| Title: | NARX Deep Convolutional Fuzzy System for Modelling Nonlinear Dynamic Processes |
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| Authors: | ID Golob, Marjan (Author) |
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| Files: |  mathematics-11-00304.pdf (5,86 MB)
MD5: 1E7256A0A89A93C472B004DB92DD3D03
 https://www.mdpi.com/2227-7390/11/2/304
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | 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. |
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| Keywords: | process identification, input-output modelling, NARX model, decomposed fuzzy system, hierarchical fuzzy system, deep convolutional fuzzy system |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 07.12.2022 |
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| Article acceptance date: | 28.12.2022 |
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| Publication date: | 06.01.2023 |
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| Publisher: | MDPI |
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| Year of publishing: | 2023 |
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| Number of pages: | 22 |
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| Numbering: | Vol. 11, no. 2 |
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| PID: | 20.500.12556/DKUM-86409  |
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| UDC: | 681.5 |
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| ISSN on article: | 2227-7390 |
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| eISSN: | 2227-7390 |
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| COBISS.SI-ID: | 136924931 |
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| DOI: | 10.3390/math11020304 |
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| Copyright: | © 2023 by the author |
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| Publication date in DKUM: | 30.11.2023 |
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| Views: | 422 |
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| Downloads: | 17 |
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| Metadata: |    |
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| Categories: | Misc.
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