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1.
NARX Deep Convolutional Fuzzy System for Modelling Nonlinear Dynamic Processes
Marjan Golob, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: process identification, input-output modelling, NARX model, decomposed fuzzy system, hierarchical fuzzy system, deep convolutional fuzzy system
Objavljeno v DKUM: 30.11.2023; Ogledov: 207; Prenosov: 8
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2.
Input-output modelling with decomposed neuro-fuzzy ARX model
Marjan Golob, Boris Tovornik, 2008, izvirni znanstveni članek

Opis: This paper presents a new neuro-fuzzy system based model, which is useful for the modelling of nonlinear dynamic systems. The new proposed model constitutes a soft computing method, namely, reasoning with a fuzzy inference system (FIS) and an optimisation by the neural-network learning algorithm. A structure, named the decomposed neuro-fuzzy ARX model is proposed. This structure is based on decomposition of the FIS. An evolution of a learning algorithm for the decomposed fuzzy model is suggested. A comparative study of dynamic system identification using conventional FIS models and the proposed neuro-fuzzy ARX model is presented for Box-Jenkins data set.
Ključne besede: input-output modelling, fuzzy ARX model, neuro-fuzzy system
Objavljeno v DKUM: 01.06.2012; Ogledov: 2694; Prenosov: 97
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