A noniterative method for identification using Hammerstein model

Chang, Fu‐Min; Luus, Rein

doi:10.1109/tac.1971.1099787

Cited by 264 publications

(94 citation statements)

References 5 publications

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“…[2][3][4][5]7,9,11,19,27,29,31,37,43 Recently, Goethals et al 19 presented a novel overparameterization (two-stage procedures 17 ) identification approach for Hammerstein systems. The most distinguishing part of that approach is the utility of a powerful machine learning method, Least Square Support Vector Machine (LS-SVM).…”

Section: Introductionmentioning

confidence: 99%

Oxygen Uptake Estimation in Humans During Exercise Using a Hammerstein Model

Wang

Celler

et al. 2007

Ann Biomed Eng

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Abstract-This paper aims to establish a block-structured model to predict oxygen uptake in humans during moderate treadmill exercises. To model the steady state relationship between oxygen uptake (oxygen consumption) and walking speed, six healthy male subjects walked on a motor driven treadmill with constant speed from 2 to 7 km/h. The averaged oxygen uptake at steady state (VO 2 ) was measured by a mixing chamber based gas analysis and ventilation measurement system (AEI Moxus Metabolic Cart). Based on these reliable date, a nonlinear steady state relationship was successfully established using Support Vector Regression methods. In order to capture the dynamics of oxygen uptake, the treadmill velocity was modulated using a Pseudo Random Binary Signal (PRBS) input. Breath by breath analysis of all subjects was performed. An ARX model was developed to accurately reproduce the measured oxygen uptake dynamics within the aerobic range. Finally, a Hammerstein model was developed, which may be useful for implementing a control system for the regulation of oxygen uptake during treadmill exercises.

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Section: Introductionmentioning

confidence: 99%

Oxygen Uptake Estimation in Humans During Exercise Using a Hammerstein Model

Wang

Celler

et al. 2007

Ann Biomed Eng

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“…According to the over-parameterized linear regression approach [36], define the information vector ϕ i (k) and the parameter vector θ i as:…”

Section: The Am-sg Algorithmmentioning

confidence: 99%

“…Note that c m (m = 2, 3, · · · , n c ) has been estimated n + 1 times at each non-uniform sampling instant kT + t i (i = 0, 1, 2, · · · , q − 1). Therefore, we can simply take their average like in [36] as the estimate of c m over the k-th frame period, i.e.,…”

Section: The Am-misg Algorithmmentioning

confidence: 99%

Auxiliary Model Based Multi-Innovation Stochastic Gradient Identification Algorithm for Periodically Non-Uniformly Sampled-Data Hammerstein Systems

Xie

Yang

2017

Algorithms

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Abstract:Due to the lack of powerful model description methods, the identification of Hammerstein systems based on the non-uniform input-output dataset remains a challenging problem. This paper introduces a time-varying backward shift operator to describe periodically non-uniformly sampled-data Hammerstein systems, which can simplify the structure of the lifted models using the traditional lifting technique. Furthermore, an auxiliary model-based multi-innovation stochastic gradient algorithm is presented to estimate the parameters involved in the linear and nonlinear blocks. The simulation results confirm that the proposed algorithm is effective and can achieve a high estimation performance.

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“…These methods can be broadly classified as iterative and noniterative methods (see [4] for further classifications). The estimation problem using the noniterative method was first proposed by Chang and Luus [5] and an iterative identification method was proposed by Narendra and Gallman [1].…”

Section: Introductionmentioning

confidence: 99%

Regularized estimation of Hammerstein systems using a decomposition-based iterative instrumental variable method

Saini¹,

Dewan²

2017

Turk J Elec Eng & Comp Sci

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This paper presents a two-step instrumental variable (IV) method to obtain the regularized and consistent parameter estimates of the Hammerstein ARMAX model based on the bilinear parameterized form. The two-step identification method consists of estimating the bilinear parameters in the first step, followed by parameter reduction in the second step. An iterative identification method is proposed, based on the idea of separating the bilinear form in the two separable forms with partial parameters and solving the decomposed model forms iteratively. The IV-based estimation is integrated into the formulated decomposed structure by introducing the instruments constructed from the estimated auxiliary model outputs. It is shown that in a stochastic environment the proposed IV method produces consistent estimates in the presence of correlated noise disturbances. The validity of the proposed algorithm is verified with the help of extensive simulations using a Monte Carlo study.

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A noniterative method for identification using Hammerstein model

Cited by 264 publications

References 5 publications

Oxygen Uptake Estimation in Humans During Exercise Using a Hammerstein Model

Oxygen Uptake Estimation in Humans During Exercise Using a Hammerstein Model

Auxiliary Model Based Multi-Innovation Stochastic Gradient Identification Algorithm for Periodically Non-Uniformly Sampled-Data Hammerstein Systems

Regularized estimation of Hammerstein systems using a decomposition-based iterative instrumental variable method

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