HyFIS

HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.


References in zbMATH (referenced in 12 articles )

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  1. Lala Riza; Christoph Bergmeir; Francisco Herrera; José Benítez: frbs: Fuzzy Rule-Based Systems for Classification and Regression in R (2015) not zbMATH
  2. Chen, Yu-Wang; Yang, Jian-Bo; Xu, Dong-Ling; Yang, Shan-Lin: On the inference and approximation properties of belief rule based systems (2013)
  3. Li, Chunshien; Chiang, Tai-Wei: Intelligent financial time series forecasting: a complex neuro-fuzzy approach with multi-swarm intelligence (2012)
  4. Oysal, Yusuf; Yilmaz, Sevcan: An adaptive wavelet network for function learning (2010) ioport
  5. Deng, Xingsheng; Wang, Xinzhou: Incremental learning of dynamic fuzzy neural networks for accurate system modeling (2009)
  6. Zhang, Yong; Zhang, Shen-Sheng; Han, Song-Qiao: Adaptive service configuration approach for quality of service management in ubiquitous computing environments (2009)
  7. Aznarte M., José Luis; Benítez, José Manuel; Castro, Juan Luis: Smooth transition autoregressive models and fuzzy rule-based systems: Functional equivalence and consequences (2007)
  8. Kothamasu, Ranganath; Huang, Samuel H.: Adaptive Mamdani fuzzy model for condition-based maintenance (2007) ioport
  9. Song, Qun; Kasabov, Nikola: TWNFI--a transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling (2006)
  10. Chen, Yuehui; Yang, Bo; Dong, Jiwen; Abraham, Ajith: Time-series forecasting using flexible neural tree model (2005) ioport
  11. Karatepe, Engin; Alcı, Musa: A new approach to fuzzy wavelet system modeling (2005) ioport
  12. Huang, Samuel H.; Xing, Hao: Extract intelligible and concise fuzzy rules from neural networks (2002)