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 208 articles )

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  1. 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)
  2. Oztekin, Asil; Kizilaslan, Recep; Freund, Steven; Iseri, Ali: A data analytic approach to forecasting daily stock returns in an emerging market (2016)
  3. Upadhyay, R.; Padhy, P.K.; Kankar, P.K.: Application of S-transform for automated detection of vigilance level using EEG signals (2016)
  4. Azar, Ahmad Taher; Hassanien, Aboul Ella: Dimensionality reduction of medical big data using neural-fuzzy classifier (2015) ioport
  5. Bayram, Duygu; Şeker, Serhat: Anfis model for vibration signals based on aging process in electric motors (2015) ioport
  6. 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)
  7. Li, Jinbo; Pedrycz, Witold; Wang, Xianmin: A rule-based development of incremental models (2015)
  8. Reyes-Galaviz, Orion F.; Pedrycz, Witold: Granular fuzzy models: analysis, design, and evaluation (2015)
  9. Skorohod, B.A.: Learning algorithms for neural networks and neuro-fuzzy systems with separable structures (2015)
  10. Acampora, Giovanni; Pedrycz, Witold; Vasilakos, Athanasios V.: Efficient modeling of MIMO systems through timed automata based neuro-fuzzy inference engine (2014)
  11. Asteriadis, Stylianos; Karpouzis, Kostas; Kollias, Stefanos: Visual focus of attention in non-calibrated environments using gaze estimation (2014) ioport
  12. Baharani, Mohammadreza; Noori, Hamid; Aliasgari, Mohammad; Navabi, Zain: High-level design space exploration of locally linear neuro-fuzzy models for embedded systems (2014) ioport
  13. Castillo, Oscar; Castro, Juan R.; Melin, Patricia; Rodriguez-Diaz, Antonio: Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction (2014) ioport
  14. Kuo, R.J.; Hung, S.Y.; Cheng, W.C.: Application of an optimization artificial immune network and particle swarm optimization-based fuzzy neural network to an RFID-based positioning system (2014) ioport
  15. Oh, Sung-Kwun; Kim, Wook-Dong; Pedrycz, Witold; Seo, Kisung: Fuzzy radial basis function neural networks with information granulation and its parallel genetic optimization (2014)
  16. Oliinyk, A.O.; Zayko, T.A.; Subbotin, S.O.: Synthesis of neuro-fuzzy networks on the basis of association rules (2014)
  17. Rostam Niakan Kalhori, M.; Fazel Zarandi, M.H.; Turksen, I.B.: A new credibilistic clustering algorithm (2014)
  18. Simiński, Krzysztof: Neuro-fuzzy system with weighted attributes (2014) ioport
  19. Tomé, José Alberto; Carvalho, Joao Paulo: Fuzzy Boolean nets -- a nature inspired model for learning and reasoning (2014)
  20. Bellamine, F.H.; Almansoori, A.; Elkamel, A.: Numerical simulation of distributed dynamic systems using hybrid intelligent computing combined with generalized similarity analysis (2013)

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