20.500.12556/DKUM-86409 NARX Deep Convolutional Fuzzy System for Modelling Nonlinear Dynamic Processes This paper presents a new approach for modelling nonlinear dynamic processes (NDP). It is based on a nonlinear autoregressive with exogenous (NARX) inputs model structure and a deep convolutional fuzzy system (DCFS). The DCFS is a hierarchical fuzzy structure, which can overcome the deficiency of general fuzzy systems when facing high dimensional data. For relieving the curse of dimensionality, as well as improving approximation performance of fuzzy models, we propose combining the NARX with the DCFS to provide a good approximation of the complex nonlinear dynamic behavior and a fast-training algorithm with ensured convergence. There are three NARX DCFS structures proposed, and the appropriate training algorithm is adapted. Evaluations were performed on a popular benchmark—Box and Jenkin’s gas furnace data set and the four nonlinear dynamic test systems. The experiments show that the proposed NARX DCFS method can be successfully used to identify nonlinear dynamic systems based on external dynamics structures and nonlinear static approximators. process identification input-output modelling NARX model decomposed fuzzy system hierarchical fuzzy system deep convolutional fuzzy system identifikacijski procesi modeliranje mehki sistemi true true true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/ © 2023 by the author Članek v reviji 2023-11-28 11:28:28 2023-11-30 11:35:50 2024-05-23 07:50:17 0000-00-00 00:00:00 2023 0 0 22 no. 2 Vol. 11 2023 0000-00-00 Zaloznikova Objavljeno apc 2022-12-07 2022-12-28 2023-01-06 681.5 2227-7390 136924931 10.3390/math11020304 2227-7390 mathematics-11-00304.pdf mathematics-11-00304.pdf 1 1E7256A0A89A93C472B004DB92DD3D03 a45fe0f436d4f3736994726a14cadd095bbfcead23db1288d99a3eec5a0e17dd f1db6f36-8dd8-11ee-b06a-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=174376 https://www.mdpi.com/2227-7390/11/2/304 1 25f42e94-8ddb-11ee-b06a-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=174377 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0