Identification for Hammerstein nonlinear ARMAX systems based on multi-innovation fractional order stochastic gradient

Cheng, Songsong; Wei, Yiheng; Sheng, Dian; Chen, Yuquan; Wang, Yong

doi:10.1016/j.sigpro.2017.06.025

Cited by 79 publications

(42 citation statements)

References 27 publications

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“…Parameter identification means estimating the parameters of partially unknown systems based on noisy observations, which is the foundation of many issues such as signal processing, system identification and system control [1][2][3][4][5]. In the last few decades, the system identification theory has been well studied and many system identification methods e.g., the instrumental variable methods [6,7], the bias compensation methods [8,9], the least squares methods [10,11], the multi-innovation identification methods [12,13] have been developed to model the dynamical systems.…”

Section: Instructionmentioning

confidence: 99%

Parameter Identification of Nonlinear Systems with Time-delay Based on the Multi-innovation Stochastic Gradient Algorithm

Xu¹

2019

ujca

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This paper considers the parameter identification problem of block-oriented Hammerstein nonlinear systems with time-delay. Firstly, we adopt the data filtering technique to transform the identification model so that all the parameters will be separated in the resulting identification model which has no redundant parameters. Secondly, a multi-innovation stochastic gradient algorithm is used to estimate the system parameters. The proposed method has high computational efficiency and good accuracy. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

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Section: Instructionmentioning

confidence: 99%

Parameter Identification of Nonlinear Systems with Time-delay Based on the Multi-innovation Stochastic Gradient Algorithm

Xu¹

2019

ujca

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“…22,23 However, the stochastic gradient (SG) algorithm has a poor convergence rate because it does not make sufficient use of data. By using the multi-innovation identification theory, [24][25][26][27] the data filtering technique, and the maximum likelihood method, many efficient gradient-based methods are developed for online identification 28 and off-line identification. 29,30 For example, Liu et al presented an SG algorithm for multivariate systems and analyzed the convergence properties of the presented algorithm.…”

Section: Introductionmentioning

confidence: 99%

The innovation algorithms for multivariable state‐space models

Ding

Xiao

2019

Adaptive Control & Signal

112

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This paper derives the input-output representation of the dynamical system described by a linear multivariable state-space model and the corresponding multivariate linear regressive model (ie, multivariate equation-error model). A projection identification algorithm, a multivariate stochastic gradient identification algorithm, and a multi-innovation stochastic gradient (MISG) identification algorithm are proposed for multivariate equation-error systems by using the negative gradient search and the multi-innovation identification theory. The convergence analysis of the MISG algorithm indicates that the parameter estimation errors converge to zero under the persistent excitation condition. Finally, a numerical example illustrates the effectiveness of the proposed algorithms.

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“…They often appear in various practical applications such as viscoelastic systems, electrochemistry, economy and biology systems [1][2][3]. Due to its importance in both theoretical study and practical applications, such systems attract increasing attention, especially with respect to system identification [4,5], stability analysis [6,7], controller synthesis [8,9] and numerical computing [10,11], etc.…”

Section: Introductionmentioning

confidence: 99%

Sufficient and necessary conditions for stabilizing singular fractional order systems with partially measurable state

Wei

Wang

Liu

et al. 2019

Journal of the Franklin Institute

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This paper is concerned with the stabilization problem of singular fractional order systems with order α ∈ (0, 2). In addition to the sufficient and necessary condition for observer based control, a sufficient and necessary condition for output feedback control is proposed by adopting matrix variable decoupling technique. The developed results are more general and efficient than the existing works, especially for the output feedback case. Finally, two illustrative examples are given to verify the effectiveness and potential of the proposed approaches.

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Identification for Hammerstein nonlinear ARMAX systems based on multi-innovation fractional order stochastic gradient

Cited by 79 publications

References 27 publications

Parameter Identification of Nonlinear Systems with Time-delay Based on the Multi-innovation Stochastic Gradient Algorithm

Parameter Identification of Nonlinear Systems with Time-delay Based on the Multi-innovation Stochastic Gradient Algorithm

The innovation algorithms for multivariable state‐space models

Sufficient and necessary conditions for stabilizing singular fractional order systems with partially measurable state

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