W.H. Guier, G.C. Weiffenbach, Genesis of satellite navigation. Johns Hopkins APL Technical Digest (Applied Physics Laboratory) 19(1), 14–17 (1998)
Google Scholar
H. Worth and M. Warren, Transit To Tomorrow. JHU/APL., 2009.
Google Scholar
J. Hightower, G. Borriello, J. Hightower, G. Borriello, G. Borriello, Location systems for ubiquitous computing. Computer (Long. Beach. Calif). 34(8), 57–66 (2001)
Google Scholar
J. H. Reed, K. J. Krizman, B. D. Woerner, and T. S. Rappaport, “An Overview of the Challenges and Progress in Meeting the E-911 Requirement for Location Service,” IEEE Communications Magazine, no. April, pp. 30–37, 1998.
C. Drane, M. Macnaughtan, C. Scott, Positioning GSM telephones. IEEE Communications Magazine 36(4), 46–59 (1998)
Article
Google Scholar
J.J. Caffery, G.L. Stuber, Overview of radiolocation in CDMA cellular systems. IEEE Communications Magazine 36(4), 38–45 (1998)
Article
Google Scholar
A. Boukerche, E. F. Nakamura, H. a B. Oliveira, E. F. Nakamura, and F. Loureiro, “Localization systems for wireless sensor networks,” IEEE Wirel. …, vol. 14, no. December, pp. 6–12, Dec. 2007.
G. Acampora, B. Di Stefano, and A. Vitiello, “IEEE 1855TM: The First IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs],” IEEE Comput. Intell. Mag., vol. 11, no. 4, pp. 4–6, Nov. 2016.
M. Xiao et al., Millimeter Wave Communications for Future Mobile Networks. IEEE J. Sel. Areas Commun. 35(9), 1909–1935 (Sep. 2017)
Article
Google Scholar
G.A. Akpakwu, B.J. Silva, G.P. Hancke, A.M. Abu-Mahfouz, A Survey on 5G Networks for the Internet of Things: Communication Technologies and Challenges. IEEE Access 6, 3619–3647 (2018)
Article
Google Scholar
H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of Wireless Indoor Positioning Techniques and Systems,” IEEE Trans. Syst. Man Cybern. Part C (Applications Rev., vol. 37, no. 6, pp. 1067–1080, Nov. 2007.
F. Seco, A. R. Jimenez, C. Prieto, J. Roa, and K. Koutsou, “A survey of mathematical methods for indoor localization,” in 2009 IEEE International Symposium on Intelligent Signal Processing, 2009, pp. 9–14.
R. Mautz, “The challenges of indoor environments and specification on some alternative positioning systems,” in 2009 6th Workshop on Positioning, Navigation and Communication, 2009, pp. 29–36.
A. Bensky, Wireless positioning technologies and applications. Artech House, 2008.
Google Scholar
A. Tahat, G. Kaddoum, S. Yousefi, S. Valaee, and F. Gagnon, “A Look at the Recent Wireless Positioning Techniques With a Focus on Algorithms for Moving Receivers,” IEEE Access, vol. 4, pp. 6652–6680, 2016.
H. Singh et al., Real-Life Applications of Fuzzy Logic. Adv. Fuzzy Syst. 2013, 1–3 (Jun. 2013)
Article
Google Scholar
G. Q. Huang, A. B. Rad, and Y. K. Wong, “Online SLAM in dynamic environments,” in 12th International Conference on Advanced Robotics, ICAR ’05, Proceedings, 2005, pp. 262–267.
N. Zikos and V. Petridis, “6-DoF Low Dimensionality SLAM (L-SLAM),” J. Intell. Robot. Syst., vol. 79, no. 1, pp. 55–72, Jul. 2015.
E.M. Gorostiza, J.L.L. Galilea, F.J.M. Meca, D.S. Monzú, F.E. Zapata, L.P. Puerto, Infrared sensor system for mobile-robot positioning in intelligent spaces. Sensors 11(5), 5416–5438 (2011)
Article
Google Scholar
M. Bouet and A. L. Dos Santos, “RFID tags: Positioning principles and localization techniques,” 2008 1st IFIP Wireless Days, WD 2008. 2008.
H.H. Lin, C.C. Tsai, J.C. Hsu, Ultrasonic localization and pose tracking of an autonomous mobile robot via fuzzy adaptive extended information filtering. IEEE Transactions on Instrumentation and Measurement 57(9), 2024–2034 (2008)
Article
Google Scholar
W. Chen, T. Zhang, An indoor mobile robot navigation technique using odometry and electronic compass. Int. J. Adv. Robot. Syst. 14(3), 1–15 (2017)
Google Scholar
D. Navarro and G. Benet, “Magnetic map building for mobile robot localization purpose,” in ETFA 2009 - 2009 IEEE Conference on Emerging Technologies and Factory Automation, 2009, pp. 1–4.
R. Mautz, “Indoor Positioning Technologies,” Inst. Geod. Photogramm. Dep. Civil, Environ. Geomat. Eng. ETH Zurich, no. February 2012, p. 127, 2012.
S. He, S.-H.G.H.G. Chan, Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons. IEEE Commun. Surv. Tutorials 18(1), 466–490 (2016)
Article
Google Scholar
C. Wann and M. Lin, “Location Estimation with Data Fusion for Wireless Location Systems,” in Proceeding of the 2004 IEEE International Conference on Networkking, Sensing and Control, 2004, pp. 327–332.
M. Attia, Map Aided Indoor and Outdoor Navigation Applications (University of Calgary, Alberta, 2013)
Google Scholar
D.F. Larios, J. Barbancho, F.J. Molina, C. Leon, LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN. Ad Hoc Networks 10(3), 604–622 (2012)
Article
Google Scholar
D.F. Larios, J. Barbancho, F.J. Molina, C. León, Localization Method for Low-power Wireless Sensor Networks. Journal of Networks 8(1), 45–58 (2013)
Article
Google Scholar
D. Herrero-Pérez, H. Martínez-Barberá, K. Leblanc, A. Saffiotti, Fuzzy uncertainty modeling for grid based localization of mobile robots. International Journal of Approximate Reasoning 51(8), 912–932 (2010)
Article
Google Scholar
A. Saffiotti, “The Uses of Fuzzy Logic in Autonomous Robot Navigation,” Soft Comput., vol. 180, no. 197, pp. 1702–1711, 1997.
X. Zhou and P. Angelov, “An Approach to Autonomous Self-localization of a Mobile Robot in Completely Unknown Environment using Evolving Fuzzy Rule-based Classifier,” in IEEE Symposium on Computational Intelligence in Security and Defense Applications, 2007, no. Cisda, pp. 131–138.
S. Chen and C. Chen, “Probabilistic fuzzy system for uncertain localization and map building of mobile robots,” IEEE Trans. Instrum. Meas., vol. 61, no. 6, pp. 1546–1560, 2012.
M. Alakhras, M. Hussein, and M. Oussalah, “Multivariable fuzzy inference with multi nearest neighbour for indoor WLAN localization based on RSS fingerprint,” in Proceedings - UKSim 15th International Conference on Computer Modelling and Simulation, UKSim 2013, 2013, pp. 656–662.
M. Oussalah, M. Alakhras, M.I.I. Hussein, Multivariable fuzzy inference system for fingerprinting indoor localization. Fuzzy Sets Syst. 269, 65–89 (2015)
Article
MathSciNet
Google Scholar
H. Zhou and N. N. Van, “Indoor Fingerprint Localization Based on Fuzzy C-Means Clustering,” in Sixth International Conference on Measuring Technology and Mechatronics Automation, 2014, pp. 337–340.
S. Yun, J. Lee, W. Chung, E. Kim, S. Kim, A soft computing approach to localization in wireless sensor networks. Expert Syst. Appl. 36(4), 7552–7561 (2009)
Article
Google Scholar
M. Alakhras, M. Oussalah, and M. Hussein, “Fuzzy inference with parameter identification for indoor WLAN positioning,” in World Congress on Engineering, The 2015 International Conference of Wireless Networks, 2015, vol. 2217, pp. 641–648.
C.-Y. Chen, J.-P. Yang, G. Tseng, Y. Wu, and R.-C. Hwang, “An Indoor Positioning Technique Based on Fuzzy Logic,” in International MultiConference of Engineers and Computer Scientists, 2010, vol. II, pp. 17–20.
G. Kul, T. Ozyer, and B. Tavli, “IEEE 802.11 WLAN based real time indoor positioning: Literature survey and experimental investigations,” Procedia Computer Science, vol. 34. pp. 157–164, 2014.
R. Araujo, A.T. De Almeida, Learning sensor-based navigation of a real mobile robot in unknown worlds. IEEE Trans. Syst. Man, Cybern. Part B Cybern. 29(2), 164–178 (1999)
Article
Google Scholar
S. Agrawal and S. Singh, “Indoor Localization based on Bluetooth Technology : A Brief Review,” Int. J. Comput. Appl., vol. 97, no. 8, pp. 31–33, Jul. 2014.
J. Jia, G. Xu, X. Pei, R. Cao, L. Hu, and Y. Wu, “Accuracy and efficiency of an infrared based positioning and tracking system for patient set-up and monitoring in image guided radiotherapy,” Infrared Phys. Technol., vol. 69, pp. 26–31, Mar. 2015.
K. Pahlavan et al., Taking Positioning Indoors: Wi-Fi Localization and GNSS. Insid. GNSS 5(3), 40–47 (May 2010)
Google Scholar
Y. Qi, Wireless Geolocation in a Non-Line-Of-Sight Environment (Princton University, 2003)
C.-H. Ou, W.-L. He, Path Planning Algorithm for Mobile Anchor-Based Localization in Wireless Sensor Networks. IEEE Sens. J. 13(2), 466–475 (2013)
Article
Google Scholar
H. Chenji, R. Stoleru, Toward accurate mobile sensor network localization in noisy environments. IEEE Trans. Mob. Comput. 12(6), 1094–1106 (2013)
Article
Google Scholar
A. Kumar, N. Chand, V. Kumar, and V. Kumar, “Range Free Localization Schemes for Wireless Sensor Networks,” Int. J. Comput. Networks Commun., vol. 3, no. 6, pp. 115–129, 2011.
J. Vander Stoep, Design and Implementation of Reliable Localization Algorithms using Received Signal Strength (University of Washington, 2009)
P. D.R. and A. K. Jha, “Review and Analysis of Different Methodologies used in Mobile Robot Navigation,” Int. Sci. Press, vol. 4, no. 1, pp. 1–18, 2012.
C. Yang, B.-S. Chen, and F. Liao, “Mobile Location Estimation Using Fuzzy-Based IMM and Data Fusion,” IEEE Trans. Mob. Comput., vol. 9, no. 10, pp. 1424–1436, 2010.
P. Bahl and V. N. Padmanabhan, “RADAR: An in-building RF based user location and tracking system,” in Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), 2000, vol. 2, no. c, pp. 775–784.
C. Zhang and T. Herman, “Localization in Wireless Sensor Grids,” Univ. Iowa Dep. Comput. Sci. Tech. Rep. TR-01-06, vol. 1, pp. 1–17, Sep. 2013.
R. Alsaqour et al., “Dynamic packet beaconing for GPSR mobile ad hoc position-based routing protocol using fuzzy logic,” J. Netw. Comput. Appl., vol. 47, pp. 32–46, Jan. 2015.
M. Bazmara, “A Novel Fuzzy Approach for Determining Best Position of Soccer Players,” International Journal of Intelligent Systems and Applications, vol. 6, no. 9. pp. 62–67, 2014.
T.J. Ho, Robust urban wireless localization: Synergy between data fusion, modeling and intelligent estimation. IEEE Trans. Wirel. Commun. 14(2), 685–697 (2015)
Article
Google Scholar
H. W. H. Wang and C. T. G. C. T. Goh, “Fuzzy logic Kalman filter estimation for 2-wheel steerable vehicles,” in Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289), 1999, vol. 1, pp. 88–93.
N. Bouzera, M. Oussalah, N. Mezhood, A. Khireddine, Fuzzy extended Kalman filter for dynamic mobile localization in urban area using wireless network. Appl. Soft Comput. J. 57, 452–467 (2017)
Article
Google Scholar
K. Kobayashi, K. C. Cheok, K. Watanabe, and F. Munekata, “Accurate differential global positioning system via fuzzy logic Kalman filter sensor fusion technique,” IEEE Trans. Ind. Electron., vol. 45, no. 3, pp. 510–518, 1998.
B. Sen Chen, C.Y. Yang, F.K. Liao, J.F. Liao, Mobile location estimator in a rough wireless environment using extended Kalman-based IMM and data fusion. IEEE Trans. Veh. Technol. 58(3), 1157–1169 (2009)
Article
Google Scholar
D. Fox, W. Burgard, F. Dellaert, and S. Thrun, “Monte Carlo Localization: Efficient Position Estimation for Mobile Robots,” in AAAI ’99/IAAI ’99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence, 1999, pp. 343–349.
M. Kadkhoda, M. A. Totounchi, S. Member, M. H. Yaghmaee, and Z. Davarzani, “A Probabilistic Fuzzy Approach for Sensor Location Estimation in Wireless Sensor Networks,” in International Conference on Fuzzy Systems, 2010.
J. Racz, A. Dubrawski, Artificial neural network for mobile robot topological localization. Rob. Auton. Syst. 16, 73–80 (1995)
Article
Google Scholar
L. Gogolak, S. Pletl, and D. Kukolj, “Neural network-based indoor localization in WSN environments,” Acta Cybern. Acta Polytech. Hungarica, vol. 10, no. 6, pp. 221–235, 2013.
J.M. Alonso, L. Magdalena, HILK++: An interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers. Soft Comput. 15(10), 1959–1980 (2011)
Article
Google Scholar
“Cellular and LPWAN Location Technologies | ABI Research.” [Online]. Available: https://www.abiresearch.com/market-research/product/1030056-cellular-and-lpwan-location-technologies/. [Accessed: 18-Jul-2018].
R. Melamed, “Indoor Localization: Challenges and Opportunities,” in 2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft), 2016, pp. 1–2.
W. T. B. FCC, “Location-Based Services: AN OVERVIEW OF OPPORTUNITIES AND OTHER CONSIDERATIONS,” Washington, DC, US, 2012.
L.A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Article
MATH
Google Scholar
J.G. Dijkman, H. van Haeringen, S.J. de Lange, Fuzzy numbers. J. Math. Anal. Appl. 92(2), 301–341 (Apr. 1983)
Article
MathSciNet
MATH
Google Scholar
G. Bohlender, A. Kaufmann, and M. M. Gupta, “Introduction to Fuzzy Arithmetic, Theory and Applications.,” Math. Comput., vol. 47, no. 176, p. 762, Oct. 1986.
J.L. Verdegay, Progress on Fuzzy Mathematical Programming: A personal perspective. Fuzzy Sets Syst. 281, 219–226 (Dec. 2015)
Article
MathSciNet
MATH
Google Scholar
C. H. Hsieh and S. H. Chen, “Model and algorithm of fuzzy product positioning,” Information sciences, vol. 121, no. 1. pp. 61–82, 1999.
A. F. Shapiro, “Fuzzy random variables,” Insur. Math. Econ., vol. 44, no. 2, pp. 307–314, 2009.
R. E. Bellman and L. A. Zadeh, “Decision-Making in a Fuzzy Environment,” Manage. Sci., vol. 17, no. 4, pp. 141–164, 1970.
J. Alcala-Fdez and J. M. Alonso, “A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects,” IEEE Trans. Fuzzy Syst., vol. 24, no. 1, pp. 40–56, Feb. 2016.
University of Granada, “Fuzzy Software,” Systems, Soft Computing and Intelligent Information Group,. [Online]. Available: http://sci2s.ugr.es/fss.
G. Acampora, di S. Bruno, and T. Martin, “1855-2016 - IEEE Standard for Fuzzy Markup Language.” IEEE Computer Society, p. 87, 2017.
D. Herrero-Perez, H. Martinez-Barbera, Indoor Fuzzy Self-Localization using Fuzzy Segments. J. Phys. Agents 1(1), 45–54 (2007)
Google Scholar
D. Herrero and H. Mart??nez, “Fuzzy mobile-robot positioning in intelligent spaces using wireless sensor networks,” Sensors, vol. 11, no. 11. pp. 10820–10839, 2011.
D. Herrero-Pérez, J. J. Alcaraz-Jimenez, and H. Martínez-Barberá, “Mobile Robot Localization Using Fuzzy Segments,” International Journal of Advanced Robotic Systems, vol. 10, no. 406. pp. 1–16, 2013.
R.L. Haar, Sketching: Estimating object positions from relational descriptions. Comput. Graph. Image Process. 19(3), 227–247 (1982)
Article
Google Scholar
P. Buschka, A. Saffiotti, and Z. Wasik, “Fuzzy landmark-based localization for a legged robot,” in Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113), 2000, vol. 2, pp. 1205–1210.
X. Shen, J. W. Mark, and J. Ye, “Mobile location estimation in cellular networks using fuzzy logic,” in Vehicular Technology Conference, 2000. IEEE VTS-Fall VTC 2000. 52nd, 2002, vol. 5, pp. 2108–2114.
X. Shen, J. O. N. W. Mark, and J. U. N. Ye, “Mobile Location Estimation in CDMA Cellular Networks by Using Fuzzy Logic,” Wirel. Pers. Commun., vol. 22, pp. 57–70, 2002.
J. Lee, S. J. Yoo, and D. C. Lee, “Fuzzy logic adaptive mobile location estimation,” Netw. Parallel Comput. Proc., vol. 3222, pp. 626–634, 2004.
A. Teuber and B. Eissfeller, “WLAN Indoor Positioning Based on Euclidean Distances and Fuzzy Logic,” in 3rd WORKSHOP ON POSITIONING, NAVIGATION AND COMMUNICATION (WPNC’06), 2006, pp. 159–168.
C.-C. Tsai and H.-H. Lin, “Improved global localization and pose tracking of an autonomous mobile robot via fuzzy adaptive extended information filtering,” in Proceedings of the IEEE International Conference on Control Applications, 2006, pp. 1813–1818.
J. Gasós and A. Martín, “Mobile robot localization using fuzzy maps,” Fuzzy Log. Artif. Intell. Towar. Intell. Syst., vol. 1188, pp. 207–224, 1997.
G. Bey, G. Sanz, Fuzzy Logic Indoor Positioning System. Int. J. Interact. Multimed. Artif. Intell. 1(1), 49–54 (2004)
Google Scholar
M. A. Sotelo, M. Oca??a, L. M. Bergasa, R. Flores, M. Marr??n, and M. A. Garc??a, “Low level controller for a POMDP based on WiFi observations,” Rob. Auton. Syst., vol. 55, no. 2, pp. 132–145, 2007.
J. M. Alonso, M. Ocana, M. a Sotelo, L. M. Bergasa, and L. Magdalena, “Wifi localization system using fuzzy rule-based classification,” in Moreno-Díaz R., Pichler F., Quesada-Arencibia A. (eds) Computer Aided Systems Theory - EUROCAST 2009. EUROCAST 2009. Lecture Notes in Computer Science, 2009, vol. 5717, pp. 383–390.
J. M. Alonso, A. Alvarez, G. Trivino, N. Hernández, F. Herranz, and M. Ocaña, “TOWARDS PEOPLE INDOOR LOCALIZATION COMBINING WIFI AND HUMAN MOTION RECOGNITION,” in XV Spanish conference for Fuzzy Logic and Technology (ESTYLF), 2010, pp. 7–12.
S. Chiang and J. Wang, “Localization in Wireless Sensor Networks by Fuzzy Logic System,” in Velásquez J.D., Ríos S.A., Howlett R.J., Jain L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science, vol 5712, 2009, pp. 721–728.
N. Hern, F. Herranz, M. Oca, L. M. Bergasa, J. M. Alonso, and L. Magdalena, “WiFi Localization System based on Fuzzy Logic to deal with Signal Variations,” in ETFA 2009 - 2009 IEEE Conference on Emerging Technologies and Factory Automation (2009), 2009.
J.M. Alonso et al., Enhanced WiFi localization system based on Soft Computing techniques to deal with small-scale variations in wireless sensors. Appl. Soft Comput. J. 11(8), 4677–4691 (2011)
Article
Google Scholar
X. Yubin, S. Yongliang, M. Lin, A KNN-based two-step fuzzy clustering weighted algorithm for WLAN Indoor Positioning.pdf. High Technology Letters 17(3), 223–229 (2011)
Google Scholar
A. S. Salazar, L. Aguilar, and G. Licea, “Estimating indoor zone-level location using Wi-Fi RSSI fingerprinting based on fuzzy inference system,” in Proceedings - 2013 International Conference on Mechatronics, Electronics and Automotive Engineering, ICMEAE 2013, 2013, pp. 178–184.
P. Torteeka, “Indoor Positioning based on Wi-Fi Fingerprint Technique using Fuzzy K-Nearest Neighbor,” in Proceedings of 2014 11th International Bhurban Conference on Applied Sciences & Technology (IBCAST), 2014.
M. Piasecki, “Mobile robot localization by fuzzy logic fusion of multisensor data,” Rob. Auton. Syst., vol. 12, no. 3–4, pp. 155–162, Apr. 1994.
H. L. SONG, “Automatic Vehicle Location in Cellular Communications-Systems,” Ieee Trans. Veh. Technol., vol. 43, no. 4, pp. 902–908, 1994.
J. Gasós and A. Rosetti, “Uncertainty representation for mobile robots: Perception, modeling and navigation in unknown environments,” Fuzzy Sets Syst., vol. 107, no. 1, pp. 1–24, 1999.
C.-J. Lin, Y.-Y. Chen, F.-R. Chang, in Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium. Fuzzy processing on GPS data To improve The Position Accuracy (1996)
Google Scholar
a.G. Dharne and S. Jayasuriya, “Using fuzzy logic for localization in mobile sensor networks: simulations and experiments,” in 2006 American Control Conference, 2006, pp. 2066–2071.
W. Ke, Z. Zhong, H. Liang, and G. Junzhong, “M-SHOW : A SYSTEM FOR ACCURATE POSITION ESTIMATION IN MULTI-FLOOR BUILDINGS M-SHOW,” in In Proceedings ofthe International Conference on Wireless Information Networks and Systems, 2006, pp. 290–297.
A. Olowolayemo, A. O. M. Tap, and T. Mantoro, “Fuzzy logic based compensated Wi-Fi signal strength for indoor positioning,” in Proceedings - 2013 International Conference on Advanced Computer Science Applications and Technologies, ACSAT 2013, 2013, no. 1, pp. 444–449.
S. R. Swanson, “A Fuzzy Navigational State Estimator for GPS/INS Integration,” in IEEE 1998 Position Location and Navigation Symposium (Cat. No.98CH36153), 1996.
K. Kobayashi, K. C. Cheok, K. Watanabe, and F. Munekata, “Accurate global positioning via fuzzy logic Kalman filter-based sensor fusion technique,” in Proceedings of IECON ’95 - 21st Annual Conference on IEEE Industrial Electronics, 1995, vol. 2, pp. 1136–1141.
A. Rodríguez-Castaño, J. Heredia, and A. Ollero, “Fuzzy path tracking and position estimation of autonomous vehicles using differential GPS,” Mathw. Soft Comput., vol. VII, 2000.
M. Hanss, Applied Fuzzy Arithmetic An Introduction with Engineering Applications (Springer, 2005)
A. Kaur and A. Kaur, “Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System,” International Journal of Soft Computing & Engineering, vol. 2, no. 2. pp. 323–325, 2012.
D. Dubois, H. Parde, Fundamentals of Fuzzy Sets, no. February 2000 (2016)
M. Alakhras, M. Oussalah, and Mousa, “ANFIS: General description for modeling dynamic objects,” IEEE/ACS 12th Int. Conf. Comput. Syst. Appl., pp. 1–8, Nov. 2015.
J. M. Mendel and H. Wu, “Type-2 Fuzzistics for Nonsymmetric Interval Type-2 Fuzzy Sets: Forward Problems,” IEEE Trans. Fuzzy Syst., vol. 15, no. 5, pp. 916–930, Oct. 2007.
R.O. Duda, P.E. Hart, D. Strok, Pattern Classification (Wiley, 2001)
J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: The fuzzy c-means clustering algorithm,” Comput. Geosci., vol. 10, no. 2–3, pp. 191–203, 1984.
R. L. Cannon, J. V. Dave, and J. C. Bezdek, “Efficient Implementation of the Fuzzy c-Means Clustering Algorithms,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 2, pp. 248–255, Mar. 1986.
and P. N. R. J. C. Bezdek, K. James, R. Krisnapuram, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. 2005.
H. Tanaka†, T. Okuda, and K. Asai, “On Fuzzy-Mathematical Programming,” J. Cybern., vol. 3, no. 4, pp. 37–46, Jan. 1973.
M. Inuiguchi and W. A. Lodwick, “The Contributions of K . Asai and H . Tanaka in Fuzzy Optimization,” in 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013, pp. 274–279.
L. . A. Zadeh, Fuzzy sets as a basis for a theory of possibility, vol. 1, no. 1. 1978, pp. 3–28.
D. Dubois, H. PRADE, Operations on fuzzy numbers. Int. J. Syst. Sci. 9(6), 613–626 (Jun. 1978)
Article
MathSciNet
MATH
Google Scholar
M. Hanss and M. H. De, “On the Implementation of Fuzzy Arithmetical Operations for Engineering Problems,” in 8th International Conference of the North American Fuzzy Information Processing Society - NAFIPS `99, 1999, pp. 462–466.
H. Zou, X. Lu, H. Jiang, L. Xie, A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine. Sensors 15(1), 1804–1824 (2015)
Article
Google Scholar
T. Ho, “Urban Location Estimation for Mobile Cellular Networks : A Fuzzy-Tuned Hybrid Systems Approach,” IEEE Trans. Wirel. Commun., vol. 12, no. 5, pp. 2389–2399, 2013.
A. Mesmoudi, M. Feham, and N. Labraoui, “WIRELESS SENSOR NETWORKS LOCALIZATION ALGORITHMS: A COMPREHENSIVE SURVEY.,” Int. J. Comput. Networks Commun., vol. 5, no. 6, 2013.
G. Cicirelli, A. Milella, and D. Di Paola, “RFID tag localization by using adaptive neuro-fuzzy inference for mobile robot applications,” The Industrial Robot, vol. 39, no. 4. pp. 340–348, 2012.
Parvin Sam J. and P. C, “ADAPTIVE ERROR CONTROL OF FUZZY MODELING IN LIEU OF ITERATIVE LOCALIZATION FOR WIRELESS SENSOR NETWORKS,” Int. J. Res. Comput. Appl. Robot., vol. 2, no. 3, pp. 42–48, 2014.
A. Hiliuta, R. Landry, and F. Gagnon, “Fuzzy corrections in a GPS/INS hybrid navigation system,” IEEE Trans. Aerosp. Electron. Syst., vol. 40, no. 2, pp. 591–600, 2004.
A. Pérez-Neira, M. A. Lagunas, and J. Bas, “Fuzzy techniques for robust localization and tracking,” in In Proceedings of Colloque sur le traitement du signal et des images (GRETSI), 1997, pp. 933–936.
N. Baccar and R. Bouallegue, Interval type 2 fuzzy localization for wireless sensor networks, vol. 2016, no. 1. EURASIP Journal on Advances in Signal Processing, 2016, p. 42.
M. Castanon-Puga, A. S. Salazar, L. Aguilar, C. Gaxiola-Pacheco, and G. Licea, “Localization of Indoor Areas Using Wi-Fi Signals , Type-2 Fuzzy Inference Systems and Data Mining,” in Proceedings of the World Congress on Engineering WCE2014, 2014, vol. I.
F. C.-H. Rhee and C. Hwang, “An interval type-2 fuzzy K-nearest neighbor,” in The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ ’03., 2003, pp. 802–807.
M. Castañón–Puga, A. Salazar, L. Aguilar, C. Gaxiola-Pacheco, and G. Licea, “A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals,” Sensors, vol. 15, no. 12, pp. 30142–30164, 2015.
M. Kadkhoda, “Uncertainty Handling for Sensor Location Estimation in Wireless Sensor Networks using Probabilistic Fuzzy System,” Int. J. Artif. Intell. Appl. Smart Devices, vol. 1, no. 1, pp. 1–14, 2013.
E. Van Broekhoven and B. De Baets, “A comparison of three methods for computing the center of gravity defuzzification,” in 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2004, vol. 3, pp. 1537–1542.
V. Kumar, A. Kumar, and S. Soni, “A New Fuzzy Based Localization Error Minimization Approach with Optimized Beacon Range,” in 2nd International Conference and workshop on Emerging Trends in Technology (ICWET) 2011 Proceedings published by International Journal of Computer Applications (IJCA), 2011, pp. 52–59.
A. Saffiotti and L. P. Wesley, “Perception-based self-localization using fuzzy locations,” Dorst L., van Lambalgen M., Voorbraak F. (eds) Reasoning with Uncertainty in Robotics. RUR 1995. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol. 1093. Springer, Berlin, Heidelberg, pp. 368–385, 1996.
M.-Y. Y. Chen and D. A. A. Linkens, “Rule-base self-generation and simplification for data-driven fuzzy models,” Fuzzy Sets Syst., vol. 142, no. 2, pp. 243–265, 2004.
J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, 1st ed. Printice Hall, 2001.
MATH
Google Scholar
H. Ishibuchi and T. Yamamoto, “Trade-off between the Number of Fuzzy Rules and Their Classification Performance,” Casillas J., Cordón O., Herrera F., Magdal. L. Accuracy Improv. Linguist. Fuzzy Model. Stud. Fuzziness Soft Comput., vol. 129, pp. 72–99, 2003.
H. R. R. Mahdiani, A. Banaiyan, M. Haji Seyed Javadi, S. M. M. Fakhraie, and C. Lucas, “Defuzzification block: New algorithms, and efficient hardware and software implementation issues,” Eng. Appl. Artif. Intell., vol. 26, no. 1, pp. 162–172, Jan. 2013.