Probability‐guaranteed encoding–decoding‐based state estimation for delayed memristive neutral networks with event‐triggered mechanism
Summary This article handles the probability‐guaranteed state estimation problem for a class of nonlinear memristive neural networks (MNNs) by using an event‐triggered mechanism. Both time‐varying delays and incomplete measurements are considered in the MNNs dynamics. To mitigate the impact of limit...
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| Veröffentlicht in: | International journal of adaptive control and signal processing Jg. 38; H. 8; S. 2750 - 2770 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Bognor Regis
Wiley Subscription Services, Inc
01.08.2024
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| ISSN: | 0890-6327, 1099-1115 |
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| Abstract | Summary
This article handles the probability‐guaranteed state estimation problem for a class of nonlinear memristive neural networks (MNNs) by using an event‐triggered mechanism. Both time‐varying delays and incomplete measurements are considered in the MNNs dynamics. To mitigate the impact of limited communication bandwidth, a communication protocol is proposed that incorporates an encoding–decoding technique in addition to an event‐triggered scheme. The aim is to devise a state estimator that can estimate the states of MNNs, ensuring that the state estimation error falls within the required ellipsoidal area with a desired chance. We obtain sufficient conditions for the feasibility of the addressed problem, where the requested gains can be found iteratively by solving certain convex optimization problems. On the basis of the proposed framework, some issues are further presented to determine locally optimal estimator parameters according to different specifications. Finally, we utilize an illustrative numerical example to show the validity of our provided theoretical results. |
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| AbstractList | Summary
This article handles the probability‐guaranteed state estimation problem for a class of nonlinear memristive neural networks (MNNs) by using an event‐triggered mechanism. Both time‐varying delays and incomplete measurements are considered in the MNNs dynamics. To mitigate the impact of limited communication bandwidth, a communication protocol is proposed that incorporates an encoding–decoding technique in addition to an event‐triggered scheme. The aim is to devise a state estimator that can estimate the states of MNNs, ensuring that the state estimation error falls within the required ellipsoidal area with a desired chance. We obtain sufficient conditions for the feasibility of the addressed problem, where the requested gains can be found iteratively by solving certain convex optimization problems. On the basis of the proposed framework, some issues are further presented to determine locally optimal estimator parameters according to different specifications. Finally, we utilize an illustrative numerical example to show the validity of our provided theoretical results. This article handles the probability‐guaranteed state estimation problem for a class of nonlinear memristive neural networks (MNNs) by using an event‐triggered mechanism. Both time‐varying delays and incomplete measurements are considered in the MNNs dynamics. To mitigate the impact of limited communication bandwidth, a communication protocol is proposed that incorporates an encoding–decoding technique in addition to an event‐triggered scheme. The aim is to devise a state estimator that can estimate the states of MNNs, ensuring that the state estimation error falls within the required ellipsoidal area with a desired chance. We obtain sufficient conditions for the feasibility of the addressed problem, where the requested gains can be found iteratively by solving certain convex optimization problems. On the basis of the proposed framework, some issues are further presented to determine locally optimal estimator parameters according to different specifications. Finally, we utilize an illustrative numerical example to show the validity of our provided theoretical results. |
| Author | Zhao, Hongyuan Guo, Jian Hu, Chen Zhang, Shuhua Ma, Lifeng |
| Author_xml | – sequence: 1 givenname: Chen surname: Hu fullname: Hu, Chen organization: Nanjing University of Science and Technology – sequence: 2 givenname: Shuhua surname: Zhang fullname: Zhang, Shuhua organization: Nanjing University of Science and Technology – sequence: 3 givenname: Hongyuan surname: Zhao fullname: Zhao, Hongyuan organization: Nanjing University of Science and Technology – sequence: 4 givenname: Lifeng orcidid: 0000-0002-1839-6803 surname: Ma fullname: Ma, Lifeng email: malifeng@njust.edu.cn organization: Nanjing University of Science and Technology – sequence: 5 givenname: Jian surname: Guo fullname: Guo, Jian organization: Nanjing University of Science and Technology |
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| References | 2021; 8 2018; 29 2021; 21 2023; 34 2021; 66 2012 2023; 37 2021; 129 2019; 346 2022; 69 2020; 14 1994 2017; 230 2019; 107 2012; 57 2021; 52 2022; 135 2021; 13 2021; 10 2021; 32 2018; 5 2020; 31 2021; 134 2017; 14 1971; 18 2021; 455 2020; 93 2022; 8 2015; 296 2022; 34 2019 2016; 84 2022; 52 2022; 53 2018; 94 2022; 1 2022; 10 2021; 81 2016; 45 e_1_2_8_29_1 e_1_2_8_24_1 e_1_2_8_47_1 e_1_2_8_25_1 e_1_2_8_46_1 e_1_2_8_26_1 e_1_2_8_27_1 e_1_2_8_48_1 Zhao B (e_1_2_8_41_1) 2022; 69 e_1_2_8_3_1 e_1_2_8_2_1 e_1_2_8_5_1 e_1_2_8_4_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_43_1 e_1_2_8_21_1 e_1_2_8_42_1 e_1_2_8_22_1 e_1_2_8_45_1 e_1_2_8_23_1 e_1_2_8_44_1 e_1_2_8_40_1 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_19_1 Wang X (e_1_2_8_28_1) 2022; 1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_16_1 e_1_2_8_37_1 Zhang Q (e_1_2_8_20_1) 2022; 1 e_1_2_8_32_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_30_1 |
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This article handles the probability‐guaranteed state estimation problem for a class of nonlinear memristive neural networks (MNNs) by using an... This article handles the probability‐guaranteed state estimation problem for a class of nonlinear memristive neural networks (MNNs) by using an event‐triggered... |
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| SubjectTerms | Coding Convexity encoding–decoding mechanism event‐triggered technique memristive neural networks Neural networks Parameter estimation probability‐guaranteed estimation State estimation |
| Title | Probability‐guaranteed encoding–decoding‐based state estimation for delayed memristive neutral networks with event‐triggered mechanism |
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