Abstract
This paper explores a new approach for the modelling and identification of non-linear dynamic systems. A model, named the Decomposed Neuro- Fuzzy Auto-Regressive with eXogenous input model (DNFARX), based on decomposed structure of the fuzzy inference system, is proposed. An evolution of a neural network learning algorithm for the decomposed structure of the fuzzy inference system is suggested. A comparative study of the dynamic system modelling with conventional fuzzy inference system based models and the proposed model is presented for Box-Jenkins data set.
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Golob, M., Tovornik, B. (2002). Decomposed Neuro-fuzzy ARX Model. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_35
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DOI: https://doi.org/10.1007/3-540-45631-7_35
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