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Добірка наукової літератури з теми "FUZZY INDEX"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "FUZZY INDEX"

Omana, Rebecca Walo. "FUZZY CONLEY INDEX AND INVARIANT FUZZY SETS." Advances in Fuzzy Sets and Systems 26, no. 2 (October 20, 2021): 175–209. http://dx.doi.org/10.17654/fs026020175.

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BUSTINCE, H., E. BARRENECHEA, M. PAGOLA, and F. SORIA. "WEAK FUZZY S-SUBSETHOOD MEASURES: OVERLAP INDEX." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, no. 05 (October 2006): 537–60. http://dx.doi.org/10.1142/s0218488506004187.

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Starting from the definitions of the weak fuzzy subsethood measure (V.R. Young, Fuzzy subsethood. Fuzzy Sets and Systems, 77 (1996) 371–384) and of strong fuzzy inclusion, (D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications, New York, Academic Press, 1980) we define weak fuzzy S-subsethood measures. We analyze their axiomatization and present various constructions obtained by aggregating implication operators with special properties. We conclude by presenting the conditions under which we can construct fuzzy entropies and overlap indices from these measures.

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Abdolshah, Mohammad. "Measuring Loss-Based Process Capability IndexLeand Its GenerationLe′′with Fuzzy Numbers." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/217406.

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Loss-based process capability indices are appropriate and realistic tools in order to measure the process capability. Among them, indexLeand its generationLe′′are well-known loss-based process capability indices, whose concepts are based on the worth (the opposite concept of loss). Sometimes, in order to calculateLeandLe′′there are some uncertainties in observations, so fuzzy logic can be employed to manage the uncertainties. This paper investigates fuzzification of process capability indexL~eand its generationL~e′′. In order to find the membership function of process capability indicesL~eandL~e′′, theα-cuts of fuzzy observation were employed. Then with an example of fuzzy process capability index,L~eandL~e′′were calculated and compared. Results showed that fuzzyL~e′′was more sensitive compared withL~eand was increased while the target departs (asymmetric tolerance). This example also showed that, with departure from the target, variation of fuzzyL~e′′and consequently its fuzziness were increased.

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Bala, Kalla Hema, and Dr B. Srinivasa Kumar. "Index Number of Multi Fuzzy Sets." International Journal of Recent Technology and Engineering (IJRTE) 8, no. 4 (November 30, 2019): 2873–76. http://dx.doi.org/10.35940/ijrte.d2888.118419.

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Multi fuzzy set theory is an extension of fuzzy set theory. In this paper we developing the theory of Index number of multi fuzzy sets. This theory is applied to medical diagnosis system and will help doctors to select the effective symptoms and could make diagnosis of diseases concern. This theory also helps in selecting right political leaders.

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BUSTINCE, HUMBERTO, EDURNE BARRENECHEA, MIGUEL PAGOLA, JAVIER FERNANDEZ, CARLOS GUERRA, PEDRO COUTO, and PEDRO MELO-PINTO. "GENERALIZED ATANASSOV'S INTUITIONISTIC FUZZY INDEX: CONSTRUCTION OF ATANASSOV'S FUZZY ENTROPY FROM FUZZY IMPLICATION OPERATORS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19, no. 01 (February 2011): 51–69. http://dx.doi.org/10.1142/s0218488511006885.

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In this paper we introduce the concept of Generalized Atanassov's Intuitionistic Fuzzy Index. We characterize it in terms of fuzzy implication operators and propose a construction method with order automorphisms. Finally, we obtain, by means of special aggregation functions applied to the generalized Atanassov's intuitionistic fuzzy index, the Atanassov's intuitionistic fuzzy entropy given by Burillo and Bustince.

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Shi, Xiaolong, Maryam Akhoundi, Ali Asghar Talebi, and Seyed Hossein Sadati. "Some Properties of Cubic Fuzzy Graphs with an Application." Symmetry 14, no. 12 (December 11, 2022): 2623. http://dx.doi.org/10.3390/sym14122623.

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The advent of fuzzy sets, and consequently fuzzy graphs, has solved many problems in ambiguous and uncertain contexts. It is interesting and necessary to study the Wiener index in a cubic fuzzy graph that employs both fuzzy membership and interval-valued fuzzy membership at the same time. In this paper, the Wiener index in a cubic fuzzy graph is introduced as a cubic fuzzy number and some related results are described. The comparison between connectivity index and Wiener index, changes in Wiener index through deleting a node or an edge, and determining the Wiener index in some specific cubic fuzzy graphs have been the other topics studied in this research. In addition, the Wiener index is determined by mentioning concepts of the saturated cubic fuzzy cycle. In this review, the Wiener index is shown as a combination of classical and interval numbers. The results indicate that when some vertices are removed, the Wiener index may change. However, this change will not be exclusively related to both values. Finally, an application of the Wiener index is presented in the study of the properties of some monomer molecules.

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Wang, Gui Xiang, and Guang Tao Zhou. "Ranking Fuzzy Numbers Based on the Mean and Fuzzy Degree of Fuzzy Number." Applied Mechanics and Materials 220-223 (November 2012): 2102–8. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2102.

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In this paper, a novel approach to ranking fuzzy numbers based on the mean and the fuzzy degree of fuzzy number is proposed. In the approach, a new ranking index that is comprehensive consideration of the mean and the fuzzy degree of fuzzy number is constructed, and then the properties of the ranking index are given. Moreover, to compare the proposed approach with the existing approaches, numerical examples are given. The comparative results illustrate that the approach proposed in this paper is better and has simpler calculations.

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De, Sujit Kumar, and Shib Sankar Sana. "Fuzzy order quantity inventory model with fuzzy shortage quantity and fuzzy promotional index." Economic Modelling 31 (March 2013): 351–58. http://dx.doi.org/10.1016/j.econmod.2012.11.046.

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Wang, Zhenyuan, and Li Zhang-Westmant. "New Ranking Method For Fuzzy Numbers By Their Expansion Center." Journal of Artificial Intelligence and Soft Computing Research 4, no. 3 (July 1, 2014): 181–87. http://dx.doi.org/10.1515/jaiscr-2015-0007.

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Abstract Based on the area between the curve of the membership function of a fuzzy number and the horizontal real axis, a characteristic as a new numerical index, called the expansion center, for fuzzy numbers is proposed. An intuitive and reasonable ranking method for fuzzy numbers based on this characteristic is also established. The new ranking method is applicable for decision making and data analysis in fuzz environments. An important criterion of the goodness for ranking fuzzy numbers, the geometric intuitivity, is also introduced. It guarantees coinciding with the natural ordering of the real numbers.

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Gil, María Angeles, Miguel López-Díaz, and Hortensia López-García. "The fuzzy hyperbolic inequality index associated with fuzzy random variables." European Journal of Operational Research 110, no. 2 (October 1998): 377–91. http://dx.doi.org/10.1016/s0377-2217(97)00252-x.

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Дисертації з теми "FUZZY INDEX"

Keskin, Sinan. "Implementation Of X-tree With 3d Spatial Index And Fuzzy Secondary Index." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612807/index.pdf.

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Multidimensional datasets are getting more extensively used in Geographic Information Systems (GIS) applications in recent years. Due to large volume of these datasets efficient querying becomes a significant problem. For this purpose, before creating index structure with these enormous datasets, choosing an efficient index structure is an urgent necessity. The aim of this thesis is to develop an efficient, flexible and extendible index structure which comprises 3D spatial data in primary index and fuzzy attributes in secondary index. These primary and secondary indexes are handled in a coupled structure. Firstly, a 3D spatial primary index is built by using X-tree structure, and then a fuzzy secondary index is overlaid over the X-tree structure. The coupled structure is shown more efficient on a certain class of queries than uncoupled index structures comprising 3D spatial data in primary index and fuzzy attributes in secondary index separately. In uncoupled index structure, we provided 3D spatial primary index by using X-tree index structure and fuzzy secondary index by using BPlusTree index structure.

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Biswas, Ajoy. "Dynamic gait stability index using a fuzzy logic model." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27334.

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The field of physical rehabilitation needs a quantitative technique for measuring dynamic gait stability (walking stability) to make decisions regarding treatment, assistive devices, potential for falls, and to track progress during rehabilitation. This measure should be easily applied in a clinical setting, valid across physical disabilities, and must provide a quantitative index that can be used for comparisons over a range of tasks and environments. This research project developed and evaluated a gait stability index using a portable pressure measurement system. This study examined current stability analysis methods and evaluated dynamic gait stability assessment parameters. Six gait parameters were identified and extracted from a commercial foot pressure measurement system (F-Scan). A fuzzy logic controller combined the six parameters and generated an index value. Fifteen healthy subjects were evaluated at four stability levels. All parameter combinations (57 indices in total) were assessed to isolate the most effective index.

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Musongole, Chibelushi Maxwell C. "Fuzzy modelling of the Johannesburg Security Exchange overall index." Doctoral thesis, University of Cape Town, 2002. http://hdl.handle.net/11427/7711.

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Bibliography: leaves 181-206.
This thesis focuses on the empirical analysis of the fuzzy feature of the Johannesburg Stock Exchange Overall Index using fuzzy logic techniques. The data for the periods 1985 - 2001 is used in the analysis. Description of the fuzzy feature is crucial to the proper understanding of the movement of the JSE Overall Index and the South African economy. The fuzzy feature of the Johannesburg Security Exchange Overall Index if understood would impact on the ﬁnancial and economical decisions. A preliminary Fractal analysis is carried out before the fuzzy analysis to investigate the nature of the Johannesburg Security Exchange Overall Index. The Johannesburg Security Exchange Overall Index experiences the Hurst phenomena of long memory for periods of 100 days (approximately three months). Outside the long memory periods, the Johannesburg Security Exchange Overall Index is found to exhibit antipersistent or short-range dependency characteristics. The fuzzy feature of the Johannesburg Security Exchange Overall Index is described by many aspects of fuzzy logic. The analysis of the fuzzy feature is carried out according to time periods of approximately 4 years each of the Johannesburg Security Exchange Overall Index. The index in each time period is partitioned in three fuzzy states: "low", "middle" and "high". The fuzzy states are important in assessing the fuzzy nature of the Johannesburg Security Exchange Overall Index. The partitioning reveals that the fuzzy states of the Johannesburg Security Exchange Overall Index do not possess sharp boundaries. The sizes of the fuzzy states are found to change with time. This reﬂects changes in the forces behind the dynamics of the index.

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Zarei, Anahita. "A novel assessment index and intelligent predictive models for orthodontics /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6093.

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Havlíček, Petr. "Spornost fuzzy logických teorií v odvozovacích systémech." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-15839.

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This thesis focus on consistency of a specific class of fuzzy logic theories that represent certain inference system. This class of theories is defined as theories containing especially so called special axioms representing rules of modeled inference system and evaluated set of formulas representing case data. Functional approach is used to develop three popular fuzzy calculi: the Gödel logic, Łukasiewicz logic and product logic. As a language it is used the language of first order propositional fuzzy logic with valuation. To check consistency we use the concept of inconsistency degree and in Łukasiewicz logic also the principle of polar index. The concept of consistency degree is also described, but not used. Simple algorithm is developed to check consistency of theory upon the basis of inconsistency degree principle. A method of use of polar index is also described and illustrated. For each fuzzy theory a term of corresponding classical theory is defined. Then consistency of fuzzy theories and their corresponding classical theories are compared. The results of comparison are presented on the example of the ad-hoc created diagnostic inference system MEDSYS II. In the end the relation between consistency of fuzzy theory of inference system and it's corresponding theory is introduced for all three used calculi and both contradiction concepts.

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Weil, Audrey M. "Predictors of Reasoning Ability: Working Memory Capacity and Fuzzy Processing Preference Index." Miami University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=miami1397745903.

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Velayutham, Kandasamy Vivek Prasad. "Development of a Performance Index for Stormwater Pipe Infrastructure using Fuzzy Inference Method." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78292.

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Stormwater pipe infrastructure collects and conveys surface runoff resulting from rainfall or snowmelt to nearby streams. Traditionally, stormwater pipe systems were integrated with wastewater infrastructure through a combined sewer system. Many of these systems are being separated due to the impact of environmental laws and regulations; and the same factors have led to the creation of stormwater utilities. However, in the current ASCE Infrastructure Report Card, stormwater infrastructure is considered a sub-category of wastewater infrastructure. Stormwater infrastructure has always lacked attention compared to water and wastewater infrastructure. However, this notion has begun to shift, as aging stormwater pipes coupled with changes in climatic patterns and urban landscapes makes stormwater infrastructure more complex to manage. These changes and lack of needed maintenance has resulted in increased rates of deterioration and capacity. Stormwater utility managers have limited resources and funds to manage their pipe system. To effectively make decisions on allocating limited resources and funds, a utility should be able to understand and assess the performance of its pipe system. There is no standard rating system or comprehensive list of performance parameters for stormwater pipe infrastructure. Previous research has identified performance parameters affecting stormwater pipes and developed a performance index using a weighted factor method. However, the weighted performance index model does not capture interdependencies between performance parameters. This research developed a comprehensive list of parameters affecting stormwater pipe performance. This research also developed a performance index using fuzzy inference method to capture interdependencies among parameters. The performance index was evaluated and validated with the pipe ratings provided by one stormwater utility to document its effectiveness in real world conditions.
Master of Science

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Duprey, Benjamin Lawrence Blake. "A New Fuzzy Based Stability Index Using Predictive Vehicle Modeling and GPS Data." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/32762.

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The use of global positioning systems, or GPS, as a means of logistical organization for fleet vehicles has become more widespread in recent years. The system has the ability to track vehicle location, report on diagnostic trouble codes, and keep tabs on maintenance schedules. This helps to improve the safety and productivity of the vehicles and their operators. Additionally, the increasing use of yaw and roll stability control in commercial trucks has contributed to an increased level of safety for truck drivers. However, these systems require the vehicle to begin a yaw or roll event before they assist in maintaining control. This thesis presents a new method for utilizing the GPS signal in conjunction with a new fuzzy logic-based stability index, the Total Safety Margin (TSM), to create a superior active safety system.

This thesis consists of four main components:

An overview of GPS technology is presented with coverage of several automotive-based applications. The proposed implementation of GPS in the new Hardware-in-the-Loop (HIL) driving simulator under development at the Virginia Tech Center for Vehicle Systems and Safety (CVeSS) is presented.
The three degree-of-freedom (3DOF), linear, single track equation set used in the Matlab simulations is derived from first principles.
Matlab and TruckSim 7Â® simulations are performed for five vehicle masses and three forward velocities in a ramp-steer maneuver. Using fuzzy logic to develop the control rules for the Total Safety Margin (TSM), TSM matrices are built for both the Matlab and TruckSim 7Â® results based on these testing conditions. By comparing these TSM matrices it is shown that the two simulation methods yield similar results.
A discussion of the development and implementation of the aforementioned HIL driving simulator is presented, specifically the steering subsystem. Using Matlab/Simulink, dSPACE ControlDesk, and CarSim RTÂ® software it is shown that the steering module is capable of steering the CarSim RTÂ® simulation vehicle accurately within the physical range of the steering sensor used.

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Hřebíčková, Jana. "Využití fuzzy logiky pro hodnocení investičních rizik." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-316944.

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This master thesis is dealing with development and application of a model that employs fuzzy logic. The model is used to support a decision of a selected Czech company that plans to invest abroad and establish a subsidiary. Selected indicators determine an investment risk in all countries considered for the investment. The model was implemented in parallel in two systems: MS Excel and MATLAB®.

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Yeung, Fai Yip. "Developing a Partnering Performance Index (PPI) for construction projects : a fuzzy set theory approach." online access from Digital Dissertation Consortium, 2007. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?3299884.

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Книги з теми "FUZZY INDEX"

Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0.

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Ramnath, Radha. Index structures for fuzzy databases. 1995.

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Atanassov, Krassimir T. Index Matrices: Towards an Augmented Matrix Calculus. Springer International Publishing AG, 2016.

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Atanassov, Krassimir T. Index Matrices: Towards an Augmented Matrix Calculus. Springer, 2014.

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Atanassov, Krassimir T. Index Matrices: Towards an Augmented Matrix Calculus. Springer, 2014.

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Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model: Case Study in Perak River, Malaysia. Springer, 2020.

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Частини книг з теми "FUZZY INDEX"

Yan, Li, Zongmin Ma, and Fu Zhang. "Fuzzy XML Queries and Index." In Fuzzy XML Data Management, 101–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-44899-7_4.

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Mordeson, John N., Sunil Mathew, and G. Gayathri. "Cyclic Connectivity Status and Integrity Index of Fuzzy Graphs." In Fuzzy Graph Theory, 229–50. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-23108-7_10.

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Muzzioli, Silvia, Luca Gambarelli, and Bernard De Baets. "Towards a Fuzzy Index of Skewness." In Fuzzy Logic and Applications, 164–75. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12544-8_13.

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Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. "Fuzzy Multiple Linear Regression." In Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model, 11–21. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0_2.

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Boulmakoul, Azedine, Mohamed Haitam Laarabi, Roberto Sacile, and Emmanuel Garbolino. "Ranking Triangular Fuzzy Numbers Using Fuzzy Set Inclusion Index." In Fuzzy Logic and Applications, 100–108. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03200-9_11.

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Selvaraj, Ayyakkannu, Anakath Arasan, Sharvari Tamane, Rajendiran Kannadasan, Subbarayan Saravanan, Mayur K. Jadhav, and Ansari Mohammed Mohsin. "Mamdani Fuzzy Based Prediction of Rainfall Fuzzy Rainfall Index." In Advances in Computer Science Research, 838–50. Dordrecht: Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-136-4_74.

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Aguilar-Martin, Joseph. "Classification Validity Index." In Enric Trillas: A Passion for Fuzzy Sets, 261–67. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16235-5_20.

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Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. "Water Quality Index Using Fuzzy Regression." In Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model, 37–53. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0_5.

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Chen, Yuehui, Ajith Abraham, Ju Yang, and Bo Yang. "Hybrid Methods for Stock Index Modeling." In Fuzzy Systems and Knowledge Discovery, 1067–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11540007_137.

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Imanov, Gorkhmaz. "Fuzzy Estimation of National Green Economy Index and Investments Distribution." In Fuzzy Models in Economics, 55–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61282-5_4.

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Тези доповідей конференцій з теми "FUZZY INDEX"

"Authors index." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584757.

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"Author index." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891896.

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"Author Index." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494480.

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"Author Index." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882583.

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"FUZZ 2019 Index." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8858882.

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"FUZZ 2018 Index." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491493.

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"Author index." In The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05. IEEE, 2005. http://dx.doi.org/10.1109/fuzzy.2005.1452553.

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"FUZZ 2020 Author Index." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177643.

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Cardenas, J. Ruben G., Angela Nebot, and Francisco Mugica. "Comparison between Composite Index Solution Surfaces with Fuzzy Composite Index Decision Surfaces." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8858871.

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"Author Index." In 12th International Fuzzy Systems Conference. IEEE, 2003. http://dx.doi.org/10.1109/fuzz.2003.1206660.

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