Partial-neurons-based state estimation for artificial neural networks under constrained bit rate: The finite-time case
This paper is concerned with the partial-neuron-based finite-time state estimation problem for a class of artificial neural networks with time-varying delays. Measurements information from only a small fractional of the artificial neurons are applied to the state estimation process. The data transmi...
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| Vydané v: | Neurocomputing (Amsterdam) Ročník 488; s. 144 - 153 |
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| Hlavní autori: | , , , , |
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| Jazyk: | English |
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Elsevier B.V
01.06.2022
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | This paper is concerned with the partial-neuron-based finite-time state estimation problem for a class of artificial neural networks with time-varying delays. Measurements information from only a small fractional of the artificial neurons are applied to the state estimation process. The data transmission from the sensor to estimator is implemented via a bit-rate constrained communication channel, and a data encoding–decoding scheme is developed to convert the original analog sensor measurements into certain digital codewords with fewer occupations of the network bandwidth. With the help of the Lyapunov stability theory, sufficient conditions are presented to guarantee the finite-time boundedness of the estimation error and the estimator gain matrix is parameterized in terms of the solution to certain matrix inequalities. Finally, a numerical example is provided to further confirm the effectiveness of the proposed state estimation scheme. |
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| AbstractList | This paper is concerned with the partial-neuron-based finite-time state estimation problem for a class of artificial neural networks with time-varying delays. Measurements information from only a small fractional of the artificial neurons are applied to the state estimation process. The data transmission from the sensor to estimator is implemented via a bit-rate constrained communication channel, and a data encoding–decoding scheme is developed to convert the original analog sensor measurements into certain digital codewords with fewer occupations of the network bandwidth. With the help of the Lyapunov stability theory, sufficient conditions are presented to guarantee the finite-time boundedness of the estimation error and the estimator gain matrix is parameterized in terms of the solution to certain matrix inequalities. Finally, a numerical example is provided to further confirm the effectiveness of the proposed state estimation scheme. |
| Author | Liu, Hongjian Ding, Derui Zhao, Di Wang, Licheng Wang, Yu-Ang |
| Author_xml | – sequence: 1 givenname: Licheng surname: Wang fullname: Wang, Licheng organization: Department of Control Science and Engineering, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China – sequence: 2 givenname: Di surname: Zhao fullname: Zhao, Di organization: College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China – sequence: 3 givenname: Yu-Ang surname: Wang fullname: Wang, Yu-Ang organization: College of Information Science and Technology, Donghua University, Shanghai 201620, China – sequence: 4 givenname: Derui surname: Ding fullname: Ding, Derui organization: Department of Control Science and Engineering, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China – sequence: 5 givenname: Hongjian surname: Liu fullname: Liu, Hongjian organization: The Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, China |
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| Keywords | Encoding–decoding mechanism Partial-nodes-based state estimation Finite-time state estimation Bit-rate constraints Artificial neural networks |
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| Title | Partial-neurons-based state estimation for artificial neural networks under constrained bit rate: The finite-time case |
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