Neural-Network-Based Set-Membership Fault Estimation for 2-D Systems Under Encoding-Decoding Mechanism

In this article, the simultaneous state and fault estimation problem is investigated for a class of nonlinear 2-D shift-varying systems, where the sensors and the estimator are connected via a communication network of limited bandwidth. With the purpose of relieving the communication burden and enha...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 34; číslo 2; s. 786 - 798
Hlavní autori:	Zhu, Kaiqun, Wang, Zidong, Chen, Yun, Wei, Guoliang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.02.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	2-D systems Artificial neural networks Convexity encoding–decoding mechanism (EDM) Estimation fault estimation Neural networks neural networks (NNs) Optimization Quantization (signal) Scalars Security set-membership estimation (SME) State estimation Symmetric matrices Task analysis Tuning
ISSN:	2162-237X, 2162-2388, 2162-2388
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