Three‐stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems

Ji, Yan; Kang, Zhen

doi:10.1002/rnc.5323

Cited by 164 publications

(113 citation statements)

References 79 publications

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“…We will be able to consider transforming the database used in the experiment to the same image quality in our future work. e proposed approaches in the paper can [45][46][47][48][49][50][51][52][53] to study the image recognition and identification problems of different plants and can be applied to other studies [54][55][56][57][58][59][60][61][62] in natural sciences and social sciences.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Cross-Database Micro-Expression Recognition Exploiting Intradomain Structure

Zhang

Liu

Wang

2021

Journal of Healthcare Engineering

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Micro-expressions are unconscious, faint, short-lived expressions that appear on the faces. It can make people's understanding of psychological state and emotion more accurate. Therefore, micro-expression recognition is particularly important in psychotherapy and clinical diagnosis, which has been widely studied by researchers for the past decades. In practical applications, the micro-expression recognition samples used in training and testing are from different databases, which causes the feature distribution between the training and testing samples to be different to a large extent, resulting in a drastic decrease in the performance of the traditional micro-expression recognition methods. However, most of the existing cross-database micro-expression recognition methods require a large number of model selection or hyperparameter tuning to select better results from them, which consumes a large amount of time and labor costs. In this paper, we overcome this problem by exploiting the intradomain structure. Nonparametric transfer features are learned through intradomain alignment, while at the same time, a classifier is learned through intradomain programming. In order to evaluate the performance, a large number of cross-database experiments were conducted in CASMEII and SMIC databases. The comparison of results shows that this method can achieve a promising recognition accuracy and with high computational efficiency.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Cross-Database Micro-Expression Recognition Exploiting Intradomain Structure

Zhang

Liu

Wang

2021

Journal of Healthcare Engineering

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“…System identification is the theory and methods of establishing the mathematical models of dynamical systems 1‐4 . Almost all the practical physical systems are nonlinear because their components have different degrees of nonlinearity 5‐7 . What is often referred to as linearity in control field is merely a simplification or an approximation to nonlinearity in most cases.…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood extended gradient‐based estimation algorithms for the input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity

Liu

Fan

2021

Intl J Robust & Nonlinear

132

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Variable‐gain nonlinearity is a piecewise‐linear characteristic to describe the process with different gains in different input regions. This article studies the parameter estimation issue of the input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity. Through introducing a suitable switching function, we describe the variable‐gain nonlinearity by a linear‐in‐parameter form and derive the identification model of the system. Based on the obtained identification model, a maximum likelihood extended stochastic gradient algorithm is presented to estimate the unknown parameters. To make sufficient use of the observation data and improve the identification accuracy, we deduce a maximum likelihood (multiinnovation) extended gradient‐based iterative algorithm by using the maximum likelihood principle. An extended gradient‐based iterative algorithm is given for comparison. A simulation example is employed to validate that the proposed algorithms can effectively identify the unknown parameters and the maximum likelihood extended gradient‐based iterative algorithm has better estimation accuracy and fitting performance than the maximum likelihood extended stochastic gradient algorithm and the extended gradient‐based iterative algorithm.

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“…Therefore, considerable attention has been paid to nonlinear systems identification. On the parameter identification of nonlinear systems, various estimation approaches have been presented, 11‐15 including the parameter estimation of signal models 16‐18 . For example, Bottegal et al proposed a two‐experiment method to identify Wiener systems by using the data from two separate experiments, in which the first experiment estimates the static nonlinearity and the second experiment identifies the linear block based on the estimated nonlinearity 19 .…”

Section: Introductionmentioning

confidence: 99%

Iterative parameter and order identification for fractional‐order nonlinear finite impulse response systems using the key term separation

Wang

Zhang

2021

Adaptive Control & Signal

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118

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Summary This article considers the parameter estimation for a fractional‐order nonlinear finite impulse response system with colored noise. For the fractional‐order systems, the challenge and difficulty are to identify the order and parameters of the systems simultaneously under colored noise disturbances. In order to reduce the problem of redundant parameter estimation, the output form of the system can be expressed by a linear combination of unknown parameters through the separation of the key term separation. A key term separation auxiliary model gradient‐based iterative algorithm is derived by using the negative gradient search. Meanwhile, to achieve the higher estimation accuracy, we propose a key term separation auxiliary model multiinnovation gradient‐based iterative algorithm by utilizing the multiinnovation theory. Finally, the simulation results test the effectiveness of the proposed algorithms.

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Three‐stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems

Cited by 164 publications

References 79 publications

Cross-Database Micro-Expression Recognition Exploiting Intradomain Structure

Cross-Database Micro-Expression Recognition Exploiting Intradomain Structure

Maximum likelihood extended gradient‐based estimation algorithms for the input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity

Iterative parameter and order identification for fractional‐order nonlinear finite impulse response systems using the key term separation

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