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Books on the topic 'Fuzzy inference system expert'

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Published: 5 June 2025

Last updated: 16 July 2025

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1

Grabisch, Michel. Fundamentals of uncertainty calculi with applications to fuzzy inference. Kluwer Academic Publishers, 1995.

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2

S, Teichrow Jon, University of Houston--Clear Lake. Research Institute for Computing and Information Systems., and Lyndon B. Johnson Space Center. Information Technology Division., eds. Real-time fuzzy inference based robot path planning: Final report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1990.

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3

Moti, Schneider, ed. Fuzzy expert system tools. John Wiley, 1996.

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4

Neelanarayanan, ed. Multi-step Prediction of Pathological Tremor With Adaptive Neuro Fuzzy Inference System (ANFIS). Association of Scientists, Developers and Faculties, 2014.

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5

M, Welch Ronald, and United States. National Aeronautics and Space Administration., eds. Global single and multiple cloud classification with a fuzzy logic expert system. National Aeronautics and Space Administration, 1996.

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6

M, Welch Ronald, and United States. National Aeronautics and Space Administration., eds. Global single and multiple cloud classification with a fuzzy logic expert system. National Aeronautics and Space Administration, 1996.

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7

B, Sheridan Thomas, and Lyndon B. Johnson Space Center. Information Technology Division., eds. Expert system training and control based on the fuzzy relation matrix: Final report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1991.

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8

Angelov, Plamen P. Evolving rule-based models: A tool for design of flexible adaptive systems. Physica-Verlag, 2002.

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9

Chernichovsky, Dov. A fuzzy logic approach toward solving the analytic maze of health system financing. National Bureau of Economic Research, 2001.

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10

Tamir, Dan E. A schema for knowledge representation and its implementation in a computer-aided design and manufacturing system. TÜV Rheinland, 1989.

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11

Relander, Sami. Towards approximate reasoning on new software product company success potential estimation: A design science based fuzzy logic expert system. Helsinki School of Economics, 2008.

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12

Park, Min Young. Evaluation of a fuzzy-expert system for fault diagnosis in power systems: Using an object-oriented hybrid solution for real-time power alarm processing. Bournemouth University, 2001.

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13

Kosko, Bart. Neural networks and fuzzy systems: A dynamical systems approach to machine intelligence. Prentice-Hall, 1992.

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14

Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

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Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI.  The textbook in the field of training "Computer Science and Computer Engineering"

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15

Varlamov, Oleg. Mivar databases and rules. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in th

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16

Hung T. Hung T. Nguyen, E. A. Walker, and Michel Grabisch. Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference. Springer London, Limited, 2013.

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17

Nguyen, Hung T., Michel Grabisch, and E. A. Walker. Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference. Springer, 2010.

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18

Real-time fuzzy inference based robot path planning: Final report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1990.

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19

Fuzzy Inference System. NY RESEARCH PRESS, 2015.

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20

Azeem, Mohammad Fazle, ed. Fuzzy Inference System - Theory and Applications. InTech, 2012. http://dx.doi.org/10.5772/2341.

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21

Niemiec, Mariusz. Fuzzy Inference System - Theory and Applications. Scitus Academics LLC, 2017.

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22

Uncu, Özge. Type 2 fuzzy system models with type 1 inference. 2003.

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23

Jiang, Shaoning. Rule base reorganization for fuzzy expert system design. 1996.

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24

Tian, Yunyan. Fuzzy expert system with application to production management. 1993.

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25

Guo, Lizhu. Key optimization issues for the design of fuzzy-inference system. 1997.

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26

Lenat, Douglas B., and R. V. Guha. Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Addison-Wesley Pub (Sd), 1990.

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27

National Aeronautics and Space Administration (NASA) Staff. Fuzzy Expert System for Fault Management of Water Supply Recovery in the Alss Project. Independently Published, 2018.

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28

"A fuzzy expert system for fault management of water supply recovery in the ALSS project": Final technical report, project #NAG9-777. National Aeronautics and Space Administration, 1998.

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29

An autonomous fault detection, isolation, and recovery system for a 20-kHz electric power distribution test bed. National Aeronautics and Space Administration, 1991.

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30

Kosko, Bart. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence/Book and Disk. Prentice Hall, 1991.

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31

Kosko, Bart. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence/Book and Disk. Prentice Hall, 1991.

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32

Ślusarski, Marek. Metody i modele oceny jakości danych przestrzennych. Publishing House of the University of Agriculture in Krakow, 2017. http://dx.doi.org/10.15576/978-83-66602-30-4.

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The quality of data collected in official spatial databases is crucial in making strategic decisions as well as in the implementation of planning and design works. Awareness of the level of the quality of these data is also important for individual users of official spatial data. The author presents methods and models of description and evaluation of the quality of spatial data collected in public registers. Data describing the space in the highest degree of detail, which are collected in three databases: land and buildings registry (EGiB), geodetic registry of the land infrastructure network

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