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An improved compressed sensing reconstruction algorithm based on artificial neural network

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2 Author(s)
Chun-hui Zhao ; College of Information & Communication Engineering, Harbin Engineering University, Harbin, P.R. China ; Yun-long Xu

To meet both the precision and convergence rate requirement of reconstruction algorithm, an improved compressed sensing reconstruction algorithm based on artificial neural network (IANN-CS) is proposed in this paper. The approach applies Artificial Neural Network structure to compressed sensing (ANN-CS) to reconstruct sparse signal, and on this basis, a dynamic learning factor is obtained by using gradient descent method repeatedly to replace the one which is a constant in ANN-CS algorithm, this improved algorithm is also called IANN-CS in this paper. The experimental results show that, compared with ANN-CS algorithm, IANN-CS algorithm has greatly improved convergence rate with a little change in convergence precision. In addition, under the same reconstruction conditions, IANN-CS algorithm has a good compromise between reconstruction precision and convergence rate, what is more, the observation value needed in ANN CS and IANN-CS algorithm are less than which in the existing reconstruction algorithms.

Published in:

Electronics, Communications and Control (ICECC), 2011 International Conference on

Date of Conference:

9-11 Sept. 2011