Fuzzy Logic Toolbox

Fuzzy Logic Toolbox™ provides functions, apps, and a Simulink® block for analyzing, designing, and simulating systems based on fuzzy logic. The product guides you through the steps of designing fuzzy inference systems. Functions are provided for many common methods, including fuzzy clustering and adaptive neurofuzzy learning. The toolbox lets you model complex system behaviors using simple logic rules, and then implement these rules in a fuzzy inference system. You can use it as a stand-alone fuzzy inference engine. Alternatively, you can use fuzzy inference blocks in Simulink and simulate the fuzzy systems within a comprehensive model of the entire dynamic system.

References in zbMATH (referenced in 44 articles )

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  1. Marsili-Libelli, Stefano: Environmental systems analysis with MATLAB (2016) ioport
  2. Xue, Dingyü; Chen, YangQuan: Scientific computing with MATLAB (2016)
  3. Muradova, Aliki D.; Stavroulakis, Georgios E.: Hybrid control of vibrations of a smart von Kármán plate (2015)
  4. Skorohod, B.A.: Learning algorithms for neural networks and neuro-fuzzy systems with separable structures (2015)
  5. Hiremath, P.S.; Tegnoor, Jyothi R.: Fuzzy inference system for follicle detection in ultrasound images of ovaries (2014) ioport
  6. Dixit, Arati M.; Singh, Harpreet: A soft computing approach to crack detection and impact source identification with field-programmable gate array implementation (2013) ioport
  7. Dostál, Petr: Forecasting of time series with fuzzy logic (2013)
  8. Duarte Pereira, Rúben; Sousa, João; Vieira, Susana; Reti, Shane; Finkelstein, Stan: Modified sequential forward selection applied to predicting septic shock outcome in the intensive care unit (2013) ioport
  9. Shapiro, Arnold F.: Modeling future lifetime as a fuzzy random variable (2013)
  10. Abbasbandy, S.; Hashemi, M.S.: Solving fully fuzzy linear systems by using implicit Gauss-Cholesky algorithm (2012)
  11. Abbasbandy, S.; Hashemi, M.S.: Solving fully fuzzy linear systems using implicit Gauss-Cholesky algorithm (2012)
  12. Jaradat, Mohammad Abdel Kareem; Garibeh, Mohammad H.; Feilat, Eyad A.: Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field (2012) ioport
  13. Azadeh, A.; Saberi, M.; Asadzadeh, S.M.: An adaptive network based fuzzy inference system-auto regression-analysis of variance algorithm for improvement of oil consumption estimation and policy making: the cases of Canada, united kingdom, and south Korea (2011) ioport
  14. Bosma, Roel; Kaymak, Uzay; van den Berg, Jan; Udo, Henk; Verreth, Johan: Using fuzzy logic modelling to simulate farmers’ decision-making on diversification and integration in the Mekong Delta, Vietnam (2011) ioport
  15. Baylar, A.; Batan, M.: Usage of artificial intelligence methods in free flowing gated closed conduits for estimation of oxygen transfer efficiency (2010)
  16. Dunea, Daniel; Oprea, Mihaela: A fuzzy logic based system for heavy metals loaded wastewaters monitoring (2010)
  17. Ozkan, Fahri; Kaya, Turgut: Using intelligent methods to predict air-demand ratio in venturi weirs (2010)
  18. Pérez, José Antonio; González, Manuel; Dopico, Daniel: Adaptive neurofuzzy ANFIS modeling of laser surface treatments (2010) ioport
  19. Rezaei, Jafar; Dowlatshahi, Shad: A rule-based multi-criteria approach to inventory classification (2010)
  20. Zarandi, Mohammad Hossein Fazel; Alaeddini, Adel: A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts (2010) ioport

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