Relevant bibliographies by topics / Fuzzy source information

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

Academic literature on the topic 'Fuzzy source information'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Fuzzy source information"

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Wang, Hong, and Hong Li. "Uncertainty Measure for Multisource Intuitionistic Fuzzy Information System." Complexity 2022 (April 7, 2022): 1–21. http://dx.doi.org/10.1155/2022/3605881.

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Multisource information systems and multigranulation intuitionistic fuzzy rough sets are important extended types of Pawlak’s classical rough set model. Multigranulation intuitionistic fuzzy rough sets have been investigated in depth in recent years. However, few studies have considered this combination of multisource information systems and intuitionistic fuzzy rough sets. In this paper, we give the uncertainty measure for multisource intuitionistic fuzzy information system. Against the background of multisource intuitionistic fuzzy information system, each information source is regarded as a
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Zhang, Yun, and Chaoxia Qin. "A Gaussian-Shaped Fuzzy Inference System for Multi-Source Fuzzy Data." Systems 10, no. 6 (2022): 258. http://dx.doi.org/10.3390/systems10060258.

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Fuzzy control theory has been extensively used in the construction of complex fuzzy inference systems. However, we argue that existing fuzzy control technologies focus mainly on the single-source fuzzy information system, disregarding the complementary nature of multi-source data. In this paper, we develop a novel Gaussian-shaped Fuzzy Inference System (GFIS) driven by multi-source fuzzy data. To this end, we first propose an interval-value normalization method to address the heterogeneity of multi-source fuzzy data. The contribution of our interval-value normalization method involves mapping
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Zhang, Jin Fang, Hou Jin Chen, and Lei Fan. "Fuzzy Logic and Classification of Information Sources." Applied Mechanics and Materials 614 (September 2014): 378–80. http://dx.doi.org/10.4028/www.scientific.net/amm.614.378.

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Classic information theory is based on the probability information which isn't involved in any fuzzy events. A hypothesis of transformation and classification of uncertain information sources presented in this paper is exploited to explain several phenomena in information retrieval. First, the form of uncertainty is introduced including fuzzy and random. Then an information source containing a sample space and a fuzzy set is supposed to illustrate the hypothesis based on which a method of identification for information sources is educed. And there are also some problems for further study, such
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Luo, Jin, and Qi Bin Deng. "Application of Uncertain Information Fusion in Diagnosis Decision of Electronic Equipment." Applied Mechanics and Materials 341-342 (July 2013): 715–18. http://dx.doi.org/10.4028/www.scientific.net/amm.341-342.715.

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Focuses on how to dispose the multi-source uncertain information and promote the testability evaluation and fault diagnosis capability of the electronic equipment, this paper uses fuzzy theory in the uncertain information description and modeling. Based on the fuzzy set description of fuzzy target, new method is proposed to obtain fuzzy evidences from fuzzy fault features, and then, Dempster-Shafer combination rule are used to fuse multi-source fuzzy evidence to get diagnosis results. The proposed method of fuzzy evidence extraction can reduces uncertainties in fusion makings and improves faul
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Lin, Guoping, Jiye Liang, Yuhua Qian, and Jinjin Li. "A fuzzy multigranulation decision-theoretic approach to multi-source fuzzy information systems." Knowledge-Based Systems 91 (January 2016): 102–13. http://dx.doi.org/10.1016/j.knosys.2015.09.022.

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Xu, Weihua, Mengmeng Li, and Xizhao Wang. "Information Fusion Based on Information Entropy in Fuzzy Multi-source Incomplete Information System." International Journal of Fuzzy Systems 19, no. 4 (2016): 1200–1216. http://dx.doi.org/10.1007/s40815-016-0230-9.

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Zhang, Pengfei, Yuxin Zhao, Dexian Wang, Yujie Zhang, and Zheng Yu. "Fuzzy Rough Set Models Based on Fuzzy Similarity Relation and Information Granularity in Multi-Source Mixed Information Systems." Mathematics 12, no. 24 (2024): 4039. https://doi.org/10.3390/math12244039.

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As a pivotal research method in the field of granular computing (GrC), fuzzy rough sets (FRSs) have garnered significant attention due to their successful overcoming of the limitations of traditional rough sets in handling continuous data. This paper is dedicated to exploring the application potential of FRS models within the framework of multi-source complex information systems, which undoubtedly holds profound research significance. Firstly, a novel multi-source mixed information system (MsMIS), encompassing five distinct data types, is introduced, thereby enriching the dimensions of data pr
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Jiang, Ping, Yuzhen Wang, and Aidong Ge. "Multivariable Fuzzy Control Based Mobile Robot Odor Source Localization via Semitensor Product." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/736720.

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In order to take full advantage of the multisensor information, a MIMO fuzzy control system based on semitensor product (STP) is set up for mobile robot odor source localization (OSL). Multisensor information, such as vision, olfaction, laser, wind speed, and direction, is the input of the fuzzy control system and the relative searching strategies, such as random searching (RS), nearest distance-based vision searching (NDVS), and odor source declaration (OSD), are the outputs. Fuzzy control rules with algebraic equations are given according to the multisensor information via STP. Any output ca
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Nguyen, Hoa, Nguyen Thi Uyen Nhi, and Le Nhat Duy. "FUZZY RELATIONAL DATABASE MODEL AND MANAGEMANT SYSTEM FOR IMPRECISE INFORMATION." Journal of Computer Science and Cybernetics 37, no. 2 (2021): 145–62. http://dx.doi.org/10.15625/1813-9663/37/2/15346.

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This paper introduces a fuzzy relational database model (FRDB) and the management system for it. FRDB is built by extending the classical relational database model with the fuzzy membership degree of tuples in relations. The management system for FRDB with the querying language like SQL is built by using a classical open-source management system.
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GARRIDO, CARMEN, NICOLÁS MARÍN, and OLGA PONS. "FUZZY INTERVALS TO REPRESENT FUZZY VALID TIME IN A TEMPORAL RELATIONAL DATABASE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, supp01 (2009): 173–92. http://dx.doi.org/10.1142/s021848850900608x.

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Temporal databases offer a common framework to those database applications that need to store or handle different types of temporal data from a variety of sources. They allow the concept of time to be handled from the point of view of meaning, representation, and manipulation. Although at first sight the incorporation of time into a database might appear to be a direct and simple task, it is, however, quite complex: not only must new structures and specific operators be included, but the semantics of conventional DML sentences (insert, update, or delete) and queries must be appropriately chang
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Dissertations / Theses on the topic "Fuzzy source information"

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Mooney, Gabrielle Joanne. "Intelligent information retrieval from the World Wide Web using fuzzy user modelling." Thesis, De Montfort University, 1999. http://hdl.handle.net/2086/10685.

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This thesis investigates the application. of fuzzy logic techniques and user modelling to the process of information retrieval (IR) from the World Wide Web (WWW). The research issue is whether this process can be improved through such an application. The exponential rise of information itself as an invaluable global commodity, coupled with .acceierating development in. computing and telecommunications, and boosted by networked information sources such as the WWW, has led to the development of tools, such as search engines, to facilitate information search and retrieval. However, despite their
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Zayrit, Karima. "Fusion de données imparfaites multi-sources : application à la spatialisation qualifiée des pratiques agricoles." Thesis, Reims, 2015. http://www.theses.fr/2015REIMS041/document.

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Notre thèse s'inscrit dans le cadre de la mise en place d'un observatoire des pratiques agricoles dans le bassin versant de la Vesle. L'objectif de ce système d'information agri-environnemental est de comprendre les pratiques responsables de la pollution de la ressource en eau par les pesticides d'origine agricole sur le territoire étudié et de fournir des outils pertinents et pérennes pour estimer leurs impacts. Notre problématique concerne la prise en compte de l'imperfection dans le processus de la fusion de données multi-sources et imparfaites. En effet, l'information sur les pratiques n'e
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Wang, Chen-Sheng, and 王振生. "Applying Fuzzy Information Confusion for Distributed Computing and Image's Source Camera Identification." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/94240523778919989998.

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博士<br>國立交通大學<br>資訊管理研究所<br>98<br>The boom of the Internet has extended the scope of information systems to cover the whole enterprise supply chain. Through the evolving process of information systems, the more important role a decision support system plays, the more demands to mine the intelligence behind information in the enterprise database. For example, to get optimized resources allocation, it is a typical application to obtain decision support information on production-distribution planning by fusing data from purchasing, production, distribution and sales information systems in a supply
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Fuller, Benjamin Woodbury. "Strong key derivation from noisy sources." Thesis, 2015. https://hdl.handle.net/2144/15636.

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A shared cryptographic key enables strong authentication. Candidate sources for creating such a shared key include biometrics and physically unclonable functions. However, these sources come with a substantial problem: noise in repeated readings. A fuzzy extractor produces a stable key from a noisy source. It consists of two stages. At enrollment time, the generate algorithm produces a key from an initial reading of the source. At authentication time, the reproduce algorithm takes a repeated but noisy reading of the source, yielding the same key when the two readings are close. For ma
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Books on the topic "Fuzzy source information"

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Nair, Premchand S. Uncertainty in multi-source databases. Springer, 2003.

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Book chapters on the topic "Fuzzy source information"

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Shams, Wafaa khazaal, Abdul Wahab, Uvais Qadwi, and Imad Fakhri Al-shaikhli. "Emotion Recognition Model Using Source-Temporal Features and Fuzzy." In Neural Information Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42054-2_72.

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Yager, Ronald R. "Multi-source Information Fusion Using Measure Representations." In On Logical, Algebraic, and Probabilistic Aspects of Fuzzy Set Theory. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28808-6_12.

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Hou, Xuecheng, Ning Kang, and Yixiang Chen. "A Trustworthiness Fuzzy Evaluation Model for Open Source Community." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7869-4_5.

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Sheng, Quan-wei. "Multi Source Information Matching and Fusion Method in Wireless Network Based on Fuzzy Set." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94554-1_5.

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Wang, Mengyu. "Research on Odor Source Location Tracking Method Based on Multi-sensor Information Fusion Technology of Fuzzy Integral Fusion Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2653-4_21.

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Urrutia, Angélica, Hector Valdes, and José Galindo. "Comparison of K-Means and Fuzzy C-Means Data Mining Algorithms for Analysis of Management Information: An Open Source Case." In Distributed Computing and Artificial Intelligence. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00551-5_23.

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Li, Ai-Ping, and Quan-Yuan Wu. "On Aggregation Operators for Fuzzy Information Sources." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30466-1_21.

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Ma, Yucheng, Peisong Shen, Kewei Lv, Xue Tian, and Chi Chen. "General Constructions of Fuzzy Extractors for Continuous Sources." In Information Security and Cryptology. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0942-7_9.

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De Tré, Guy. "Making Large Information Sources Better Accessible Using Fuzzy Set Theory." In On Fuzziness. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35641-4_21.

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Ravi Sankar, R. S., and S. V. Jaya Ram Kumar. "Fuzzy Current Control of Grid Interactive Voltage Source Converter with Solar Energy." In Smart Computing and Informatics. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5544-7_20.

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Conference papers on the topic "Fuzzy source information"

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Fuller, Benjamin, and Lowen Peng. "Continuous-Source Fuzzy Extractors: Source uncertainty and insecurity." In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849421.

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Yu, Jianhang, and Weihua Xu. "Information fusion for multi-source fuzzy information system with the same structure." In 2015 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2015. http://dx.doi.org/10.1109/icmlc.2015.7340917.

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Hassan, Mehdi, Asmatullah Chaudhry, Asifullah Khan, M. Aksam Iftikhar, and Jin Young Kim. "Medical image segmentation employing information gain and fuzzy c-means algorithm." In 2013 International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2013. http://dx.doi.org/10.1109/icosst.2013.6720602.

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Kabzinski, Jacek, and Marcin Jastrzebski. "Fuzzy modeling of complex, multi-source, dynamic friction." In 2015 XXV International Conference on Information, Communication and Automation Technologies (ICAT). IEEE, 2015. http://dx.doi.org/10.1109/icat.2015.7340528.

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Shafiq, Sobia, Muhammad Adeel Asghar, Muhammad Emad Amjad, and Jawwad Ibrahim. "An Effective Early Stage Detection of Lung Cancer Using Fuzzy Local Information cMean and GoogLeNet." In 2022 16th International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2022. http://dx.doi.org/10.1109/icosst57195.2022.10016866.

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Jia, Jia, Yongxin Wang, Zhu Ren, and Lianhong Cai. "Intention understanding based on multi-source information integration for Chinese Mandarin spoken commands." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6233831.

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Wang, Chenglong, Mao Xu, and Bolong Yan. "WOA-based fuzzy self-tuning PID evaporative source temperature control." In International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), edited by Johan Debayle. SPIE, 2022. http://dx.doi.org/10.1117/12.2658354.

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Saha, A. K., S. P. Chowdhury, and S. Chowdhury. "PEM fuel cell as energy source using fuzzy logic controller." In IET-UK International Conference on Information and Communication Technology in Electrical Sciences (ICTES 2007). IEE, 2007. http://dx.doi.org/10.1049/ic:20070626.

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Fan, Caisheng, Xue-Dong Liu, Feiyu Li, and Yuxin Liang. "Realization of Fuzzy PID Controller on FPGA for Source Measurement Unit." In ICITEE 2022: 5th International Conference on Information Technologies and Electrical Engineering. ACM, 2022. http://dx.doi.org/10.1145/3582935.3583010.

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Gomzov, N. "APPLICATION OF NEURAL FUZZY CLASSIFICATION IN DECISION SUPPORT SYSTEMS." In CHALLENGING ISSUES IN SYSTEMS MODELING AND PROCESSES. FSBE Institution of Higher Education Voronezh State University of Forestry and Technologies named after G.F. Morozov, 2025. https://doi.org/10.58168/cismp2024_283-288.

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The article proposes a classification of fuzzy information about the terrain for solving problems in the fields of agriculture and forestry. This classification allows for a more accurate description and understanding of the characteristics of fuzzy information, which can be useful for decision-making under uncertainty. Special attention is given to the area of forest fire modeling using geographic information systems. A hybrid fuzzy network is proposed for classifying the parameters of the fire source and the environment to select the optimal analytical model.
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