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. Upadhyay, R.; Padhy, P.K.; Kankar, P.K.: Application of S-transform for automated detection of vigilance level using EEG signals (2016)
  2. 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)
  3. Li, Jinbo; Pedrycz, Witold; Wang, Xianmin: A rule-based development of incremental models (2015)
  4. Reyes-Galaviz, Orion F.; Pedrycz, Witold: Granular fuzzy models: analysis, design, and evaluation (2015)
  5. Skorohod, B.A.: Learning algorithms for neural networks and neuro-fuzzy systems with separable structures (2015)
  6. Acampora, Giovanni; Pedrycz, Witold; Vasilakos, Athanasios V.: Efficient modeling of MIMO systems through timed automata based neuro-fuzzy inference engine (2014)
  7. Asteriadis, Stylianos; Karpouzis, Kostas; Kollias, Stefanos: Visual focus of attention in non-calibrated environments using gaze estimation (2014)
  8. Baharani, Mohammadreza; Noori, Hamid; Aliasgari, Mohammad; Navabi, Zain: High-level design space exploration of locally linear neuro-fuzzy models for embedded systems (2014)
  9. 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)
  10. 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)
  11. 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)
  12. Oliinyk, A.O.; Zayko, T.A.; Subbotin, S.O.: Synthesis of neuro-fuzzy networks on the basis of association rules (2014)
  13. Simiński, Krzysztof: Neuro-fuzzy system with weighted attributes (2014)
  14. Tomé, José Alberto; Carvalho, Joao Paulo: Fuzzy Boolean nets -- a nature inspired model for learning and reasoning (2014)
  15. Bellamine, F.H.; Almansoori, A.; Elkamel, A.: Numerical simulation of distributed dynamic systems using hybrid intelligent computing combined with generalized similarity analysis (2013)
  16. Dick, Scott; Tappenden, Andrew; Badke, Curtis; Olarewaju, Olufemi: A granular neural network: performance analysis and application to re-granulation (2013)
  17. Jiang, Xunlin; Ling, Haifeng; Yan, Jun; Li, Bo; Li, Zhao: Forecasting electrical energy consumption of equipment maintenance using neural network and particle swarm optimization (2013)
  18. Khuntia, Swasti R.; Panda, Sidhartha: ANFIS approach for SSSC controller design for the improvement of transient stability performance (2013)
  19. Liu, Peilin; Leng, Wenhao; Fang, Wei: Training ANFIS model with an improved quantum-behaved particle swarm optimization algorithm (2013)
  20. Maji, Kuntal; Pratihar, D.K.; Nath, A.K.: Analysis and synthesis of laser forming process using neural networks and neuro-fuzzy inference system (2013)

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