Year: 2024
Type: article
Abstract: Dynamic LR and QR factorization are fundamental problems that exist widely in the control field. However, the existing solutions under noises are lack of convergence speed and anti-noise ability. To this end, this paper incorporates the advantages of Dynamic-Coefficient Type (DCT) and Integration-Enhance Type (IET) Zeroing Neural Dynamic (ZND), and proposes an Adaptive and Robust-Enhanced Neural Dynamic (AREND). On this basis, a Strategy of Integration-Coupling (SIC) is proposed to address multiple error function problems, improving model stability and application scenarios. This strategy is experimentally proven to be effective and has potential expansion capability. After that, the convergence and robustness of our AREND is theoretically analyzed. Furthermore, the proposed AREND is verified by numerical experiments of low-to-high dimensional factorization in comparison with existing solutions. Finally, the real-time 3-D Angle of Arrival (AoA) localization in multiple high-noise conditions, is validated to the accuracy of the proposed model. Code is available at https://github.com/Alana2a3/AREND-Code-Implementation .
Source: Scientific Reports
Authors: Fenghao Zhuang, Huiting He, Aiping Ye, Lilan Zou
Institution: Guangdong Ocean University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,590
Awards: G6912452004, G8319300695
OpenAlex ID: W4404312884