Delayed Lagrange neural network for sparse signal reconstruction under compressive sampling

In this paper, two new Lagrange neural networks are proposed for compressed sensing. The first one is for the standard recovery of sparse signals. The second one is for the recovery of sparse signals with noise. By drawing into delay, we also derive two delayed Lagrange neural networks. Both the two networks are based on the l0 norm approximation. They can be implemented by circuits.