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Journal articles on the topic 'Fuzzy standard deviation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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3

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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10

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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Dodi, Yudo Setyawan, Yuliawati Dona, Warsito, and Warsono. "Calibration of Geomagnetic and Soil Temperatur Sensor for Earthquake Early Warning System." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 5 (2018): 2239–44. https://doi.org/10.12928/TELKOMNIKA.v16i5.7592.

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The study of Design of Earthquake Early Warning System for Real Time Using Geomagnetism and Total Electron Content with Fuzzy Logic through competitive grants scheme has obtained the prototype of the earthquake early warning system. However, it still needs improvements on in the calibration of thesensor system especially for MAG3110 sensor and DHT11 sensor. This calibration was done by adjusting the sensor system to the existing measuring devices standards in the Physics Department laboratory of the Sains Faculty Lampung University, to obtain measurement accuracyand to get a good result about

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Li, Wei, Xiaoyu Ma, Yumin Chen, et al. "Random Fuzzy Granular Decision Tree." Mathematical Problems in Engineering 2021 (June 9, 2021): 1–17. http://dx.doi.org/10.1155/2021/5578682.

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In this study, the classification problem is solved from the view of granular computing. That is, the classification problem is equivalently transformed into the fuzzy granular space to solve. Most classification algorithms are only adopted to handle numerical data; random fuzzy granular decision tree (RFGDT) can handle not only numerical data but also nonnumerical data like information granules. Measures can be taken in four ways as follows. First, an adaptive global random clustering (AGRC) algorithm is proposed, which can adaptively find the optimal cluster centers and maximize the ratio of

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Demir, N., M. Kaynarca, and S. Oy. "EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 747–51. http://dx.doi.org/10.5194/isprs-archives-xli-b7-747-2016.

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Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Th

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Demir, N., M. Kaynarca, and S. Oy. "EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 747–51. http://dx.doi.org/10.5194/isprsarchives-xli-b7-747-2016.

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Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Th

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Bimbim Oktaviandi, Tessy Octavia Mukhti, Yenni Kurniawati, and Zamahsary Martha. "Implementation of the Fuzzy C-Means Clustering Method in Grouping Provinces in Indonesia based on the Types of Goods Sold in E-commerce Businesses in 2022." UNP Journal of Statistics and Data Science 2, no. 3 (2024): 360–65. http://dx.doi.org/10.24036/ujsds/vol2-iss3/210.

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The internet facilitates e-commerce by enabling efficient transactions and building consumer trust. With internet users in Indonesia reaching 204 million in 2022, it is crucial to Cluster provinces based on the types of goods and services sold online to design effective marketing strategies. The Fuzzy C-Means (FCM) method is used for Cluster analysis, allowing objects to have different membership degrees in multiple Clusters and providing accurate Cluster center placement. This study applies Fuzzy C-Means to Cluster 34 provinces in Indonesia based on the sale of goods/services in e-commerce in

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Harikrishnan, Manikandan, Jeyabharathi Sundarrajan, and Muthuraj Rengasamy. "An Introduction to Fuzzy Testing of Multialternative Hypotheses for Group of Samples with the Single Parameter: Through the Fuzzy Confidence Interval of Region of Acceptance." Journal of Applied Mathematics 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/365304.

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Classical statistics and many data mining methods rely on “statistical significance” as a sole criterion for evaluating alternative hypotheses. It is very useful to find out the significant difference existing between the samples as well as the population or between two samples. But in this paper, the researchers try to apply the concepts of fuzzy group testing of hypothesis problem between multi group of samples of same size or different, through comparing the parameters like mean, standard deviation, and so forth. Hence we can compare multigroups such that they have the significant differenc

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Zhang, Ying, Li Deng, and Bo Wei. "Imbalanced Data Classification Based on Improved Random-SMOTE and Feature Standard Deviation." Mathematics 12, no. 11 (2024): 1709. http://dx.doi.org/10.3390/math12111709.

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Oversampling techniques are widely used to rebalance imbalanced datasets. However, most of the oversampling methods may introduce noise and fuzzy boundaries for dataset classification, leading to the overfitting phenomenon. To solve this problem, we propose a new method (FSDR-SMOTE) based on Random-SMOTE and Feature Standard Deviation for rebalancing imbalanced datasets. The method first removes noisy samples based on the Tukey criterion and then calculates the feature standard deviation reflecting the degree of data discretization to detect the sample location, and classifies the samples into

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18

Nguyen, Tuan Hung, and Huynh Xuan Le. "Fuzzy finite element analysis based on the transformation between fuzzy and random variables." Ministry of Science and Technology, Vietnam 64, no. 4 (2022): 45–50. http://dx.doi.org/10.31276/vjste.64(4).45-50.

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In this paper, a fuzzy finite element method (FFEM) for determining the responses of structures is proposed by using the transformation between fuzzy and random variables. Firstly, the formulae for establishing normal random variables equivalent to symmetric triangular fuzzy numbers are presented based on the combination of the principle of insufficient reason and that of maximum specificity. As a result, fuzzy finite element analysis is transferred into stochastic finite element analysis. To solve this problem, the response surface method with the aid of standard normal random variables is ut

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Nguyen Nhu, Phong, and Tu Anh Nguyen Nhu. "Applying fuzzy theory to develop linguistic control charts The pLCC model." BOHR International Journal of Operations Management Research and Practices 3, no. 1 (2024): 8–14. http://dx.doi.org/10.54646/bijomrp.2024.23.

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This paper studies an approach to use fuzzy set theory and possibility theory to construct control charts – a very important on-line process control tool used in quality control. The control chart is constructed based on linguistic data. The model aims to control the process simply and effectively. The quality characteristics are modeled by fuzzy variable. The status of quality characteristics is modeled by triangle fuzzy number. Fuzzy arithmetic is used to calculate the control chart’s center line. The control chart’s control limits are constructed according to Shewhart’s principle. The fuzzy

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Ngang, B. N., C. C. Nwagu, O. E. Ojuka, and E. Ntiedo. "Optimizing Distributed Generation Size and Location to Minimize Voltage Deviation Using Hybrid ANN and Fuzzy Logic." International Journal of Engineering and Environmental Sciences 7, no. 3 (2024): 12–27. https://doi.org/10.5281/zenodo.13744158.

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<em>Voltage stability in power distribution systems is crucial for preventing system inefficiencies and equipment damage, particularly when integrating Distributed Generation (DG). This study proposes an advanced method for minimizing voltage deviation in distribution feeders by optimizing the size and location of DG units using a hybrid Artificial Neural Network (ANN) and Fuzzy Logic approach. The ANN is employed to predict optimal DG placement and sizing, while Fuzzy Logic addresses the uncertainties within the distribution network. The proposed method is validated on a standard IEEE 33-bus

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Mahmudi, Mahmudi, Rito Goejantoro, and Fidia Deny Tisna Amijaya. "Perbandingan Metode C-Means dan Fuzzy C-Means Pada Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator IPM Tahun 2019." EKSPONENSIAL 12, no. 2 (2021): 193. http://dx.doi.org/10.30872/eksponensial.v12i2.814.

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The Human Development Index is an indicator used to measure one important aspect related to the quality of the results of economic development, namely the degree of human development. Data Mining is a technique or process for obtained information from large database warehouses. Based on its function, one of the data mining tasks was to group data, where the method used in this study was the C-Means and Fuzzy C-Means grouping methods. The two classification methods were applied to the human development index indicator data. The purpose of this study was to determined the best method based on th

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Rani, Pratibha, Shyi-Ming Chen, and Arunodaya Raj Mishra. "Multiple attribute decision making based on MAIRCA, standard deviation-based method, and Pythagorean fuzzy sets." Information Sciences 644 (October 2023): 119274. http://dx.doi.org/10.1016/j.ins.2023.119274.

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23

Rehman, Inayatur, Tariq Shah, and Iqtadar Hussain. "Analyses of S-Box in Image Encryption Applications Based on Fuzzy Decision Making Criterion." Zeitschrift für Naturforschung A 69, no. 5-6 (2014): 207–14. http://dx.doi.org/10.5560/zna.2014-0023.

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In this manuscript, we put forward a standard based on fuzzy decision making criterion to examine the current substitution boxes and study their strengths and weaknesses in order to decide their appropriateness in image encryption applications. The proposed standard utilizes the results of correlation analysis, entropy analysis, contrast analysis, homogeneity analysis, energy analysis, and mean of absolute deviation analysis. These analyses are applied to well-known substitution boxes. The outcome of these analyses are additional observed and a fuzzy soft set decision making criterion is used

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Mojtaba Zabihinpour, S., MKA Ariffin, SH Tang, and AS Azfanizam. "Construction of fuzzy ¯ X - S control charts with an unbiased estimation of standard deviation for a triangular fuzzy random variable." Journal of Intelligent & Fuzzy Systems 28, no. 6 (2015): 2735–47. http://dx.doi.org/10.3233/ifs-151551.

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Szaksz, Bence, and Gabor Stepan. "Transient chaotic behavior of fuzzy controlled polishing processes." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 9 (2022): 093112. http://dx.doi.org/10.1063/5.0101257.

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This paper investigates the dynamics of a fuzzy controlled polishing machine where the effect of temporal sampling is also taken into account. Chaotic and transient chaotic behaviors are experienced for certain control parameter combinations. In the case of transient chaotic motion, closed-form algebraic expressions are determined for the expected value of the kickout number and for the corresponding standard deviation.

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Glensk, Barbara, and Reinhard Madlener. "Fuzzy Portfolio Optimization of Power Generation Assets." Energies 11, no. 11 (2018): 3043. http://dx.doi.org/10.3390/en11113043.

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Fuzzy theory is proposed as an alternative to the probabilistic approach for assessing portfolios of power plants, in order to capture the complex reality of decision-making processes. This paper presents different fuzzy portfolio selection models, where the rate of returns as well as the investor’s aspiration levels of portfolio return and risk are regarded as fuzzy variables. Furthermore, portfolio risk is defined as a downside risk, which is why a semi-mean-absolute deviation portfolio selection model is introduced. Finally, as an illustration, the models presented are applied to a selectio

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Zhao, Ligang, Hua Zheng, Hongyue Zhen, Li Xie, Yuan Xu, and Xianchao Huang. "Improvement of Fuzzy Newton Power Flow Convergence." Energies 16, no. 24 (2023): 8044. http://dx.doi.org/10.3390/en16248044.

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In order to address the convergence issue in fuzzy power flow calculations, this paper proposes an analytical approach based on the Levenberg–Marquardt method, aiming to improve the convergence of the fuzzy Newton power flow method. Firstly, a detailed analysis is conducted on the convergence theorem and convergence behavior of the fuzzy Newton method, revealing its poor convergence when the initial values are not properly selected. The Levenberg–Marquardt method is then selected as a means to enhance the convergence of the fuzzy Newton power flow calculations, specifically to tackle the probl

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Li, Jia Yang, Kai Ning Liu, and Ying Qiu Gu. "New Fuzzy Multi-Level Comprehensive Methodology for Safety Risk of Subway Operation." Applied Mechanics and Materials 373-375 (August 2013): 2195–99. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.2195.

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Subway has already become an important tool of public transportation. Subway security operation is to achieve a serious guarantee of safety and convenience. In this article, subway safety operation evaluation index system is constructed. Based on hierarchy process, triangular fuzzy number was used to describe the evaluation value obtained by experts. In order to be more objective and more precise, the correlation coefficient and standard deviation integrated approach will be used to calculate the weights of criteria in the fuzzy hierarchy process.

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BURGIN, MARK, and OKTAY DUMAN. "STATISTICAL FUZZY CONVERGENCE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 06 (2008): 879–902. http://dx.doi.org/10.1142/s0218488508005674.

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The goal of this work is the further development of neoclassical analysis, which extends the scope and results of the classical mathematical analysis by applying fuzzy logic to conventional mathematical objects, such as functions, sequences, and series. This allows us to reflect and model vagueness and uncertainty of our knowledge, which results from imprecision of measurement and inaccuracy of computation. Basing on the theory of fuzzy limits, we develop the structure of statistical fuzzy convergence and study its properties. Relations between statistical fuzzy convergence and fuzzy convergen

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Zhang, Hong Tao, Yu Xia Hu, and Heng Yuan Zhang. "Extraction and Classifier Design for Image Recognition of Insect Pests on Field Crops." Advanced Materials Research 756-759 (September 2013): 4063–67. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4063.

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Feature extraction and the classifier design were the crucial parts for image recognition of insect pests on agriculture field crops. The hardware of the detection device for insect pests included the trapping, stunning and buffering unit, the even illumination unit, the scattering and transporting unit, and the image vision unit. The seven morphological features from binary images of the insect pests were extracted and normalized, such as area, perimeter, and complexity. The standard vector model library and the membership functions were established based on the feature mean and the feature s

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Tang, Y. H., and Z. Z. Han. "CME Properties and Fuzzy Classification." Symposium - International Astronomical Union 203 (2001): 422–24. http://dx.doi.org/10.1017/s0074180900219700.

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In this paper, the theory and method of fuzzy classification are applied to analyse CME properties, with the use of the data in preliminary report during 1979-1981, the theoretical computation for CME category is performed. According to the original data of average properties for 9 structural classes of CME, the standardized value is obtained by average value and standard deviation. The fuzzy similitude matrix and equivalent matrix are built by use of the correlation coefficient transformation and the method of similitude coefficient, then we can divide 9 different structures into different ca

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Nagpal, Chetna, and P. K. Uppadhyay. "Sleep EEG Classification Using Fuzzy Logic." International Journal of Advanced Research in Engineering 1, no. 1 (2015): 17. http://dx.doi.org/10.24178/ijare.2015.1.1.17.

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the computerized detection of multi stage system of EEG signals using fuzzy logic has been developed and tested on prerecorded data of the EEG of rats.The multistage detection system consists of three major stages: Awake, SWS (Slow wave sleep), REM (Rapid eye movement) which has been recorded and can be detected by the fuzzy classification and fuzzy rule base. The proposed work approaches to identify thestage of 3- channel signal on the basis of frequency distribution of EEG, standard deviation of EOG and EMG, variance of EOG and EMG. Based on feature extracted data, fuzzy logic rule base mode

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P., Ananthi, and Parthipan V. "An Innovative Method for Detection of Malicious Behaviours in Automated Vehicle System Using Hybrid Fuzzy C-Means Algorithm with Neural Network Algorithm Based Accuracy and Cost." ECS Transactions 107, no. 1 (2022): 11765–79. http://dx.doi.org/10.1149/10701.11765ecst.

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Aim: To improve the predictive accuracy and cost analysis for malicious behaviors in automated vehicle systems using the Hybrid Fuzzy C-Means algorithm (HFCM) and Neural Network algorithm (NN). Materials and Methods: Accuracy is performed with two groups Fuzzy C-Means Algorithm and the Neural Network algorithm of sample size per group (N = 125). G power 80% threshold 0.05%, CI 95%. Mean and Standard deviation. Result: Independent sample T-Test was carried out using Fuzzy C-Means and Neural Network. C-means (92.1%) perform better than NN (89.6%). There is a statistically significant difference

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Narayanamoorthy, Samayan, Veerappan Annapoorani, Samayan Kalaiselvan, and Daekook Kang. "Hybrid Hesitant Fuzzy Multi-Criteria Decision Making Method: A Symmetric Analysis of the Selection of the Best Water Distribution System." Symmetry 12, no. 12 (2020): 2096. http://dx.doi.org/10.3390/sym12122096.

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Every country’s influence and livelihood is centered on that country’s water source. Therefore, many studies are being conducted worldwide to improve and sustain water resources. In this research paper, we have selected and researched the water scheme for groundwater recharge and drinking water supply of drought prone areas. The water project is aimed at connecting the drought prone areas of the three districts of Tamil Nadu to filling up the ponds in their respective villages and raising the ground water level and meeting the drinking water requirement. We have chosen a multi-criteria decisio

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.., Ankita, and P. K. Mishra. "Fuzzy Logic Based Load Balanced Clustering for Network Lifetime Enhancement in WSN." International Journal of Wireless and Ad Hoc Communication 7, no. 1 (2023): 08–17. http://dx.doi.org/10.54216/ijwac.070101.

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Large number of small sensor nodes exists in WSN’s for sensing and collecting information from the environment. In today’s time, these sensor nodes were applied in under water, military area, health care, earthquake sensing and in dedicated areas with recent technologies. Sensor nodes have limited life time and have supplementary network life. Network lifecycle depends on many factors such as connectivity, residual energy, topology types, single hop, multi hop, distance from base station, distance to cluster heads and much more. Among the various solutions given, clustering is considered to be

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Sangeetha, J., and P. Renuga. "Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices." Journal of Circuits, Systems and Computers 26, no. 02 (2016): 1750034. http://dx.doi.org/10.1142/s0218126617500347.

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This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated control

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Jabbar, Reyam Raheem, and Ahmed Abdulrasool Ahmed Alkhafaji. "Analysis of Traditional and Fuzzy Quality Control Charts to Improve Short-Run Production in the Manufacturing Industry." Journal of Engineering 29, no. 6 (2023): 159–76. http://dx.doi.org/10.31026/j.eng.2023.06.12.

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Quality control charts are limited to controlling one characteristic of a production process, and it needs a large amount of data to determine control limits to control the process. Another limitation of the traditional control chart is that it doesn’t deal with the vague data environment. The fuzzy control charts work with the uncertainty that exists in the data. Also, the fuzzy control charts investigate the random variations found between the samples. In modern industries, productivity is often of different designs and a small volume that depends on the market need for demand (short-run pro

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Reyam, Raheem Jabbar, and Abdulrasool Ahmed Alkhafaji Ahmed. "COMPUTER AIDED FUZZY CONTROL CHARTS FOR EVALUATING AND ANALYZING VARIABLE DATA." Engineering and Technology Journal 9, no. 01 (2024): 3282–93. https://doi.org/10.5281/zenodo.10469702.

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One of the limitations of fuzzy control charts is the complexity of their mathematical relations, and there is no software built to draw and analyze the fuzzy control charts. This research presents a visual presentation of fuzzy control charts for variables ( , , I-MR), using an integrated program built by MATLAB20 to draw and analyze fuzzy control charts, A real case study was conducted of data collected from the Al-Numan factory for a plastic connecter product. The number of attempts to reach the approved control limits was less in the fuzzy charts, as well as the number of deleted samples i

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Chen, Kuen-Suan, Tsun-Hung Huang, Ruey-Chyn Tsaur, and Wen-Yang Kao. "Fuzzy Evaluation Models for Accuracy and Precision Indices." Mathematics 10, no. 21 (2022): 3961. http://dx.doi.org/10.3390/math10213961.

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The random variable X is used to represent the normal process containing two important parameters—the process average and the process standard deviation. The variable is transformed using Y = (X − T)/d, where T is the target value and d is the tolerance. The average of Y is then called the accuracy index, and the standard deviation is called the precision index. If only the values of the accuracy index and the process precision index are well controlled, the process quality level as well as the process yield are ensured. Based on this concept, this paper constructed a control chart for the acc

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FRIEDMAN, MENAHEM, MA MING, and ABRAHAM KANDEL. "ON THE THEORY OF TYPICALITY." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 03, no. 02 (1995): 127–42. http://dx.doi.org/10.1142/s0218488595000116.

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The classical definitions of ‘expected value’ and ‘standard deviation’ may sometimes lead to quantities which fail to represent a ‘typical’ feature of a given data set, whenever this set consists of more than one cluster. The use of the fuzzy expected value (FEV) and the clustering fuzzy expected value (CFEV) also yield central tendency and in general cannot represent a typical value of the given data. In this work a new quantity—a Most Typical Value (MTV) is defined and investigated. A given fuzzy set in Rn is first clustered and replaced by a finite set of clusters. This set is then represen

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Latifah, Ummu Wachidatul, Sugiyarto Surono, and Suparman Suparman. "K-means and fuzzy c-means algorithm comparison on regency/city grouping in Central Java Province." Desimal: Jurnal Matematika 5, no. 2 (2022): 155–68. http://dx.doi.org/10.24042/djm.v5i2.12204.

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The Human Development Index (HDI) is very important in measuring the country's success as an effort to build the quality of life of people in a region, including Indonesia. The government needs to make groupings based on the needs of a city/district. To facilitate data grouping based on the similarity of existing characteristics, it is necessary to have a data grouping method, namely the clustering technique. There are several algorithms that are often used in clustering techniques, namely K-Means and Fuzzy C-Means. Each algorithm has advantages and disadvantages. Therefore, in this research,

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Muhammad, Shukri Che Lah, and Arbaiy Nureize. "A simulation study of first-order autoregressive to evaluate the performance of measurement error based symmetry triangular fuzzy number." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 3 (2020): 1559–67. https://doi.org/10.11591/ijeecs.v18.i3.pp1559-1567.

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Data collected by various data collection methods are often exposed to uncertainties that may affect the information presented by quantitative results. This also causes the forecasted model developed to be less precise because of the uncertainty contained in the input data used. Hence, preparing the data by means of handling inherent uncertainties is necessary to avoid the developed forecasting model to be less accurate. Traditional autoregressive (AR) model uses precise values and deals with the uncertainty normally in forecasting model. Fewer researches are focused on data preparation in tim

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Bei, Shao Yi, Jing Bo Zhao, Lan Chun Zhang, and Shao Hua Liu. "Fuzzy Control and Co-Simulation of Automobile Semi-Active Suspension System Based on SIMPACK and MATLAB." Applied Mechanics and Materials 39 (November 2010): 50–54. http://dx.doi.org/10.4028/www.scientific.net/amm.39.50.

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Using the multi-body simulation software SIMPACK as platform, a whole CHANGHE mini-car model was built. A fuzzy controller was adopted based on MATLAB/SIMULINK software to control the full car model. Pulse input running test simulation was carried out under co-simulation of SIMAT. The results showed that compared to passive suspension, with the speed 40km/h, the body vertical acceleration, body pitch angular velocity, standard deviation and peak were respectively decreased by 10.76%, 18.03% and 20.48%, 12.13%. The semi-active suspension system with fuzzy controller had better performance than

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Yuan, Chuan Yi, Ye Fang Teng, and Xin Ye Yin. "Study of Active Suspension Based on Fuzzy Neural Network Control." Applied Mechanics and Materials 251 (December 2012): 201–5. http://dx.doi.org/10.4028/www.scientific.net/amm.251.201.

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Based on the established full-car active suspension model, fuzzy control theory was combined with neural network control, the fuzzy neural network control system of vehicle active suspension was designed, simulation and analysis of random road input and sine wave input were carried on. The results show that, by comparison with the traditional suspension system, the peak and standard deviation of vehicle mass vertical acceleration decreased by 55.38% and 59.04%, the peak of vehicle mass vertical acceleration decreased by 49.96% when vehicle go through the sine wave at the speed of 5m/s, the rid

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Aria, Jozi, Pinto Tiago, Praça Isabel, Silva Francisco, Teixeira Brigida, and Vale Zita. "Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting." Advances in Distributed Computing and Artificial Intelligence Journal 8, no. 1 (2020): 55–64. https://doi.org/10.14201/ADCAIJ2019815564.

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This paper presents the application of a Methodology to Obtain Genetic fuzzy rulebased systems Under the iterative rule Learning approach (MOGUL) to forecast energy consumption. Historical data referring to the energy consumption gathered from three groups, namely lights, HVAC and electrical socket, are used to train the proposed approach and achieve forecasting results for the future. The performance of the proposed method is compared to that of previous approaches, namely Hybrid Neural Fuzzy Interface System (HyFIS) and Wang and Mendel’s Fuzzy Rule Learning Method (WM). Results show th

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Tian Swee, Tan, Jahanzeb Sheikh, Hira Zahid, et al. "Enhancing the Performance of Pressure Regulation via Genetic Algorithm (GA) for Negative Pressure Wound Therapy (NPWT) System." Malaysian Journal of Fundamental and Applied Sciences 21, no. 3 (2025): 2146–58. https://doi.org/10.11113/mjfas.v21n3.4226.

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Hard-to-heal wounds, such as diabetic foot ulcers, have become a significant healthcare concern with the rising incidence of diabetes. Negative Pressure Wound Therapy (NPWT) has shown advantages over traditional wound management methods, but the classical NPWT controllers have issues with unstable pressure generation and occasional injury. To address this, fuzzy logic has been integrated into NPWT systems to enhance performance. However, fuzzy controllers have limitations due to uncertainty and inconsistency in system design. This study hybridizes a genetic algorithm (GA) with the fuzzy NPWT s

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Akbaş, Serkan, Türkan Erbay Dalkiliç, and Tuğba Gül Aksoy. "A Study on Portfolio Selection Based on Fuzzy Linear Programming." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 02 (2022): 211–30. http://dx.doi.org/10.1142/s021848852250009x.

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Portfolio management is the allocation of funds in the hands of the investor to ensure minimum risk and maximum profitability among the existing securities. In the finance sector related to portfolio management, many approaches, theories, and models have been developed. In this study, a new model is proposed. The basic objective of this model is to minimize the risk of portfolio while maximizing expected return of the portfolio. The proposed model is based on the Mean Absolute Deviation Model (MAD) proposed by Konno-Yamazaki. Considering the uncertainty of expected returns in the Mean Absolute

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García-Aguilar, Fernando, Miguel López-Fernández, David Barbado, Francisco J. Moreno, and Rafael Sabido. "Assessing Motor Variability during Squat: The Reliability of Inertial Devices in Resistance Training." Sensors 24, no. 6 (2024): 1951. http://dx.doi.org/10.3390/s24061951.

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Movement control can be an indicator of how challenging a task is for the athlete, and can provide useful information to improve training efficiency and prevent injuries. This study was carried out to determine whether inertial measurement units (IMU) can provide reliable information on motion variability during strength exercises, focusing on the squat. Sixty-six healthy, strength-trained young adults completed a two-day protocol, where the variability in the squat movement was analyzed at two different loads (30% and 70% of one repetition maximum) using inertial measurement units and a force

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Ferreira, Leandro, Tadayuki Yanagi Junior, Wilian Soares Lacerda, and Giovanni Francisco Rabelo. "A fuzzy system for cloacal temperature prediction of broiler chickens." Ciência Rural 42, no. 1 (2012): 166–71. http://dx.doi.org/10.1590/s0103-84782012000100027.

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Cloacal temperature (CT) of broiler chickens is an important parameter to classify its comfort status; therefore its prediction can be used as decision support to turn on acclimatization systems. The aim of this research was to develop and validate a system using the fuzzy set theory for CT prediction of broiler chickens. The fuzzy system was developed based on three input variables: air temperature (T), relative humidity (RH) and air velocity (V). The output variable was the CT. The fuzzy inference system was performed via Mamdani's method which consisted in 48 rules. The defuzzification was

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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." International Journal of Fuzzy System Applications 1, no. 3 (2011): 66–85. http://dx.doi.org/10.4018/ijfsa.2011070105.

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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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