DENFIS

DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each time moment, the output of DENFIS is calculated through a fuzzy inference system based on m-most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: (1) dynamic creation of a first-order Takagi-Sugeno-type fuzzy rule set for a DENFIS online model; and (2) creation of a first-order Takagi-Sugeno-type fuzzy rule set, or an expanded high-order one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before or during its learning process. Fuzzy rules can also be extracted during or after the learning process. An evolving clustering method (ECM), which is employed in both online and offline DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.


References in zbMATH (referenced in 58 articles )

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  1. Shahriari, Zahra; Small, Michael: Permutation entropy of state transition networks to detect synchronization (2020)
  2. Gu, Xiaowei; Angelov, Plamen P.: Self-organising fuzzy logic classifier (2018)
  3. Maciel, Leandro; Ballini, Rosangela; Gomide, Fernando: Evolving possibilistic fuzzy modelling (2017)
  4. Zhao, Wanqing; Beach, Thomas H.; Rezgui, Yacine: Efficient least angle regression for identification of linear-in-the-parameters models (2017)
  5. Abiyev, Rahib H.; Abizade, Sanan: Diagnosing Parkinson’s diseases using fuzzy neural system (2016)
  6. Al-Hmouz, Rami; Pedrycz, Witold; Balamash, Abdullah; Morfeq, Ali: Description and classification of granular time series (2015) ioport
  7. Inacio, Maurilio; Lemos, Andre; Caminhas, Walmir: Fault diagnosis with evolving fuzzy classifier based on clustering algorithm and drift detection (2015)
  8. Lala Riza; Christoph Bergmeir; Francisco Herrera; José Benítez: frbs: Fuzzy Rule-Based Systems for Classification and Regression in R (2015) not zbMATH
  9. Leite, Daniel; Gomide, Fernando: Incremental granular fuzzy modeling using imprecise data streams (2015)
  10. Liparulo, Luca; Proietti, Andrea; Panella, Massimo: Fuzzy clustering using the convex hull as geometrical model (2015)
  11. Reyes-Galaviz, Orion F.; Pedrycz, Witold: Granular fuzzy models: analysis, design, and evaluation (2015)
  12. Abiyev, Rahib H.: Credit rating using type-2 fuzzy neural networks (2014)
  13. Baharani, Mohammadreza; Noori, Hamid; Aliasgari, Mohammad; Navabi, Zain: High-level design space exploration of locally linear neuro-fuzzy models for embedded systems (2014) ioport
  14. Chairez, I.: Multiple DNN identifier for uncertain nonlinear systems based on Takagi-Sugeno inference (2014)
  15. Chen, Cheng-Hung; Liao, Yen-Yun: Tribal particle swarm optimization for neurofuzzy inference systems and its prediction applications (2014)
  16. Cui, Wen-Hua; Wang, Jie-Sheng; Ning, Chen-Xu: Time series prediction method of bank cash flow and simulation comparison (2014)
  17. Pedrycz, Witold; Lu, Wei; Liu, Xiaodong; Wang, Wei; Wang, Lizhong: Human-centric analysis and interpretation of time series: a perspective of granular computing (2014) ioport
  18. Razi, Farshad Faezy: A hybrid grey relational analysis and nondominated sorting genetic algorithm-II for project portfolio selection (2014) ioport
  19. Hametner, Christoph; Jakubek, Stefan: Local model network identification for online engine modelling (2013) ioport
  20. Kasabov, Nikola; Dhoble, Kshitij; Nuntalid, Nuttapod; Indiveri, Giacomo: Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition (2013) ioport

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