Skip to main content

Fuzzy Sets, Systems, and Applications

  • Reference work entry
  • First Online:
Book cover Encyclopedia of Operations Research and Management Science

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 799.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 899.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abbod, M. F., Von Keyserlingk, D. G., Linkens, D. A., & Mahfouf, M. (2001). Survey of utilisation of fuzzy technology in medicine and health care. Fuzzy Sets and Systems, 120(3), 331–349.

    Article  Google Scholar 

  • Adamopoulos, G. I., Pappis, C. P., & Karacapilidis, N. I. (2000). A methodology for solving a range of sequencing problems with uncertain data. In R. Slowinski & M. Hapke (Eds.), Advances in scheduling and sequencing under fuzziness (pp. 147–164). Heidelberg: Physica-Verlag.

    Google Scholar 

  • Afshar, A., & Fathi, H. (2009). Fuzzy multi-objective optimization of finance-based scheduling for construction projects with uncertainties in cost. Engineering Optimization, 41(11), 1063–1080.

    Article  Google Scholar 

  • Albrecht, R. F. (2003). Interfaces between fuzzy topological interpretation of fuzzy sets and intervals. Fuzzy Sets and Systems, 135(1), 11–20.

    Article  Google Scholar 

  • Alexandridis, A., Siettos, C. I., Sarimveis, H., Boudouvis, A. G., & Bafas, G. V. (2002). Modeling of nonlinear process dynamics using Kohonen’s neural networks. Computers and Chemical Engineering, 26, 479–486.

    Article  Google Scholar 

  • Alpaydin, G., Dündar, G., & Balkir, S. (2002). Evolution-based design of neural fuzzy networks using self-adapting genetic parameters. IEEE Transactions on Fuzzy Systems, 10(2), 211–221.

    Article  Google Scholar 

  • Ammar, S., Duncombe, W., Jump, B., & Wright, R. (2004). Constructing a fuzzy-knowledge-based-system: An application for assessing the financial condition of public schools. Expert Systems with Applications, 27(3), 349–364.

    Article  Google Scholar 

  • Assilian, S., & Mamdani, E. H. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man–Machine Studies, 7(1), 1–13.

    Article  Google Scholar 

  • Bansal, R. C. (2003). Bibliography on the fuzzy set theory applications in power systems (1994–2001). IEEE Transactions on Power Systems, 18(4), 1291–1299.

    Article  Google Scholar 

  • Barro, S., & Marin, R. (2002). Fuzzy logic in medicine. Heidelberg: Physica-Verlag.

    Book  Google Scholar 

  • Belacel, N., & Boulassel, M. R. (2001). Multicriteria fuzzy assignment method: A useful tool to assist medical diagnosis. Artificial Intelligence in Medicine, 21(1–3), 201–207.

    Article  Google Scholar 

  • Biacino, L., & Gerla, G. (2002). Fuzzy logic, continuity and effectiveness. Archive for Mathematical Logic, 41, 643–667.

    Article  Google Scholar 

  • Biswas, R. (1995). An application of fuzzy sets in students' evaluation. Fuzzy Sets and Systems, 74(2), 187–194.

    Article  Google Scholar 

  • Blanco, A., Pelta, D. A., & Verdegay, J. L. (2002). Applying a fuzzy sets-based heuristic to the protein structure prediction problem. International Journal of Intelligent. Systems, 17(7), 629–643.

    Article  Google Scholar 

  • Bottani, E., & Rizzi, A. (2006). A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Management, 11(4), 294–308.

    Article  Google Scholar 

  • Boyland, M., Nelson, J., Bunnell, F., & D’ Eon, R. G. (2006). An application of fuzzy set theory for seral-class constraints in forest planning models. Forest Ecology and Management, 223(1–3), 395–402.

    Article  Google Scholar 

  • Bradshaw, C. W., Jr. (1983). A fuzzy set theoretic interpretation of economic control limits. European Journal of Operational Research, 13(4), 403–408.

    Article  Google Scholar 

  • Buckley, J. J. (1987). The fuzzy mathematics of finance. Fuzzy Sets and Systems, 21(3), 257–273.

    Article  Google Scholar 

  • Buckley, J. J. (1992). Solving fuzzy equations in economics and finance. Fuzzy Sets and Systems, 48(3), 289–296.

    Article  Google Scholar 

  • Cao, H., & Chen, G. (1983). Some applications of fuzzy sets to meteorological forecasting. Fuzzy Sets and Systems, 9(1–3), 1–12.

    Article  Google Scholar 

  • Cayrac, D., Dubois, D., & Prade, H. (1996). Handling uncertainty with possibility theory and fuzzy sets in a satellite fault diagnosis application. IEEE Transactions on Fuzzy Systems, 4(3), 251–269.

    Article  Google Scholar 

  • Chen, S. M. (1994). A weighted fuzzy reasoning algorithm for medical diagnosis. Decision Support Systems, 11(1), 37–43.

    Article  Google Scholar 

  • Chen, M., Ishii, H., & Wu, C. (2008). Transportation problems on a fuzzy network. International Journal of Innovative Computing Information and Control, 4, 1105–1109.

    Google Scholar 

  • Cheng, J. H., Chen, S. S., & Chuang, Y. W. (2008). An application of fuzzy delphi and fuzzy AHP for multi-criteria evaluation model of fourth party logistics. WSEAS Transactions on Systems, 7(5), 466–478.

    Google Scholar 

  • Cheng, Y. Y. M., & McInnis, B. (1980). Algorithm for multiple attribute, multiple alternative decision problems based on fuzzy sets with application to medical diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, SMC-10(10), 645–650.

    Google Scholar 

  • David, A. K., & Zhao, R. (1991). An expert system with fuzzy sets for optimal planning. IEEE Transactions on Power Systems, 6(2), 59–65.

    Article  Google Scholar 

  • De Moraes, R. M., Banon, G. J. F., & Sandri, S. A. (2002). Fuzzy expert systems architecture for image classification using mathematical morphology operators. The Information of the Science, 142(1/4), 7–21.

    Article  Google Scholar 

  • De, S. K., Biswas, R., & Roy, A. R. (2001). An application of intuitionistic fuzzy sets in medical diagnosis. Fuzzy Sets and Systems, 117(2), 209–213.

    Article  Google Scholar 

  • Del Amo, A., Comez, D., Montero, J., & Biging, G. (2001). Relevance and redundancy in fuzzy classification systems. Mathware and Soft Computing, VIII, 3, 203–216.

    Google Scholar 

  • Di Nola, A., Esteva, F., Garcia, P., Godo, L., & Sessa, S. (2002). Subvarieties of BL-algebras generated by single component chains. Archives for Mathematical Logic, 41, 673–685.

    Article  Google Scholar 

  • Driankov, D., Hellendoorn, H., & Reinfrank, M. (1993). An introduction to fuzzy control. Berlin: Springer.

    Book  Google Scholar 

  • Dubois, D., & Prade, H. (1980). Fuzzy sets and systems: Theory and applications. New York: Academic.

    Google Scholar 

  • Dutta, S. (1993). Fuzzy logic applications: Technological and strategic issues. IEEE Transactions on Engineering Management, 40(3), 237–254.

    Article  Google Scholar 

  • Egusa, Y., Akahori, H., Morimura, A., & Wakami, N. (1995). Application of fuzzy set theory for an electronic video camera image stabilizer. IEEE Transactions on Fuzzy Systems, 3(3), 351–356.

    Article  Google Scholar 

  • Esogbue, A. O. (1996). Fuzzy sets modeling and optimization for disaster control systems planning. Fuzzy Sets and Systems, 81(1), 169–183.

    Article  Google Scholar 

  • Esogbue, A. O., Theologidu, M., & Guo, K. (1992). On the application of fuzzy sets theory to the optimal flood control problem arising in water resources systems. Fuzzy Sets and Systems, 48(2), 155–172.

    Article  Google Scholar 

  • Gabrys, B., & Bargiela, A. (2002). General fuzzy min-max neural network for clustering and classification. IEEE Transactions on Neural Networks, 11(3), 769–783.

    Article  Google Scholar 

  • Ghomshei, M. M., & Meech, J. A. (2000). Application of fuzzy logic in environmental risk assessment: Some thoughts on fuzzy sets. Cybernetics and Systems, 31(3), 317–332.

    Article  Google Scholar 

  • Gil-Lafuente, A. M. (2005). Fuzzy logic in financial analysis. New York: Springer.

    Google Scholar 

  • Guan, X., Liu, W. H. E., & Papalexopoulos, A. D. (1995). Application of a fuzzy set method in an optimal power flow. Electric Power Systems Research, 34(1), 11–18.

    Article  Google Scholar 

  • Gutierrez, I., & Carmona, S. (1988). A fuzzy set approach to financial ratio analysis. European Journal of Operational Research, 36(1), 78–84.

    Article  Google Scholar 

  • Hanesch, M., Scholger, R., & Dekkers, M. J. (2001). The application of fuzzy C-means cluster analysis and non-linear mapping to a soil data set for the detection of polluted sites. Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, 26(11–12), 885–891.

    Article  Google Scholar 

  • Hitachi (1984) http://www.hitachi.com/rev/1999/revjun99/r3_109.pdf

  • Holmblad, L. P., & Østergaard, J.-J. (1995). The FLS application of fuzzy logic. Fuzzy Sets and Systems, 70(2–3), 135–146.

    Article  Google Scholar 

  • Hong, T. P., Lin, K. Y., & Wang, S. L. (2002). Mining linguistic browsing patterns in the world wide web. Soft Computing, 6(5), 329–336.

    Article  Google Scholar 

  • Hudson, D. L., & Cohen, M. E. (1994). Fuzzy logic in medical expert systems. IEEE Engineering in Medicine and Biology Magazine, 13(5), 693–698.

    Article  Google Scholar 

  • Hung, W. L. (2002). Partial correlation coefficients of intuitionist fuzzy sets. International Journal of Uncertainty Fuzziness Knowledge-Based Systems, 10(1), 105–112.

    Article  Google Scholar 

  • Intan, R., & Mukaidono, M. (2002). On knowledge-based fuzzy sets. International Journal of Fuzzy Systems, 4(2), 655–664.

    Google Scholar 

  • Kahraman, C. (2008). Fuzzy sets in engineering economic decision-making. Studies in Fuzziness and Soft Computing, 233, 1–9.

    Article  Google Scholar 

  • Kahraman, C., Ruan, D., & Tolga, E. (2002). Capital budgeting techniques using discounted fuzzy versus probabilistic cash flows. The Information of the Science, 142(1/4), 57–56.

    Article  Google Scholar 

  • Karacapilidis, N. I., Pappis, C. P., & Adamopoulos, G. I. (2000). Fuzzy set approaches to lot sizing. In R. Slowinski & M. Hapke (Eds.), Advances in scheduling and sequencing under fuzziness (pp. 291–304). Heidelberg: Physica-Verlag.

    Google Scholar 

  • Kardaras, D., & Karakostas, B. (1999). Use of fuzzy cognitive maps to simulate the information systems strategic planning process. Information and Software Technology, 41(4), 197–210.

    Article  Google Scholar 

  • Karr, C. L., & Gentry, E. J. (1993). Fuzzy control of pH using genetic algorithms. IEEE Transactions on Fuzzy Systems, 1, 46–53.

    Article  Google Scholar 

  • Kilic, K., Sproule, B. A., Türksen, I. B., & Naranjo, C. A. (2002). Fuzzy system modeling in pharmacology: An improved algorithm. Fuzzy Sets and Systems, 130(2), 253–264.

    Article  Google Scholar 

  • King, P. J., & Mamdani, E. H. (1977). The application of fuzzy control systems to industrial processes. Automatica, 13, 235–242.

    Article  Google Scholar 

  • Koo, J.-K., & Shin, H.-S. (1985). Application of fuzzy sets to water quality management. Water Supply, 4(1), 293–305.

    Google Scholar 

  • Kosko, B. (1992). Neural networks and fuzzy systems: A dynamical system approach. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  • Kunsch, P. L., & Fortemps, P. (2002). A Fuzzy decision support system for the economic calculus in radioactive waste management. The Information of the Science, 142, 103–116.

    Article  Google Scholar 

  • Lalla, M., Facchinetti, G., Mastroleo, G., et al. (2005). Ordinal scales and fuzzy set systems to measure agreement: An application to the evaluation of teaching activity. Quality and Quantity, 38(5), 577–601.

    Article  Google Scholar 

  • Laplante, P. A., & Sinha, D. (1996). Extensions to the fuzzy pointed set with applications to image processing. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 26(1), 21–28.

    Article  Google Scholar 

  • Laukoven, E. G., & Pasino, K. M. (1995). Training fuzzy systems to perform estimation and identification. Engineering Applications on Artificial Intelligence, 8(5), 499–514.

    Article  Google Scholar 

  • Lee, E. T. (1976). An application of fuzzy sets to the classification of geometric figures and chromosome images. Information Sciences, 10(2), 95–114.

    Article  Google Scholar 

  • Lee, M. C., & Chang, J. F. (2009). Agent and multi-agent systems: technologies and applications. Lecture Notes in Computer Science, 5559, 542–549.

    Article  Google Scholar 

  • Lee, H. M., & Yao, J. S. (1998). Economic production quantity for fuzzy demand quantity and fuzzy production quantity. European Journal of Operational Research, 109(1), 203–211.

    Article  Google Scholar 

  • Li, D., & Deogun, J. S. (2009). Applications of fuzzy and rough set theory in data mining. Studies in Computational Intelligence, 225, 71–113.

    Article  Google Scholar 

  • Liang, T. F., & Cheng, H. W. (2009). Application of fuzzy sets to manufacturing/distribution planning decisions with multi-product and multi-time period in supply chains. Expert Systems with Applications, 36, 3367–3377.

    Article  Google Scholar 

  • Lin, C., & Hsieh, P. J. (2004). A fuzzy decision support system for strategic portfolio management. Decision Support Systems, 38(3), 383–398.

    Article  Google Scholar 

  • Liou, S. M., Lo, S. L., & Hu, C. Y. (2003). Application of two-stage fuzzy set theory to river quality evaluation in Taiwan. Water Research, 37(6), 1406–1416.

    Article  Google Scholar 

  • Liu, M., Wan, C., & Wang, L. (2002). Content-based audio classification and retrieval using a fuzzy logic system: Towards multimedia search engines. Soft Computing, 6(5), 357–364.

    Article  Google Scholar 

  • Liu, H. T., & Wang, W. K. (2009). An integrated fuzzy approach for provider evaluation and selection in third-party logistics. Expert Systems with Applications, 36(3 PART 1), 4387–4398.

    Article  Google Scholar 

  • Majozi, T., & Zhu, X. X. (2005). A combined fuzzy set theory and MILP approach in integration of planning and scheduling of batch plants - personnel evaluation and allocation. Computers and Chemical Engineering, 29(9), 2029–2047.

    Article  Google Scholar 

  • Mamdani, E. H. (1977). Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers, C-26(12), 1182–1191.

    Article  Google Scholar 

  • McBratney, A. B., & Moore, A. W. (1985). Application of fuzzy sets to climatic classification. Agricultural and Forest Meteorology, 35(1–4), 165–185.

    Article  Google Scholar 

  • McBratney, A. B., & Odeh, I. O. A. (1997). Application of fuzzy sets in soil science: Fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma, 77(2–4), 85–113.

    Article  Google Scholar 

  • McIvor, R. T., McCloskey, A. G., Humphreys, P. K., & Maguire, L. P. (2004). Using a fuzzy approach to support financial analysis in the corporate acquisition process. Expert Systems with Applications, 27(4), 533–547.

    Article  Google Scholar 

  • Muthusamy, K., Sung, S. C., Vlach, M., & Ishii, H. (2003). Scheduling with fuzzy delays and fuzzy precedences. Fuzzy Sets and Systems, 134(3), 387–395.

    Article  Google Scholar 

  • Naessens, H., De Meyer, H., & De Baets, B. (2002). Algorithms for the computation of T-transitive closures. IEEE Transactions on Fuzzy Systems, 10(4), 541–551.

    Article  Google Scholar 

  • Narukawa, Y., & Torra, V. (2007). Fuzzy measures and integrals in evaluation of strategies. Information Sciences, 177(21), 4686–4695.

    Article  Google Scholar 

  • Nguyen, V. U. (1985). Some fuzzy set applications in mining geomechanics. International Journal of Rock Mechanics and Mining Sciences, 22(6), 369–379.

    Article  Google Scholar 

  • Nikravesh, M., Loia, V., & Azvine, B. (2002). Fuzzy logic and the internet (FLINT): Internet, world wide web and search engines. Soft Computing, 6(5), 287–299.

    Article  Google Scholar 

  • Nobuhara, H., Bede, B., & Hirota, K. (2006). On various eigen fuzzy sets and their application to image reconstruction. Information Sciences, 176(20), 2988–3010.

    Article  Google Scholar 

  • Novak, V. (2002). Joint consistency of fuzzy theories. Mathematical Logic Quarterly, 48, 563–573.

    Article  Google Scholar 

  • Oh, S. K., Kim, D. W., & Pedrycz, W. (2002). Hybrid fuzzy polynomial neural networks. International Journal of Uncertainty Fuzziness Knowledge-Based Systems, 10(3), 257–280.

    Article  Google Scholar 

  • Ong, S. K., & Nee, A. Y. C. (1994). Application of fuzzy set theory to setup planning. CIRP Annals - Manufacturing Technology, 43(1), 137–144.

    Article  Google Scholar 

  • Østergaard, J. J. (1977). Fuzzy logic control of a heat exchanger system. In M. M. Gupta, G. N. Saridis, & B. R. Gaines (Eds.), Fuzzy automata and decision processes (pp. 285–320). Amsterdam: North-Holland.

    Google Scholar 

  • Østergaard, J. J. (1990) Fuzzy II: The new generation of high level kiln control. Zement Kalk Gips (Cement-Lime-Gypsum), 43(11), 539–541.

    Google Scholar 

  • Pappis, C. P., & Karacapilidis, N. I. (1993). A comparative assessment of measures of similarity of fuzzy values. Fuzzy Sets and Systems, 56, 171–174.

    Article  Google Scholar 

  • Pappis, C. P., & Karacapilidis, N. I. (1995). Application of a similarity measure of fuzzy sets to fuzzy relational equations. Fuzzy Sets and Systems, 75, 35–142.

    Article  Google Scholar 

  • Pappis, C. P., & Mamdani, E. H. (1977). A fuzzy logic controller for a traffic junction. IEEE Systems Man and Cybernetics, SMC-7(10), 707–717.

    Article  Google Scholar 

  • Pappis, C. P., & Sugeno, M. (1985). Fuzzy relational equations and the inverse problem. Fuzzy Sets and Systems, 15(1), 79–90.

    Article  Google Scholar 

  • Pedrycz, W., & Gacek, A. (2002). Temporal granulation and its application to signal analysis. The Information of the Science, 143(1/4), 47–71.

    Article  Google Scholar 

  • Pedrycz, W., & Vasilakos, A. V. (2002). Modularization of fuzzy relational equations. Soft Computing, 6(1), 33–37.

    Article  Google Scholar 

  • Petrovic, D., Roy, R., & Petrovic, R. (1999). Supply chain modelling using fuzzy sets. International Journal of Production Economics, 59(1), 443–453.

    Article  Google Scholar 

  • Polat, K., Åžahan, S., & Salih, G. (2006). A new method to medical diagnosis: Artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia. Expert Systems with Applications, 31(2), 264–269.

    Article  Google Scholar 

  • Pradera, A., Trillas, E., & Calvo, T. (2002). A general class of triangular norm-based aggregation operators: Quasilinear T-S operators. International Journal of Approximate Reasoning, 30(1), 57–72.

    Article  Google Scholar 

  • Procyk, T. J., & Mamdani, E. H. (1979). A linguistic self-organizing process controller. Automatica, 15, 15–30.

    Article  Google Scholar 

  • Ramírez-Rosado, I. J., & Domínguez-Navarro, J. A. (2004). Possibilistic model based on fuzzy sets for the multiobjective optimal planning of electric power distribution networks. IEEE Transactions on Power Systems, 19(4), 1801–1810.

    Article  Google Scholar 

  • Ramkumar, V., Rajasekar, S., & Swamynathan, S. (2010). Scoring products from reviews through application of fuzzy techniques. Expert Systems with Applications, 37(10), 6862–6867.

    Article  Google Scholar 

  • Ross, T. J. (1995). Fuzzy logic with engineering applications. New York: McGraw-Hill.

    Google Scholar 

  • Ruan, D., Zhou, C., & Gupta, M. M. (2003). Fuzzy set techniques for intelligent robotic systems. Fuzzy Sets and Systems, 134(1), 1–4.

    Article  Google Scholar 

  • Sàrfi, R. J., Salama, M. M. A., & Chikhani, A. Y. (1996). Applications of fuzzy sets theory in power systems planning and operation: A critical review to assist in implementation. Electric Power Systems Research, 39(2), 89–101.

    Article  Google Scholar 

  • Setnes, M., & Kaymak, U. (2001). Fuzzy modeling of client preference from large data sets: An application to target selection in direct marketing. IEEE Transactions on Fuzzy Systems, 9(1), 153–163.

    Article  Google Scholar 

  • Sevastjanov, P. V., & Róg, P. (2003). Fuzzy modeling of manufacturing and logistic systems. Mathematics and Computers in Simulation, 63(6), 569–585.

    Article  Google Scholar 

  • Sheen, J. N. (2005). Fuzzy-financial decision-making: Load management programs case study. IEEE Transactions on Power Systems, 20(4), 1808–1817.

    Article  Google Scholar 

  • Sheu, J. B. (2004). A hybrid fuzzy-based approach for identifying global logistics strategies. Transportation Research Part E: Logistics and Transportation Review, 40(1), 39–61.

    Article  Google Scholar 

  • Shiraishi, N., Furuta, H., & Ozaki, Y. (1988). Application of fuzzy set theory to fatigue analysis of bridge structures. Information Sciences, 45(2), 175–184.

    Article  Google Scholar 

  • Siettos, C. I., Boudouvis, A. G., & Bafas, G. V. (2002). Approximation of fuzzy control systems using truncated Chebyshev series. Fuzzy Sets and Systems, Fuzzy Sets and Systems, 126, 89–104.

    Article  Google Scholar 

  • Silva, C. A., Sousa, J. M. C., & Runkler, T. A. (2007). Optimization of logistic systems using fuzzy weighted aggregation. Fuzzy Sets and Systems, 158(17), 1947–1960.

    Article  Google Scholar 

  • Smithson, M. (1982). Applications of fuzzy set concepts to behavioral sciences. Mathematical Social Sciences, 2(3), 257–274.

    Article  Google Scholar 

  • Sorenson, G. E., & Lavelle, J. P. (2008). A comparison of fuzzy set and probabilistic paradigms for ranking vague economic investment information using a present worth criterion. The Engineering Economist, 53(1), 42–67.

    Article  Google Scholar 

  • Spiegel, D., & Sudkamp, T. (2002). Employing locality in the evolutionary generation of fuzzy rule bases. IEEE Transactions on Systems Man Cybernet – Part B: Cybernetics, 32(3), 296–305.

    Article  Google Scholar 

  • Sugeno, M., & Kang, G. T. (1988). Structure identification of fuzzy model. Fuzzy Sets and Systems, 28, 15–23.

    Article  Google Scholar 

  • Sugeno, M., & Yasukawa, T. (1993). A fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems, 1(1), 7–31.

    Article  Google Scholar 

  • Szmidt, E., & Kacprzyk, J. (2004). A similarity measure for intuitionistic fuzzy sets and its application in supporting medical diagnostic reasoning. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 388–393.

    Google Scholar 

  • Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its application to modelling and control. IEEE Transactions on Systems Man Cybernetics, 15, 116–132.

    Article  Google Scholar 

  • Teodorović, D. (1994). Fuzzy sets theory applications in traffic and transportation. European Journal of Operational Research, 74(3), 379–390.

    Article  Google Scholar 

  • Togai, M., & Watanabe, H. (1986). Expert systems on a chip: An engine for real-time approximate reasoning. IEEE Expert Magazine, 1, 55–62.

    Article  Google Scholar 

  • Tong, S., Wang, T., & Li, H. X. (2002). Fuzzy robust tracking control for uncertain nonlinear systems. International Journal of Approximate Reasoning, 30, 73–90.

    Article  Google Scholar 

  • Van den Berg, J., Kaymak, U., & Van Den Bergh, W. M. (2004). Financial markets analysis by using a probabilistic fuzzy modelling approach. International Journal of Approximate Reasoning, 35(3), 291–305.

    Article  Google Scholar 

  • Wang, L. X. (1992) Fuzzy systems are universal approximators. Proceedings of IEEE International Conference on Fuzzy Systems, San Diego, 1163–1170.

    Google Scholar 

  • Wang, H. F. (2000). Fuzzy multicriteria decision making – an overview. Journal of Intelligent and Fuzzy Systems, 9(1/2), 61–84.

    Google Scholar 

  • Wang, W., De Baets, B., & Kerre, E. (1995). A comparative study of similarity measures. Fuzzy Sets and Systems, 73, 259–268.

    Article  Google Scholar 

  • Wang, J., & Lin, Y. I. (2003). A fuzzy multicriteria group decision making approach to select configuration items for software development. Fuzzy Sets and Systems, 134(3), 343–363.

    Article  Google Scholar 

  • Xu, X., Liu, X., & Yan, C. (2009). Applications of axiomatic fuzzy set clustering method on management strategic analysis. European Journal of Operational Research, 198(1), 297–304.

    Article  Google Scholar 

  • Yager, R. R. (1982). Measuring tranquility and anxiety in decision making: An application of fuzzy sets. International Journal of General Systems, 8(3), 139–146.

    Article  Google Scholar 

  • Yager, R. R. (2002a). The power average operator. IEEE Transactions on Systems Man Cybernetics-Part A: Systems Humans, 31(6), 724–730.

    Article  Google Scholar 

  • Yager, R. R. (2002b). On the valuation of alternatives for decision-making under uncertainty. International Journal of Intelligent Systems, 17(7), 687–707.

    Article  Google Scholar 

  • Yamakawa, T., & Miki, T. (1986). The current mode fuzzy logic integrated circuits fabricated by the standard CMOS process. IEEE Transactions on Computers, C-35(2), 161–167.

    Article  Google Scholar 

  • Yan, J., Ryan, M., & Power, J. (1994). Using fuzzy logic. Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Yu, L., Wang, S., & Lai, K. K. (2009). An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring. European Journal of Operational Research, 195(3), 942–959.

    Article  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Infection Control, 8, 338–353.

    Article  Google Scholar 

  • Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics, 3, 28–44.

    Article  Google Scholar 

  • Zhang, G., & Lu, J. (2007). Model and approach of fuzzy bilevel decision making for logistics planning problem. Journal of Enterprise Information Management, 20(2), 178–197.

    Article  Google Scholar 

  • Zimmerman, H. J. (1983). Using fuzzy sets in operational research. European Journal of Operational Research, 13(3), 201–216.

    Article  Google Scholar 

  • Zimmermann, H. J. (1996). Fuzzy set theory and its applications (3rd ed.). Norwell, MA: Kluwer.

    Book  Google Scholar 

  • Zimmermann, H. J. (2001). Fuzzy set theory–and its applications. Netherlands: Springer.

    Book  Google Scholar 

  • Zimmermann, H. J., Ruan, D., & Huang, C. (Eds.). (2000). Fuzzy sets and operations research for decision support: Key selected papers. Beijing: Beijing Normal University Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Costas P. Pappis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this entry

Cite this entry

Pappis, C.P., Siettos, C.I., Dasaklis, T.K. (2013). Fuzzy Sets, Systems, and Applications. In: Gass, S.I., Fu, M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1153-7_370

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-1153-7_370

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-1137-7

  • Online ISBN: 978-1-4419-1153-7

  • eBook Packages: Business and Economics

Publish with us

Policies and ethics