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. Ahuja, Anjali; Jain, Anamika; Jain, Madhu: Transient analysis and ANFIS computing of unreliable single server queueing model with multiple stage service and functioning vacation (2022)
  2. Muñoz-Vázquez, Aldo Jonathan; Treesatayapun, Chidentree: Model-free discrete-time fractional fuzzy control of robotic manipulators (2022)
  3. Gao, Siyu; Wang, Xin: Neural-network-based collaborative control for continuous unknown nonlinear systems (2021)
  4. Sarmeili, M.; Rezaei Ashtiani, H. R.; Rabiee, A. H.: Nonlinear energy sinks with nonlinear control strategies in fluid-structure simulations framework for passive and active FIV control of sprung cylinders (2021)
  5. Siminski, Krzysztof: An outlier-robust neuro-fuzzy system for classification and regression (2021)
  6. Volna, Eva; Jarusek, Robert; Kotyrba, Martin; Zacek, Jaroslav: Training set fuzzification based on histogram to increase the performance of a neural network (2021)
  7. Wang, Ning; Yao, Wen; Zhao, Yong; Chen, Xiaoqian: Bayesian calibration of computer models based on Takagi-Sugeno fuzzy models (2021)
  8. Bullock, Joseph; Luccioni, Alexandra; Pham, Katherine Hoffman; Lam, Cynthia Sin Nga; Luengo-Oroz, Miguel: Mapping the landscape of artificial intelligence applications against COVID-19 (2020)
  9. Hassanniakalager, Arman; Sermpinis, Georgios; Stasinakis, Charalampos; Verousis, Thanos: A conditional fuzzy inference approach in forecasting (2020)
  10. Pérez Pupo, Iliana; García Vacacela, Roberto; Piñero Pérez, Pedro; Mahdi, Gaafar Sadeq S.; Peña, Marieta: Experience in the use of softcomputing techniques for the evaluation of software projects (2020)
  11. Pourabdollah, Amir; Mendel, Jerry M.; John, Robert I.: Alpha-cut representation used for defuzzification in rule-based systems (2020)
  12. Sethi, R.; Jain, M.; Meena, R. K.; Garg, D.: Cost optimization and ANFIS computing of an unreliable M/M/1 queueing system with customers’ impatience under (N)-policy (2020)
  13. Tak, Nihat: Type-1 possibilistic fuzzy forecasting functions (2020)
  14. Tian, Chengshi; Hao, Yan: Point and interval forecasting for carbon price based on an improved analysis-forecast system (2020)
  15. Atsalakis, George S.; Atsalaki, Ioanna G.; Pasiouras, Fotios; Zopounidis, Constantin: Bitcoin price forecasting with neuro-fuzzy techniques (2019)
  16. Golnary, Farshad; Moradi, Hamed: Dynamic modelling and design of various robust sliding mode controls for the wind turbine with estimation of wind speed (2019)
  17. Gupta Roy, Rupam; Ghoshal, Dibyendu: Adaptive second-order sliding-mode controller for shank-foot orthosis system (2019)
  18. Jain, Madhu; Sanga, Sudeep Singh: Optimal control F-policy for m/M/R/(K) queue with an additional server and balking (2019)
  19. Ma, Xiaofeng; Aminian, Manuchehr; Kirby, Michael: Error-adaptive modeling of streaming time-series data using radial basis functions (2019)
  20. Mendel, Jerry M.: Adaptive variable-structure basis function expansions: candidates for machine learning (2019)

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