Robust Resilient H∞ State Estimation for Time-varying Recurrent Neural Networks Subject to Probabilistic Quantization Under Variance Constraint
This paper is concerned with the robust resilient H ∞ state estimation problem for time-varying recurrent neural networks (TVRNNs) with probabilistic quantization under variance constraint. Here, a situation is considered where the signals are quantized before entering the network, and the occurrenc...
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| Published in | International journal of control, automation, and systems Vol. 21; no. 2; pp. 684 - 695 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bucheon / Seoul
Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
01.02.2023
Springer Nature B.V 제어·로봇·시스템학회 |
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| Abstract | This paper is concerned with the robust resilient
H
∞
state estimation problem for time-varying recurrent neural networks (TVRNNs) with probabilistic quantization under variance constraint. Here, a situation is considered where the signals are quantized before entering the network, and the occurrence probability is assumed to be known. In addition, during the design of the state estimation algorithm, the additive variation of the estimator gain is considered to reflect the parameter deviation that may occur during the execution. The main purpose is to design a finite-horizon resilient state estimation algorithm such that, in the presence of probabilistic quantization and estimator gain perturbation, some sufficient criteria are obtained for the estimation error system to satisfy the prescribed
H
∞
performance requirement within the finite-horizon and the error variance boundedness. Finally, a numerical example is conducted to verify the feasibility of the presented estimation algorithm against the probabilistic quantization and estimator gain perturbation. |
|---|---|
| AbstractList | This paper is concerned with the robust resilient H∞ state estimation problem for time-varying recurrent neural networks (TVRNNs) with probabilistic quantization under variance constraint. Here, a situation is considered where the signals are quantized before entering the network, and the occurrence probability is assumed to be known. In addition, during the design of the state estimation algorithm, the additive variation of the estimator gain is considered to reflect the parameter deviation that may occur during the execution. The main purpose is to design a finite-horizon resilient state estimation algorithm such that, in the presence of probabilistic quantization and estimator gain perturbation, some sufficient criteria are obtained for the estimation error system to satisfy the prescribed H∞ performance requirement within the finite-horizon and the error variance boundedness. Finally, a numerical example is conducted to verify the feasibility of the presented estimation algorithm against the probabilistic quantization and estimator gain perturbation. KCI Citation Count: 3 This paper is concerned with the robust resilient H∞ state estimation problem for time-varying recurrent neural networks (TVRNNs) with probabilistic quantization under variance constraint. Here, a situation is considered where the signals are quantized before entering the network, and the occurrence probability is assumed to be known. In addition, during the design of the state estimation algorithm, the additive variation of the estimator gain is considered to reflect the parameter deviation that may occur during the execution. The main purpose is to design a finite-horizon resilient state estimation algorithm such that, in the presence of probabilistic quantization and estimator gain perturbation, some sufficient criteria are obtained for the estimation error system to satisfy the prescribed H∞ performance requirement within the finite-horizon and the error variance boundedness. Finally, a numerical example is conducted to verify the feasibility of the presented estimation algorithm against the probabilistic quantization and estimator gain perturbation. This paper is concerned with the robust resilient H ∞ state estimation problem for time-varying recurrent neural networks (TVRNNs) with probabilistic quantization under variance constraint. Here, a situation is considered where the signals are quantized before entering the network, and the occurrence probability is assumed to be known. In addition, during the design of the state estimation algorithm, the additive variation of the estimator gain is considered to reflect the parameter deviation that may occur during the execution. The main purpose is to design a finite-horizon resilient state estimation algorithm such that, in the presence of probabilistic quantization and estimator gain perturbation, some sufficient criteria are obtained for the estimation error system to satisfy the prescribed H ∞ performance requirement within the finite-horizon and the error variance boundedness. Finally, a numerical example is conducted to verify the feasibility of the presented estimation algorithm against the probabilistic quantization and estimator gain perturbation. |
| Author | Gao, Yan Du, Junhua Hu, Jun Yu, Hui |
| Author_xml | – sequence: 1 givenname: Yan surname: Gao fullname: Gao, Yan organization: Department of Mathematics, Harbin University of Science and Technology – sequence: 2 givenname: Jun orcidid: 0000-0002-7852-5064 surname: Hu fullname: Hu, Jun email: jhu@hrbust.edu.cn organization: School of Automation, Harbin University of Science and Technology – sequence: 3 givenname: Hui surname: Yu fullname: Yu, Hui organization: School of Automation, Harbin University of Science and Technology – sequence: 4 givenname: Junhua surname: Du fullname: Du, Junhua organization: Department of Mathematics, Harbin University of Science and Technology, College of Science, Qiqihar University |
| BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002926565$$DAccess content in National Research Foundation of Korea (NRF) |
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| Keywords | resilient state estimator time-varying recurrent neural networks performance variance constraint probabilistic quantization |
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H
∞
state estimation problem for time-varying recurrent neural networks (TVRNNs) with probabilistic... This paper is concerned with the robust resilient H∞ state estimation problem for time-varying recurrent neural networks (TVRNNs) with probabilistic... |
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| SubjectTerms | Algorithms Control Engineering H infinity Horizon Measurement Mechatronics Neural networks Perturbation Recurrent neural networks Regular Papers Robotics Robustness (mathematics) State estimation Statistical analysis Variance 제어계측공학 |
| Title | Robust Resilient H∞ State Estimation for Time-varying Recurrent Neural Networks Subject to Probabilistic Quantization Under Variance Constraint |
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