ANFIS: adaptive-network-based fuzzy inference system. The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested.

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  1. Hassanniakalager, Arman; Sermpinis, Georgios; Stasinakis, Charalampos; Verousis, Thanos: A conditional fuzzy inference approach in forecasting (2020)
  2. Tak, Nihat: Type-1 possibilistic fuzzy forecasting functions (2020)
  3. Tian, Chengshi; Hao, Yan: Point and interval forecasting for carbon price based on an improved analysis-forecast system (2020)
  4. Atsalakis, George S.; Atsalaki, Ioanna G.; Pasiouras, Fotios; Zopounidis, Constantin: Bitcoin price forecasting with neuro-fuzzy techniques (2019)
  5. Golnary, Farshad; Moradi, Hamed: Dynamic modelling and design of various robust sliding mode controls for the wind turbine with estimation of wind speed (2019)
  6. Gupta Roy, Rupam; Ghoshal, Dibyendu: Adaptive second-order sliding-mode controller for shank-foot orthosis system (2019)
  7. Ma, Xiaofeng; Aminian, Manuchehr; Kirby, Michael: Error-adaptive modeling of streaming time-series data using radial basis functions (2019)
  8. Sanga, Sudeep Singh; Jain, Madhu: Cost optimization and ANFIS computing for admission control of (M/M/1/K) queue with general retrial times and discouragement (2019)
  9. Uçak, Kemal: A Runge-Kutta neural network-based control method for nonlinear MIMO systems (2019)
  10. Yazdanbakhsh, Omolbanin; Dick, Scott: FANCFIS: fast adaptive neuro-complex fuzzy inference system (2019)
  11. Chawla, Ishan; Singla, Ashish: Real-time control of a rotary inverted pendulum using robust LQR-based ANFIS controller (2018)
  12. Das, Amit Kumar; Das, Debasish; Pratihar, Dilip Kumar: Multi-objective optimization and cluster-wise regression analysis to establish input-output relationships of a process (2018)
  13. Javier Barragán, A.; Enrique, Juan M.; Calderón, Antonio J.; Andújar, José M.: Discovering the dynamic behavior of unknown systems using fuzzy logic (2018)
  14. Moradi, Milad; Chaibakhsh, Ali; Ramezani, Amin: An intelligent hybrid technique for fault detection and condition monitoring of a thermal power plant (2018)
  15. Tsai, Shun-Hung; Chen, Yu-Wen: A novel identification method for Takagi-Sugeno fuzzy model (2018)
  16. Yazdanbakhsh, Omolbanin; Dick, Scott: A systematic review of complex fuzzy sets and logic (2018)
  17. Ye, Qiang; Xia, Yi; Yao, Zhiming: Classification of gait patterns in patients with neurodegenerative disease using adaptive neuro-fuzzy inference system (2018)
  18. Coufal, David: Radial fuzzy systems (2017)
  19. Fu, Yating; Yang, Hui; Wang, Dianhui: Real-time optimal control of tracking running for high-speed electric multiple unit (2017)
  20. Jelušič, Primož; Žlender, Bojan: Discrete optimization with fuzzy constraints (2017)

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