Please use this identifier to cite or link to this item:
|Title:||A General Recurrent Neural Network Model for Time-Varying Matrix Inversion||Authors:||Zhang, Y.
|Issue Date:||2003||Citation:||Zhang, Y.,Ge, S.S. (2003). A General Recurrent Neural Network Model for Time-Varying Matrix Inversion. Proceedings of the IEEE Conference on Decision and Control 6 : 6169-6174. ScholarBank@NUS Repository. https://doi.org/10.1109/CDC.2003.1272262||Abstract:||This paper presents a general recurrent neural network model for online inversion of time-varying matrices. Utilizing the first-order time-derivative, the neural model guarantees its state trajectory globally converge to the exact inverse of a given time-varying matrix. In addition, exponential convergence can be achieved if linear or sigmoid activation function is used. Network sensitivity is also studied to show the desirable robustness property of this neural approach. Simulation results, including the application to kinematic control of redundant manipulators, are used to demonstrate the effectiveness and performance of the proposed neural model.||Source Title:||Proceedings of the IEEE Conference on Decision and Control||URI:||http://scholarbank.nus.edu.sg/handle/10635/68817||ISBN:||0780379241||ISSN:||01912216||DOI:||10.1109/CDC.2003.1272262|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 29, 2021
checked on Jul 16, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.