Relevant bibliographies by topics / Fuzzy standard deviation

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

Academic literature on the topic 'Fuzzy standard deviation'

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

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Journal articles on the topic "Fuzzy standard deviation"

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Fokrul, Alom Mazarbhuiya. "FINDING STANDARD DEVIATION OF A FUZZY NUMBER." International Journal of Research – Granthaalayah 4, no. 1 (2017): 63–69. https://doi.org/10.5281/zenodo.848171.

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Two probability laws can be root of a possibility law. Considering two probability densities over two disjoint ranges, we can define the fuzzy standard deviation of a fuzzy variable with the help of the standard deviation two random variables in two disjoint spaces.
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Mazarbhuiya, FokrulAlom. "FINDING STANDARD DEVIATIONOFA FUZZY NUMBER." International Journal of Research -GRANTHAALAYAH 4, no. 1 (2016): 63–69. http://dx.doi.org/10.29121/granthaalayah.v4.i1.2016.2844.

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Two probability laws can be root of a possibility law. Considering two probability densities over two disjoint ranges, we can define the fuzzy standard deviation of a fuzzy variable with the help of the standard deviation two random variables in two disjoint spaces.
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Sikkannan, Krishna Prabha, and Vimala Shanmugavel. "Sorting Out Fuzzy Transportation Problems via ECCT and Standard Deviation." International Journal of Operations Research and Information Systems 12, no. 2 (2021): 1–14. http://dx.doi.org/10.4018/ijoris.20210401.oa1.

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A well-organized arithmetical procedure entitled standard deviation is employed to find the optimum solution in this paper. This technique has been divided into two parts. The first methodology deals with constructing the entire contingency cost table, and the second deals with optimum allocation. In this work, the method of magnitude is used for converting fuzzy numbers into crisp numbers as this method is better than the existing methods. This technique gives a better optimal solution than other methods. A numerical example for the new method is explained, and the authors compared their meth
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Inna Auliya, Fadhilah Fitri, Nonong Amalita, and Tessy Octavia Mukhti. "Comparison of K-Means and Fuzzy C-Means Algorithms for Clustering Based on Happiness Index Components Across Provinces in Indonesia." UNP Journal of Statistics and Data Science 2, no. 1 (2024): 114–21. http://dx.doi.org/10.24036/ujsds/vol2-iss1/150.

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Cluster analysis is a multivariate technique aimed at grouping objects into several clusters based on the characteristics they possess. This study aims to determine the clustering results of 34 provinces in Indonesia based on the indicators of the happiness index for the year 2021 by comparing non-hierarchical cluster analysis methods, namely K-Means and Fuzzy C-Means. K-Means is a non-hierarchical cluster analysis that divides objects into cluster groups based on the distance of objects to the nearest cluster center, while Fuzzy C-Means is a cluster analysis that uses a fuzzy grouping model w
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Efendi, Riswan, Adhe N. Imandari, Yusnita Rahmadhani, et al. "Fuzzy Autoregressive Time Series Model Based on Symmetry Triangular Fuzzy Numbers." New Mathematics and Natural Computation 17, no. 02 (2021): 387–401. http://dx.doi.org/10.1142/s1793005721500204.

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The symmetry triangular fuzzy number has been developed to build fuzzy autoregressive models by using various approaches such as low-high data, integer number, measurement error, and standard deviation data. However, most of these approaches are not simulated and compared between ordinary least square and fuzzy optimization in parameter estimation. In this paper, we are interested in implementation of measurement error and standard deviation data in construction symmetry triangular fuzzy numbers. Additionally, both types of triangular fuzzy numbers are deployed to build a fuzzy autoregressive
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Azizi, Wali Mohammad, Athiqullah Hayat, Shamsullah Shams, and Mohammad Izat Emir Zulkifly. "An Estimation of Underground Economy in Afghanistan Using Mathematical Fuzzy Model Based on Mean and Standard Deviation." Journal for Research in Applied Sciences and Biotechnology 2, no. 4 (2023): 176–81. http://dx.doi.org/10.55544/jrasb.2.4.25.

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The underground economy (UE) briefly comprises services, activities, and transactions, which could be legal or illegal. In this paper the size of UE is estimated through mathematical fuzzy model based on fuzzy set, fuzzy logic and constructed a yearly time-series for UE over the period 2001 to 2020 in Afghanistan. Two input variables are used; unemployment rate (UR) and the government regulations (REG). Fuzzification, fuzzy inference and defuzzification; the three steps that are considered for estimating UE in the country, based on mean and standard deviation (SD) for each variable individuall
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Lin, Y. K. "On the standard deviation of change-in-impedance due to fuzzy subsystems." Journal of the Acoustical Society of America 101, no. 1 (1997): 616–18. http://dx.doi.org/10.1121/1.418127.

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Azami, Hamed, Alberto Fernández, and Javier Escudero. "Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis." Medical & Biological Engineering & Computing 55, no. 11 (2017): 2037–52. http://dx.doi.org/10.1007/s11517-017-1647-5.

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Nurjanah, Desi, Indira Anggriani, and Primadina Hasanah. "APPLICATION OF K-MEANS AND FUZZY C-MEANS ALGORITHMS TO DETERMINE FLOOD VULNERABILITY CLUSTERS (CASE STUDY: KUTAI KARTANEGARA REGENCY)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 2 (2024): 0821–36. http://dx.doi.org/10.30598/barekengvol18iss2pp0821-0836.

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Flooding show situation where areas that are not usually inundated, such as farmland and settlements, and city district areas, become inundated due to water. Floods can to occur when the flow of water on rivers or waste channels overrun its normal measurements. This study describes the K-Means and Fuzzy C-Means Algorithm methods for clustered flood-prone areas built on Districts in Kutai Kartanegara Regency. This research begins with data collection in the character of rainfall, land elevation, the number of victims affected, the quantity of damaged houses, the quantity of damage to facilities
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Juan, Carlos Figueroa Garcia, and Kreinovich Vladik. "How Accurate Are Fuzzy Control Recommendations: Interval-Valued Case." Advances in Artificial Intelligence and Machine Learning 1, no. 1 (2021): 12–25. https://doi.org/10.54364/AAIML.2021.1102.

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As a result of applying fuzzy rules, we get a fuzzy set describing possible control values. In automatic control systems, we need to defuzzify this fuzzy set, i.e., to transform it to a single control value. One of the most frequently used defuzzification techniques is centroid defuzzification. From the practical viewpoint, an important question is: how accurate is the resulting control recommendation? The more accurately we need to implement the control, the more expensive the resulting controller. The possibility to gauge the accuracy of the fuzzy control recommendation follows from the fact
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Dissertations / Theses on the topic "Fuzzy standard deviation"

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Vaško, Jan. "Využití prostředků umělé inteligence na kapitálových trzích." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2011. http://www.nusl.cz/ntk/nusl-222910.

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Diploma thesis deals with analyzing the possibility of using artificial intelligence, specifically artificial neural networks and fuzzy logic, on the capital markets as a tool to support decision making in business. The Matlab software is used for this purpose. The work is divided into three parts. The first part deals with theoretical knowledge, brief description of the current situationin is covered in a second part and the theoretical solutions are applied to the system in the third section.
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Adeyefa, Segun Adeyemi. "Satisticing solutions for multiobjective stochastic linear programming problems." Thesis, 2011. http://hdl.handle.net/10500/5703.

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Multiobjective Stochastic Linear Programming is a relevant topic. As a matter of fact, many real life problems ranging from portfolio selection to water resource management may be cast into this framework. There are severe limitations in objectivity in this field due to the simultaneous presence of randomness and conflicting goals. In such a turbulent environment, the mainstay of rational choice does not hold and it is virtually impossible to provide a truly scientific foundation for an optimal decision. In this thesis, we resort to the bounded rationality and chance-constrained princip
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Adeyefa, Segun Adeyemi. "Satisficing solutions for multiobjective stochastic linear programming problems." Thesis, 2011. http://hdl.handle.net/10500/5703.

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Multiobjective Stochastic Linear Programming is a relevant topic. As a matter of fact, many real life problems ranging from portfolio selection to water resource management may be cast into this framework. There are severe limitations in objectivity in this field due to the simultaneous presence of randomness and conflicting goals. In such a turbulent environment, the mainstay of rational choice does not hold and it is virtually impossible to provide a truly scientific foundation for an optimal decision. In this thesis, we resort to the bounded rationality and chance-constrained princip
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Fan, Kang-Yun, and 范綱允. "Multiattribute Decision Making Based on Probability Density Functions and the Variances and Standard Deviations of Largest Ranges of Evaluating Interval-Valued Intuitionistic Fuzzy Values." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/fdk9sz.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>107<br>In this thesis, we propose a new multiattribute decision making method based on probability density functions and the variances and standard deviations of the largest ranges of evaluating interval-valued intuitionistic fuzzy values. First, the proposed method obtains the largest range of each evaluating interval-valued intuitionistic fuzzy value in the decision matrix provided by the decision maker. Then, it computes the average value of the largest ranges of each attribute. Then, it obtains the probability density function of the largest range of each evaluat
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Book chapters on the topic "Fuzzy standard deviation"

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Xu, Yejun. "Standard Deviation Method for Risk Evaluation in Failure Mode under Interval-Valued Intuitionistic Fuzzy Environment." In Computational Risk Management. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18387-4_61.

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Peng, Hua, and Yixin He. "Analysis of Subway Braking Performance Based on Fuzzy Comprehensive Evaluation Method." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_81.

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AbstractIn the process of subway operation, the braking system is a complex system, and its state detection is for high data accuracy and state positioning accuracy According to the structure of the braking system and the principle of the braking method, the basic braking performance parameters of the system are analyzed, combined with the abnormal state of the subway brake cylinder pressure data, the braking process is divided into two stages: brake cylinder pressure establishment and peak stability. And define the six characteristic parameters of 90% brake cylinder pressure establishment tim
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Lah, Muhammad Shukri Che, Nureize Arbaiy, and Riswan Efendi. "Stock Market Forecasting Model Based on AR(1) with Adjusted Triangular Fuzzy Number Using Standard Deviation Approach for ASEAN Countries." In Intelligent and Interactive Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6031-2_22.

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Jayapriya, V., and S. Akila Devi. "FUZZY MATRIX ANALYSIS - SOLAR ENERGY." In Futuristic Trends in Contemporary Mathematics & Applications Volume 3 Book 4. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bbcm4p2ch7.

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This paper gives a brief survey on the solar energy Production in various states. The method of application of Combined Effective Time Dependent Data (CETD) Matrix, Average Time Dependent Data (ATD) Matrix, and Refined Time Dependent Data (RTD) Matrix which are fuzzy models are studied using fuzzy matrices. The effects and objectives of data’s using the concept of mean and Standard deviation.
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Khan, Laiq, Rabiah Badar, Saima Ali, and Umar Farid. "Comparison of Uncertainties in Membership Function of Adaptive Lyapunov NeuroFuzzy-2 for Damping Power Oscillations." In Fuzzy Systems. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1908-9.ch004.

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The direct focus of this chapter is to explore the potential of online Adaptive NeuroFuzzy Type-2 (ANFT2) control system for damping inter-area oscillations using Static Synchronous Compensator (STATCOM). The nonlinear ANFT2-based direct control scheme is proposed to damp inter-area oscillations by utilizing its model free and universal approximation capabilities. The Gaussian and triangular membership functions with different variations of uncertain mean and standard deviation are considered for ANFT2. The adaptation mechanism utilizes gradient descent-based back-propagation algorithm using L
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Sen, Zekâi. "New Trends in Fuzzy Clustering." In Data Mining in Dynamic Social Networks and Fuzzy Systems. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4213-3.ch012.

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Fuzzy methodologies show progress day by day towards better explanation of various natural, social, engineering and information problem solutions in the best, economic, fast and effective manner. This chapter provides cluster analyses from probabilistic, statistical and especially fuzzy methodology points of view by consideration of various classical and innovative cluster modeling and inference systems. After the conceptual assessment explanation of fuzzy logic thinking fundamentals various clustering methodologies are presented with brief revisions but innovative trend analyses as k-mean-sta
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Dutta, Palash. "Human Health Risk Assessment via Amalgamation of Probability and Fuzzy Numbers." In Emerging Trends and Applications in Cognitive Computing. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5793-7.ch005.

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This chapter presents an approach to combine probability distributions with imprecise (fuzzy numbers) parameters (mean and standard deviation) as well as fuzzy numbers (FNs) of various types and shapes within the same framework. The amalgamation of probability distribution and fuzzy numbers are done by generating three algorithms. Human health risk assessment is performed through the proposed algorithms. It is found that the chapter provides an exertion to perform human health risk assessment in a specific manner that has more efficacies because of its capacity to exemplify uncertainties of ri
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Lin, Tsung-Chih, Yi-Ming Chang, and Tun-Yuan Lee. "System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network." In Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-1870-1.ch013.

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This paper proposes a novel fuzzy modeling approach for identification of dynamic systems. A fuzzy model, recurrent interval type-2 fuzzy neural network (RIT2FNN), is constructed by using a recurrent neural network which recurrent weights, mean and standard deviation of the membership functions are updated. The complete back propagation (BP) algorithm tuning equations used to tune the antecedent and consequent parameters for the interval type-2 fuzzy neural networks (IT2FNNs) are developed to handle the training data corrupted by noise or rule uncertainties for nonlinear system identification
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Guo Qi, Xue Chengqi, Zhou Lei, and Wang Haiyan. "A Study on Comprehensive Evaluation of Deep-Sea HOV Cockpit Console Based on Fuzzy Gravity Center." In Advances in Transdisciplinary Engineering. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-779-5-547.

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The cockpit console is an important part in the deep-sea human occupied vehicle (HOV). The comprehensive evaluation of HOV cockpit console is a multi-target and multi-hierarchical decision-making process. In this paper, a multi-level fuzzy comprehensive evaluation model based on gravity center is proposed which can improve the accuracy of the evaluation. Firstly, the ergonomics evaluation index system of HOV cockpit console is established according to the characteristics of HOV console. Secondly, the combination weight of index is calculated by expert subjective weighting method and standard d
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Zhu, Qingsong, Ruiting Tan, and Ping Wang. "The Relation of Career Adaptability to Values Realization Degree and Organizational Citizenship Behavior." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200749.

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The sudden outbreak of the pandemic COVID-19 inevitably has a great impact on economic and social development. Therefore, the innovation-driven value becomes more and more prominent. Through literature review, it is not difficult to find that values have gradually become an important reference standard for organizations to select talents for their teams, as well as an important reference factor for studying organizational citizenship behavior. In order to explore the relationship between values realization degree and organizational citizenship behavior, this investigation based on the social i
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Conference papers on the topic "Fuzzy standard deviation"

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Zhu, Liansen, and Ruoning Xu. "Ranking fuzzy numbers based on fuzzy mean and standard deviation." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019703.

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Yongning Guo, Shuliang Sun, and Chenglian Liu. "Edge detection based on fuzzy gradient and standard deviation values." In 2011 2nd International Conference on Control, Instrumentation, and Automation (ICCIA). IEEE, 2011. http://dx.doi.org/10.1109/icciautom.2011.6184015.

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barkhoda, Wafa, Fardin Akhlaqian Tab, and Om-Kolsoom Shahryari. "Fuzzy edge detection based on pixel's gradient and standard deviation values." In 2009 International Multiconference on Computer Science and Information Technology (IMCSIT). IEEE, 2009. http://dx.doi.org/10.1109/imcsit.2009.5352742.

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Chen, Shi-Jay, and Hsiao-Wei Kao. "Measure of similarity between interval-valued fuzzy numbers based on standard deviation operator." In 2010 International Conference on Electronics and Information Engineering (ICEIE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iceie.2010.5559801.

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Hari, Ch V. M. K., Prasad P. V. G. D. Reddy, M. Jagadeesh, and G. SriRam Ganesh. "Interval Type-2 Fuzzy Logic for Software Cost Estimation Using TSFC with Mean and Standard Deviation." In 2010 International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom 2010). IEEE, 2010. http://dx.doi.org/10.1109/artcom.2010.40.

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Hamza, Karim, and Kazuhiro Saitou. "An Efficient Algorithm for Vehicle Crashworthiness Design Via Crash Mode Matching." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99485.

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This paper presents an efficient algorithm for developing vehicle structures for crashworthiness, based on the analyses of crash mode, a history of the deformation of the different structural zones during a crash event. It emulates a process called crash mode matching where structural crashworthiness is improved by manually modifying the design until its crash mode matches the one the designers deem as optimal. Given an initial design and a desired crash mode, the algorithm iteratively finds a new design whose crash mode is increasingly closer to the desired one. At each iteration, a new desig
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Rezaee, Babak. "An analytical formula for similarity measure between interval type-2 fuzzy sets with Gaussian Primary membership function and uncertain standard deviation." In 2010 Third International Workshop on Advanced Computational Intelligence (IWACI). IEEE, 2010. http://dx.doi.org/10.1109/iwaci.2010.5585157.

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Bomfim, Carlos H. M., Walmir Matos Caminhas, Benjamim Rodrigues de Menezes, and Carlos Alexandre Laurentys de Almeida. "Building a Leakage Detection System Using Ensembles: A New Way." In 2004 International Pipeline Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/ipc2004-0187.

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Pipeline leakage is a demand from governmental and environmental associations that petroleum companies need to comply. Recent accidents with Petrobras pipelines increase local demand for leakage detection system. Due the high accuracy on detecting leakage required from that system is necessary to set a procedure that once applied will achieve the best performance considering the quality of the installed instrumentation. This paper describes a procedure to set such system in order to accomplish with the legal requirement keeping high reliability during normal and failure operations. Nuisance al
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Stefanakos, Christos N., and Erik Vanem. "Climatic Forecasting of Wind and Waves Using Fuzzy Inference Systems." In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/omae2017-61968.

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Wind and wave climatic simulations are of great interest in a number of different applications, including the design and operation of ships and offshore structures, marine energy generation, aquaculture and coastal installations. In a climate change perspective, projections of such simulations to a future climate are of great importance for risk management and adaptation purposes. This work investigates the applicability of FIS/ANFIS models for climatic simulations of wind and wave data. The models are coupled with a nonstationary time series modelling, which decomposes the initial time series
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Alakbari, Fahd Saeed, Mysara Eissa Mohyaldinn, Mohammed Abdalla Ayoub, Ali Samer Muhsan, and Ibnelwaleed Ali Hussein. "Development of Oil Formation Volume Factor Model using Adaptive Neuro-Fuzzy Inference Systems ANFIS." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205817-ms.

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Abstract The oil formation volume factor is one of the main reservoir fluid properties that plays a crucial role in designing successful field development planning and oil and gas production optimization. The oil formation volume factor can be acquired from pressure-volume-temperature (PVT) laboratory experiments; nonetheless, these experiments' results are time-consuming and costly. Therefore, many studies used alternative methods, namely empirical correlations (using regression techniques) and machine learning to determine the formation volume factor. Unfortunately, the previous correlations
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