Gradient-based iterative identification for MISO Wiener nonlinear systems: Application to a glutamate fermentation process
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The proposed method has been applied to identify the parameters of a glutamate fermentation process and the results of real data simulation show that this method is effective.About:
This article is published in Applied Mathematics Letters.The article was published on 2013-08-01 and is currently open access. It has received 27 citations till now. The article focuses on the topics: Iterative method & Nonlinear system.read more
Citations
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Hierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed models
TL;DR: This paper reframes an MIMO Hammerstein system with different types of coefficients into two models, each of which is expressed as a regression form in the parameters of the nonlinear part or in the Parameters of the linear part, and applies a hierarchical extended least squares algorithm to these two models.
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Least-Squares-Based Iterative Identification Algorithm for Wiener Nonlinear Systems
TL;DR: The least-squares-based iterative algorithm is presented by replacing the unmeasurable variables in the information vector with their corresponding iterative estimates, and the simulation results indicate that the proposed algorithm is effective.
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Gradient-based iterative identification for Wiener nonlinear systems with non-uniform sampling
TL;DR: In this article, the identification problem of Wiener nonlinear systems with non-uniform sampling is addressed by using the gradient-based iterative algorithm by replacing the unmeasurable variables with their corresponding iterative estimates.
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Robust identification of Wiener time-delay system with expectation-maximization algorithm
TL;DR: The negative effect of outliers imposed on the parameter estimation problem is sufficiently suppressed and the unknown time-delay and model parameters can be estimated simultaneously.
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Recursive least squares parameter estimation algorithm for dual-rate sampled-data nonlinear systems
TL;DR: An auxiliary model-based recursive least squares algorithm is presented by replacing the unmeasurable variables in the information vector with their corresponding recursive estimates, and results show that the proposed algorithm can estimate the parameters of a class of nonlinear systems.
References
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Identification of Hammerstein nonlinear ARMAX systems
Feng Ding,Tongwen Chen +1 more
TL;DR: Two identification algorithms are developed for Hammerstein nonlinear systems with memoryless nonlinear blocks and linear dynamical blocks described by ARMAX/CARMA models to replace unmeasurable noise terms in the information vectors by their estimates, and to compute the noise estimates based on the obtained parameter estimates.
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Hierarchical gradient-based identification of multivariable discrete-time systems
Feng Ding,Tongwen Chen +1 more
TL;DR: A hierarchical gradient iterative algorithm and a hierarchical stochastic gradient algorithm are proposed and it is proved that the parameter estimation errors given by the algorithms converge to zero for any initial values under persistent excitation.
Journal ArticleDOI
Identification methods for Hammerstein nonlinear systems
TL;DR: A Newton recursive and a Newton iterative identification algorithms are derived by using the Newton method (Newton-Raphson method) to reduce the sensitivity of the projection algorithm to noise, and to improve convergence rates of the SG algorithm.
Journal ArticleDOI
Performance analysis of multi-innovation gradient type identification methods
Feng Ding,Tongwen Chen +1 more
TL;DR: The performance analysis and simulation results show that the proposed MISG and MIFG algorithms have faster convergence rates and better tracking performance than their corresponding SG algorithms.
Journal ArticleDOI
Hierarchical least squares identification methods for multivariable systems
Feng Ding,Tongwen Chen +1 more
TL;DR: For multivariable discrete-time systems described by transfer matrices, an HLSI algorithm and a hierarchical least squares iterative algorithm based on a hierarchical identification principle are developed and shown to have significant computational advantage over existing identification algorithms.
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