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A neural network solver for basis pursuit and its applications to time-frequency analysis of biomedical signals

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5 Author(s)
Wang, Z.S. ; Dept. of Radio Eng., Southeast Univ., Nanjing, China ; Xia, Y.S. ; Li, W.H. ; He, Z.Y.
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In this paper the authors present a new neural network model, called the constrained smallest l1-norm neural network (CSl 1 NN), for basis pursuit (BP) implementation. The BP is considered as a large-scale linear programming problem. In contrast with the simplex-BP or inferior-BP, the proposed CSl1 NN-BP does not double the optimizing scale and can be implemented in real time via hardware. Using non-stationary artificial signals and electrogastrograms to test our simulations show that the CSl1 NN-BP presents an excellent convergence performance for a wide range of time-frequency (TF) dictionaries and has a higher joint TF resolution not only than the traditional Wigner distribution, but also other overcomplete representation methods. Combining the high resolution with the fast implementation, the CSl1 NN-BP can be used for online time-frequency analysis of various kinds of non-stationary signals including medical data, such as ECG, EEG and EGG

Published in:

Neural Networks,1997., International Conference on  (Volume:4 )

Date of Conference:

9-12 Jun 1997