1 research outputs found
Fast L1-Minimization Algorithm for Sparse Approximation Based on an Improved LPNN-LCA framework
The aim of sparse approximation is to estimate a sparse signal according to
the measurement matrix and an observation vector. It is widely used in data
analytics, image processing, and communication, etc. Up to now, a lot of
research has been done in this area, and many off-the-shelf algorithms have
been proposed. However, most of them cannot offer a real-time solution. To some
extent, this shortcoming limits its application prospects. To address this
issue, we devise a novel sparse approximation algorithm based on Lagrange
programming neural network (LPNN), locally competitive algorithm (LCA), and
projection theorem. LPNN and LCA are both analog neural network which can help
us get a real-time solution. The non-differentiable objective function can be
solved by the concept of LCA. Utilizing the projection theorem, we further
modify the dynamics and proposed a new system with global asymptotic stability.
Simulation results show that the proposed sparse approximation method has the
real-time solutions with satisfactory MSEs