality, risk or scope of project. Each of these fields can be considered as a set of constraints, and then there is a possibility to verify their fulfillment in sense of an enterprise's constraints and its environment. These constraints determine a completion of project activities and its success or failure, finally. The paper aims to present a problem of project management in terms of fuzzy constraints satisfaction problem, and then the using of constraint programming techniques to the evaluation of project completion. A fuzzy constraints satisfaction problem enables a description of data in distinct, as well as imprecise form, in a unified framework. It seems especially important in case of unique activities of project, when their estimation is based on linguistic information from experts.

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Cheung-Mak, S. K. P., and I. Le May. "DAMAGE AND FUZZY RISK ASSESSMENT IN STEAM PLANTS." Transactions of the Canadian Society for Mechanical Engineering 17, no. 2 (June 1993): 111–25. http://dx.doi.org/10.1139/tcsme-1993-0007.

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The various methods of damage assessment in steam plants are reviewed. An example of estimating the remaining life of steam pipe welds containing cracks is discussed. Modelling techniques provide a quantitative evaluation of the risk of failure. However, damage assessment is not an exact science. Thus the fuzzy set method of risk assessment is introduced. Most material data for creep are obtained from accelerated tests, and the extrapolation of shorter time data to longer times at different stresses and temperatures is used in the prediction of remaining life. Inexact knowledge of load and temperature variations in the operation of actual steam plant further complicates overall risk assessment. Estimation of the overall risk of failure in the plant using the fuzzy set method requires identification of critical areas in different components at which damage may have accumulated, together with nondestructive testing, microstructural evaluation and other methods to define the extent of the damage. The importance of the particular component to the operation of the whole system must be assessed before a judgement can be made to provide a prediction of the plant’s remaining life.

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Pappenberger, F., K. Frodsham, K. Beven, R. Romanowicz, and P. Matgen. "Fuzzy set approach to calibrating distributed flood inundation models using remote sensing observations." Hydrology and Earth System Sciences Discussions 3, no. 4 (August 15, 2006): 2243–77. http://dx.doi.org/10.5194/hessd-3-2243-2006.

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Abstract. The paper presents a methodology for the estimation of uncertainty of inundation extent, which takes account of the uncertainty in the observed spatially distributed information and implements a fuzzy evaluation methodology. The Generalised Likelihood Uncertainty Estimation (GLUE) technique and the 2-D LISFLOOD-FP model were applied to derive the set of uncertain inundation realisations and resulting flood inundation maps. Conditioning of the inundation maps on fuzzified Synthetic Aperture Radar (SAR) images results in much more realistic inundation risk maps which can better depict the variable pattern of inundation extent than previously used methods. It has been shown that the methodology compares well to traditional approaches and can produce flood hazard maps that reflect the uncertainties in model evaluation.

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Pappenberger, F., K. Frodsham, K. Beven, R. Romanowicz, and P. Matgen. "Fuzzy set approach to calibrating distributed flood inundation models using remote sensing observations." Hydrology and Earth System Sciences 11, no. 2 (January 17, 2007): 739–52. http://dx.doi.org/10.5194/hess-11-739-2007.

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Abstract. The paper presents a methodology for the estimation of uncertainty of inundation extent, which takes account of the uncertainty in the observed spatially distributed information and implements a fuzzy evaluation methodology. The Generalised Likelihood Uncertainty Estimation (GLUE) technique and the 2-D LISFLOOD-FP model were applied to derive the set of uncertain inundation realisations and resulting flood inundation maps. Conditioning of the inundation maps on fuzzified Synthetic Aperture Radar (SAR) images results in much more realistic inundation risk maps which can better depict the variable pattern of inundation extent than previously used methods. It has been shown that the evaluation methodology compares well to traditional approaches and can produce flood hazard maps that reflect the uncertainties in model evaluation.

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Dieulot, J. Y., A. El Kamel, and P. Borne. "Study of the stability of fuzzy controllers by an estimation of the attraction regions: A Vector Norm approach." Mathematical Problems in Engineering 8, no. 3 (2002): 221–31. http://dx.doi.org/10.1080/10241230215288.

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A fuzzy controller with singleton defuzzification can be considered as the association of a regionwise constant term and of a regionwise non linear term, the latter being bounded by a linear controller. Based on the regionwise structure of fuzzy controller, the state space is partitioned into a series of disjoint sets. The fuzzy controller parameters are tuned in order to ensure that theith set is included into the domain of attraction of the preceding sets of the series. If the first set of the series is included into the region of attraction of the equilibrium point, the overall fuzzy controlled system is stable. The attractors are estimated with the help of the comparison principle, using Vector Norms, which ensures the robustness with respect to uncertainties and perturbations of the open loop system.

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Velimirovic, Andrija, Goran Djordjevic, Maja Velimirovic, and Milica Jovanovic. "A fuzzy set-based approach to range-free localization in wireless sensor networks." Facta universitatis - series: Electronics and Energetics 23, no. 2 (2010): 227–44. http://dx.doi.org/10.2298/fuee1002227v.

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Localization in Wireless Sensor Networks (WSNs) refers to the ability of determining the positions of sensor nodes, with an acceptable accuracy, based on known positions of several anchor nodes. Among the plethora of possible localization schemes, the Received Signal Strength (RSS) based range-free localization techniques have attracted significant research interest for their simplicity and low cost. However, these approaches suffer from significant estimation errors due to low accuracy of RSS measurements influenced by irregular radio propagation. In order to tackle the problem of RSS uncertainty, in this work we propose a fuzzy set-based localization method as an enhancement of the ring-overlapping scheme [1]. In the proposed method, first we use a fuzzy membership function based on RSS measurements to generate fuzzy sets of rings that constrain sensor node position with respect to each anchor. Then we generate fuzzy set of regions by intersecting rings from different ring sets. Finally, we employ weighted centroid method on the fuzzy set of regions to localize the node. The results obtained from simulations demonstrate that our solution improve localization accuracy in the presence of radio irregularity, but even for the case without radio irregularity.

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Journal articles on the topic 'Fuzzy set estimation'

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