ANFIS

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.


References in zbMATH (referenced in 226 articles )

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  1. Chawla, Ishan; Singla, Ashish: Real-time control of a rotary inverted pendulum using robust LQR-based ANFIS controller (2018)
  2. Tsai, Shun-Hung; Chen, Yu-Wen: A novel identification method for Takagi-Sugeno fuzzy model (2018)
  3. Yazdanbakhsh, Omolbanin; Dick, Scott: A systematic review of complex fuzzy sets and logic (2018)
  4. Coufal, David: Radial fuzzy systems (2017)
  5. Mishra, Rabi Narayan; Mohanty, Kanungo Barada: Implementation of feedback-linearization-modelled induction motor drive through an adaptive simplified neuro-fuzzy approach (2017)
  6. Sanchez, Mauricio A.; Castro, Juan R.; Ocegueda-Miramontes, Violeta; Cervantes, Leticia: Hybrid learning for general type-2 TSK fuzzy logic systems (2017)
  7. Fattahi, Hadi; Karimpouli, Sadegh: Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods (2016)
  8. Goudarzi, Sobhan; Khodabakhshi, Mohammad Bagher; Moradi, Mohammad Hassan: Interactively recurrent fuzzy functions with multi objective learning and its application to chaotic time series prediction (2016)
  9. Oztekin, Asil; Kizilaslan, Recep; Freund, Steven; Iseri, Ali: A data analytic approach to forecasting daily stock returns in an emerging market (2016)
  10. Upadhyay, R.; Padhy, P. K.; Kankar, P. K.: Application of S-transform for automated detection of vigilance level using EEG signals (2016)
  11. Azar, Ahmad Taher; Hassanien, Aboul Ella: Dimensionality reduction of medical big data using neural-fuzzy classifier (2015) ioport
  12. Bayram, Duygu; Şeker, Serhat: Anfis model for vibration signals based on aging process in electric motors (2015) ioport
  13. Dragović, Ivana; Turajlić, Nina; Pilčević, Dejan; Petrović, Bratislav; Radojević, Dragan: A Boolean consistent fuzzy inference system for diagnosing diseases and its application for determining peritonitis likelihood (2015)
  14. Lala Riza; Christoph Bergmeir; Francisco Herrera; José Benítez: frbs: Fuzzy Rule-Based Systems for Classification and Regression in R (2015)
  15. Li, Jinbo; Pedrycz, Witold; Wang, Xianmin: A rule-based development of incremental models (2015)
  16. Luo, Minnan; Sun, Fuchun; Liu, Huaping: Dynamic T-S fuzzy systems identification based on sparse regularization (2015)
  17. Reyes-Galaviz, Orion F.; Pedrycz, Witold: Granular fuzzy models: analysis, design, and evaluation (2015)
  18. Simiński, Krzysztof: Rough subspace neuro-fuzzy system (2015)
  19. Skorohod, B. A.: Learning algorithms for neural networks and neuro-fuzzy systems with separable structures (2015)
  20. Acampora, Giovanni; Pedrycz, Witold; Vasilakos, Athanasios V.: Efficient modeling of MIMO systems through timed automata based neuro-fuzzy inference engine (2014)

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