Abstract
Compressive sampling is a sampling technique for sparse signals. The advantage of compressive sampling is that signals are compactly represented by a few number of measured values. This paper adopts an analog neural network technique, Lagrange programming neural networks (LPNNs), to recover data in compressive sampling. We propose the LPNN dynamics to handle three sceneries, including the standard recovery of sparse signal, the recovery of non-sparse signal, and the noisy measurement values, in compressive sampling. Simulation examples demonstrate that our approach effectively recovers the signals from the measured values for both noise free and noisy environment.
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Lam, PM., Leung, C.S., Sum, J., Constantinides, A.G. (2010). Lagrange Programming Neural Networks for Compressive Sampling. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_22
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DOI: https://doi.org/10.1007/978-3-642-17534-3_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17533-6
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