Joint State and Unknown Input Estimation for a Class of Artificial Neural Networks With Sensor Resolution: An Encoding-Decoding Mechanism
This article is concerned with the joint state and unknown input (SUI) estimation for a class of artificial neural networks (ANNs) with sensor resolution (SR) under the encoding-decoding mechanisms. The consideration of SR, which is an important specification of sensors in the real world, caters to...
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| Vydané v: | IEEE transaction on neural networks and learning systems Ročník 36; číslo 2; s. 3671 - 3681 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
IEEE
01.02.2025
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| ISSN: | 2162-237X, 2162-2388, 2162-2388 |
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| Abstract | This article is concerned with the joint state and unknown input (SUI) estimation for a class of artificial neural networks (ANNs) with sensor resolution (SR) under the encoding-decoding mechanisms. The consideration of SR, which is an important specification of sensors in the real world, caters to engineering practice. Furthermore, the implementation of the encoding-decoding mechanism in the communication network aims to accommodate the limited bandwidth. The objective of this study is to propose a set-membership estimation algorithm that accurately estimates the state of the ANN without being influenced by the unknown input while accounting for the SR and the encoding-decoding mechanism. First, a sufficient condition is derived to ensure an ellipsoidal constraint on the estimation error. Then, by addressing an optimization problem, the design of the estimator gains is accomplished, and the minimal ellipsoidal constraint on the state estimation error is obtained. Finally, an example is provided to confirm the validity of the proposed joint SUI estimation scheme. |
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| AbstractList | This article is concerned with the joint state and unknown input (SUI) estimation for a class of artificial neural networks (ANNs) with sensor resolution (SR) under the encoding-decoding mechanisms. The consideration of SR, which is an important specification of sensors in the real world, caters to engineering practice. Furthermore, the implementation of the encoding-decoding mechanism in the communication network aims to accommodate the limited bandwidth. The objective of this study is to propose a set-membership estimation algorithm that accurately estimates the state of the ANN without being influenced by the unknown input while accounting for the SR and the encoding-decoding mechanism. First, a sufficient condition is derived to ensure an ellipsoidal constraint on the estimation error. Then, by addressing an optimization problem, the design of the estimator gains is accomplished, and the minimal ellipsoidal constraint on the state estimation error is obtained. Finally, an example is provided to confirm the validity of the proposed joint SUI estimation scheme.This article is concerned with the joint state and unknown input (SUI) estimation for a class of artificial neural networks (ANNs) with sensor resolution (SR) under the encoding-decoding mechanisms. The consideration of SR, which is an important specification of sensors in the real world, caters to engineering practice. Furthermore, the implementation of the encoding-decoding mechanism in the communication network aims to accommodate the limited bandwidth. The objective of this study is to propose a set-membership estimation algorithm that accurately estimates the state of the ANN without being influenced by the unknown input while accounting for the SR and the encoding-decoding mechanism. First, a sufficient condition is derived to ensure an ellipsoidal constraint on the estimation error. Then, by addressing an optimization problem, the design of the estimator gains is accomplished, and the minimal ellipsoidal constraint on the state estimation error is obtained. Finally, an example is provided to confirm the validity of the proposed joint SUI estimation scheme. This article is concerned with the joint state and unknown input (SUI) estimation for a class of artificial neural networks (ANNs) with sensor resolution (SR) under the encoding-decoding mechanisms. The consideration of SR, which is an important specification of sensors in the real world, caters to engineering practice. Furthermore, the implementation of the encoding-decoding mechanism in the communication network aims to accommodate the limited bandwidth. The objective of this study is to propose a set-membership estimation algorithm that accurately estimates the state of the ANN without being influenced by the unknown input while accounting for the SR and the encoding-decoding mechanism. First, a sufficient condition is derived to ensure an ellipsoidal constraint on the estimation error. Then, by addressing an optimization problem, the design of the estimator gains is accomplished, and the minimal ellipsoidal constraint on the state estimation error is obtained. Finally, an example is provided to confirm the validity of the proposed joint SUI estimation scheme. |
| Author | Liu, Hongjian Dong, Hongli Shen, Yuxuan Wang, Zidong Liu, Xiaohui |
| Author_xml | – sequence: 1 givenname: Yuxuan orcidid: 0000-0003-4870-9038 surname: Shen fullname: Shen, Yuxuan email: shenyuxuan5973@163.com organization: Artificial Intelligence Energy Research Institute, Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, National Key Laboratory of Continental Shale Oil of China, Northeast Petroleum University, Daqing, China – sequence: 2 givenname: Zidong orcidid: 0000-0002-9576-7401 surname: Wang fullname: Wang, Zidong email: Zidong.Wang@brunel.ac.uk organization: Department of Computer Science, Brunel University London, Uxbridge, Middlesex, U.K – sequence: 3 givenname: Hongli orcidid: 0000-0001-8531-6757 surname: Dong fullname: Dong, Hongli email: shiningdhl@vip.126.com organization: Artificial Intelligence Energy Research Institute, Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, National Key Laboratory of Continental Shale Oil of China, Northeast Petroleum University, Daqing, China – sequence: 4 givenname: Hongjian orcidid: 0000-0001-6471-5089 surname: Liu fullname: Liu, Hongjian email: hjliu1980@gmail.com organization: Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Ministry of Education, and the School of Mathematics and Physics, Anhui Polytechnic University, Wuhu, China – sequence: 5 givenname: Xiaohui orcidid: 0000-0003-1589-1267 surname: Liu fullname: Liu, Xiaohui organization: Department of Computer Science, Brunel University London, Uxbridge, Middlesex, U.K |
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| SubjectTerms | Artificial neural networks Artificial neural networks (ANNs) encoding–decoding mechanism Estimation Filtering Neurons Robustness sensor resolution (SR) setmembership estimation State estimation Uncertainty unknown input |
| Title | Joint State and Unknown Input Estimation for a Class of Artificial Neural Networks With Sensor Resolution: An Encoding-Decoding Mechanism |
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