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 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.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:PP

Enthalten in:

IEEE transactions on neural networks and learning systems - PP(2024) vom: 10. Jan.

Sprache:

Englisch

Beteiligte Personen:

Shen, Yuxuan [VerfasserIn]
Wang, Zidong [VerfasserIn]
Dong, Hongli [VerfasserIn]
Liu, Hongjian [VerfasserIn]
Liu, Xiaohui [VerfasserIn]

Links:

Volltext

Themen:

Journal Article

Anmerkungen:

Date Revised 10.01.2024

published: Print-Electronic

Citation Status Publisher

doi:

10.1109/TNNLS.2023.3348752

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM366888498