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 59 articles )

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  1. Şimşek, Barış; İç, Yusuf Tansel: Fuzzy failure mode and effect analysis application to reduce risk level in a ready-mixed concrete plant: a fuzzy rule based system modelling approach (2020)
  2. Razzaghnia, Tahereh: Regression parameters prediction in data set with outliers using neural network (2019)
  3. Roshani, Gholam Hossein; Karami, Alimohammad; Nazemi, Ehsan; Shama, Farzin: Volume fraction determination of the annular three-phase flow of gas-oil-water using adaptive neuro-fuzzy inference system (2018)
  4. Baoulina, Ioulia N.; Kreh, Martin; Steuding, Jörn: Deleting digits (2017)
  5. Dimirovski, Georgi M.: Learning intelligent controls in high speed networks: synergies of computational intelligence with control and Q-learning theories (2016)
  6. Marsili-Libelli, Stefano: Environmental systems analysis with MATLAB (2016) ioport
  7. Xue, Dingyü; Chen, YangQuan: Scientific computing with MATLAB (2016)
  8. Muradova, Aliki D.; Stavroulakis, Georgios E.: Hybrid control of vibrations of a smart von Kármán plate (2015)
  9. Skorohod, B. A.: Learning algorithms for neural networks and neuro-fuzzy systems with separable structures (2015)
  10. Suleman, Abdul: A convex semi-nonnegative matrix factorisation approach to fuzzy (c)-means clustering (2015)
  11. Hiremath, P. S.; Tegnoor, Jyothi R.: Fuzzy inference system for follicle detection in ultrasound images of ovaries (2014) ioport
  12. Dixit, Arati M.; Singh, Harpreet: A soft computing approach to crack detection and impact source identification with field-programmable gate array implementation (2013) ioport
  13. Dostál, Petr: Forecasting of time series with fuzzy logic (2013)
  14. 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
  15. Meshcheryakov, V. A.; Denisov, I. V.: Operation algorithm of adaptive network-based fuzzy control system for a jib crane (2013)
  16. Shapiro, Arnold F.: Modeling future lifetime as a fuzzy random variable (2013)
  17. Abbasbandy, S.; Hashemi, M. S.: Solving fully fuzzy linear systems using implicit Gauss-Cholesky algorithm (2012)
  18. Abbasbandy, S.; Hashemi, M. S.: Solving fully fuzzy linear systems by using implicit Gauss-Cholesky algorithm (2012)
  19. Jaradat, Mohammad Abdel Kareem; Garibeh, Mohammad H.; Feilat, Eyad A.: Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field (2012) ioport
  20. 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

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