Нейро-нечёткие системы

Нейро-нечёткие системы или Нечёткие нейронные сети — это системы из области искусственного интеллекта, были предложены Ж. С. Р. Чангом, которые комбинируют методы искусственных нейронных сетей и систем на нечёткой логике. Нейро-нечёткие системы являются результатом попытки создания гибридной интеллектуальной системы, которая бы давала синергетический эффект этих двух подходов путём комбинирования человекоподобного стиля рассуждений нечётких систем с обучением и коннекционистской структурой нейронных сетей. Основная сила нейро-нечётких систем состоит в том, что они являются универсальными аппроксиматорами со способностью запрашивать интерпретируемые правила ЕСЛИ-ТО.

Преимущество нейро-нечётких систем включает в себя две противоречивые необходимости нечёткого моделирования: интерпретируемость и точность. На практике, одно из них всегда преобладает. Нейро-нечёткие системы в исследовательской сфере нечёткого моделирования разделены на две зоны:

  • лингвистическое нечёткое моделирование, которое ориентировано на интерпретируемость, в основном, в модели Мамдани;
  • точное нечёткое моделирование, которое ориентировано на точность, в основном, в модели Такаги-Сугено-Канга (ТСК).

Литература

  • Abraham A., «Adaptation of Fuzzy Inference System Using Neural Learning, Fuzzy System Engineering: Theory and Practice», Nadia Nedjah et al. (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag Germany, ISBN 3-540-25322-X, Chapter 3, pp. 53-83, 2005. information on publisher’s site.
  • Ang, K. K., & Quek, C. (2005). «RSPOP: Rough Set-Based Pseudo Outer-Product Fuzzy Rule Identification Algorithm». Neural Computation, 17(1), 205—243.
  • Kosko, Bart (1992). Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Englewood Cliffs, NJ: Prentice Hall. ISBN 0-13-611435-0.
  • Lin, C.-T., & Lee, C. S. G. (1996). Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Upper Saddle River, NJ: Prentice Hall.
  • A. Bastian, J. Gasós (1996): «Selection of input variables for model identification of static nonlinear systems», Journal of Intelligent and Robotic Systems, Vol. 16, pp. 185—207.
  • Quek, C., & Zhou, R. W. (2001). «The POP learning algorithms: reducing work in identifying fuzzy rules.» Neural Networks, 14(10), 1431—1445.

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