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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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 36; číslo 2; s. 3671 - 3681
Hlavní autoři: Shen, Yuxuan, Wang, Zidong, Dong, Hongli, Liu, Hongjian, Liu, Xiaohui
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.02.2025
Témata:
Artificial neural networks
Artificial neural networks (ANNs)
encoding–decoding mechanism
Estimation
Filtering
Neurons
Robustness
sensor resolution (SR)
setmembership estimation
State estimation
Uncertainty
unknown input
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2023.3348752