期刊
ISA TRANSACTIONS
卷 96, 期 -, 页码 177-184出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2019.06.015
关键词
Hammerstein model; State estimation; Fractional-order systems; Neural networks
This paper introduces a continuous-time fractional-order Hammerstein state-space model with a systematic identification algorithm for modeling nonlinear dynamic systems. The proposed model consists of a radial-basis function neural network followed by a fractional-order system. The proposed identification scheme is accomplished in two stages. The structural parameters of the fractional-order system (i.e. the values of the fractional order and the degree of the denominator in the fractional order system) are estimated in the frequency domain. Then, the synaptic weights of the radial-basis function neural network and the coefficients of the fractional-order system are determined in the time domain via the Lyapunov stability theory, which guarantees stability of the given method and its convergence under a mild condition. Three examples are provided to show the effectiveness of the proposed method. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.
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