Neuro-fuzzy control based on the NEFCON-model: recent developments

@article{Nrnberger1998NeurofuzzyCB,
  title={Neuro-fuzzy control based on the NEFCON-model: recent developments},
  author={Andreas N{\"u}rnberger and Detlef D. Nauck and Rudolf Kruse},
  journal={Soft Comput.},
  year={1998},
  volume={2},
  pages={168-182}
}
Fuzzy systems are currently being used in a wide field of industrial and scientific applications. Since the design and especially the optimization process of fuzzy systems can be very time consuming, it is convenient to have algorithms which construct and optimize them automatically. One popular approach is to combine fuzzy systems with learning techniques derived from neural networks. Such approaches are usually called neuro-fuzzy systems. In this paper we present our view of neuro-fuzzy… CONTINUE READING
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An Introduction to Neural Computing

  • I Aleksander, H Morton
  • 1990
Highly Influential
5 Excerpts

Neuro-fuzzy control based on the NEFCON model under MATLAB/SIMULINK

  • A Nürnberger, D Nauck, R Kruse
  • 1997
1 Excerpt

Designing neuro-fuzzy systems through backpropagation, In: Pedrycz W (Ed) Fuzzy Modelling: Paradigms and Practice, Boston: Kluwar

  • D Nauck, R Kruse
  • 1996
3 Excerpts

Neuro-fuzzy systems research and applications outside of Japan (in Japanese), In: Umano M, Hayashi I, Furuhashi T (Eds) Fuzzy-Neural Networks (in Japanese), Soft Computing

  • D Nauck, R Kruse
  • 1996
1 Excerpt

Rule Net — A new knowledge-based artificial neural network model with application examples in robotics, PhD thesis, ETH Zürich

  • N Tschichold-Gürman
  • 1996
1 Excerpt

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