Zhang, Yunong and Ge, Shuzhi Sam (2005) Design and Analysis of a General Recurrent Neural Network Model for Time-Varying Matrix Inversion. IEEE Transactions on Neural Networks, 16 (6). pp. 1477-1490. ISSN 1045-9227

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Abstract

Following the idea of using first-order time derivatives, this paper presents a general recurrent neural network (RNN) model for online inversion of time-varying matrices. Different kinds of activation functions are investigated to guarantee the global exponential convergence of the neural model to the exact inverse of a given time-varying matrix. The robustness of the proposed neural model is also studied with respect to different activation functions and various implementation errors. Simulation results, including the application to kinematic control of redundant manipulators, substantiate the theoretical analysis and demonstrate the efficacy of the neural model on time-varying matrix inversion, especially when using a power-sigmoid activation function.

Item Type:	Article
Keywords:	Activation function; implicit dynamics; inverse kinematics; recurrent neural network (RNN); time-varying matrix; inversion;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	2278
Identification Number:	https://doi.org/10.1109/TNN.2005.857946
Depositing User:	Hamilton Editor
Date Deposited:	24 Nov 2010 16:47
Journal or Publication Title:	IEEE Transactions on Neural Networks
Publisher:	IEEE
Refereed:	Yes
URI:	http://ieeexplore.ieee.org/Xplore/guesth...
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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