Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/28573
Title: | Efficient Predefined-Time Adaptive Neural Networks for Computing Time-Varying Tensor Moore–Penrose Inverse |
Authors: | Qi, Z Ning, Y Xiao, L Wang, Z He, Y |
Keywords: | adaptive parameter;event-triggering mechanism;predefined-time convergence;recurrent neural network (RNN);sound source localization;time-varying tensor Moore–Penrose (MP) inverse |
Issue Date: | 30-Jan-2024 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Qi, Z. et al. (2024) 'Efficient Predefined-Time Adaptive Neural Networks for Computing Time-Varying Tensor Moore–Penrose Inverse', IEEE Transactions on Neural Networks and Learning Systems, 0 (early access), pp. 1 - 12. doi: 10.1109/TNNLS.2024.3354936. |
Abstract: | This article proposes predefined-time adaptive neural network (PTANN) and event-triggered PTANN (ET-PTANN) models to efficiently compute the time-varying tensor Moore–Penrose (MP) inverse. The PTANN model incorporates a novel adaptive parameter and activation function, enabling it to achieve strongly predefined-time convergence. Unlike traditional time-varying parameters that increase over time, the adaptive parameter is proportional to the error norm, thereby better allocating computational resources and improving efficiency. To further enhance efficiency, the ET-PTANN model combines an event trigger with the evolution formula, resulting in the adjustment of step size and reduction of computation frequency compared to the PTANN model. By conducting mathematical derivations, the article derives the upper bound of convergence time for the proposed neural network models and determines the minimum execution interval for the event trigger. A simulation example demonstrates that the PTANN and ET-PTANN models outperform other related neural network models in terms of computational efficiency and convergence rate. Finally, the practicality of the PTANN and ET-PTANN models is demonstrated through their application for mobile sound source localization. |
URI: | https://bura.brunel.ac.uk/handle/2438/28573 |
DOI: | https://doi.org/10.1109/TNNLS.2024.3354936 |
ISSN: | 2162-237X |
Other Identifiers: | ORCiD: Zhaohui Qi https://orcid.org/0000-0002-0028-3317 ORCiD: Lin Xiao https://orcid.org/0000-0003-0007-2904 ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401 ORCiD: Yongjun He https://orcid.org/0000-0002-5228-0302 |
Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © 2024 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. See https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ for more information | 1.35 MB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.