Adaptive filter theory
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
The Linear l1 Estimator and the Huber M-Estimator
SIAM Journal on Optimization
Breakdown of equivalence between the minimal l1-norm solution and the sparsest solution
Signal Processing - Sparse approximations in signal and image processing
System parameter estimation with input/output noisy data andmissing measurements
IEEE Transactions on Signal Processing
Cooperative Recurrent Neural Networks for the Constrained L1 Estimator
IEEE Transactions on Signal Processing - Part I
A recursive least squares implementation for LCMP beamforming underquadratic constraint
IEEE Transactions on Signal Processing
Novel Cooperative Neural Fusion Algorithms for Image Restoration and Image Fusion
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
| Hi-index | 35.68 |
Recently, cooperative recurrent neural networks for solving three linearly constrained L1 estimation problems were developed and applied to linear signal and image models under non-Gaussian noise environments. For wide applications, this paper proposes a compact cooperative recurrent neural network (CRNN) for calculating general constrained L1 norm estimators. It is shown that the proposed CRNN converges globally to the constrained L1 norm estimator without any condition. The proposed CRNN includes three existing CRNNs as its special cases. Unlike the three existing CRNNs, the proposed CRNN is easily applied and can deal with the nonlinear elliptical sphere constraint. Moreover, when computing the general constrained L1 norm estimator, the proposed CRNN has a fast convergence speed due to low computational complexity. Simulation results confirm further the good performance of the proposed CRNN.