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A recurrent neural network for online design of robust optimal filters


Jiang, D and Wang, J, A recurrent neural network for online design of robust optimal filters, IEEE Transactions on Circuits and Systems Part 1: Fundamental Theory and Applications, 47, (6) pp. 921-926. ISSN 1057-7122 (2000) [Refereed Article]

DOI: doi:10.1109/81.852947


A recurrent neural network is developed for robust optimal filter design. The purpose is to fill the gap between the real-time computation requirement in practice and the computational complexity of the filter design in the case that the statistical properties of noise are unknown. First, an H∞ requirement and an L2 requirement of filter design problem are formulated as a group of linear matrix inequalities. On this basis, an optimization problem is introduced to solve the robust optimal filter design problem. Then, a recurrent neural network is deliberately developed for solving the optimization problem in real time. The effectiveness and efficiency of the recurrent neural network is shown by use of theoretical and simulation results. © 2000 IEEE.

Item Details

Item Type:Refereed Article
Research Division:Engineering
Research Group:Communications engineering
Research Field:Signal processing
Objective Division:Manufacturing
Objective Group:Computer, electronic and communication equipment
Objective Field:Integrated circuits and devices
UTAS Author:Jiang, D (Dr Danchi Jiang)
ID Code:34373
Year Published:2000
Web of Science® Times Cited:3
Deposited By:Engineering
Deposited On:2005-08-01
Last Modified:2011-10-07

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