A cooperative recurrent neural network for solving L-1 estimation problems with general linear constraints

作者：Xia Youshen^*; Kamel Mohamed S

来源：Neural Computation, 2008, 20(3): 844-872.

DOI：10.1162/neco.2007.10-06-376

The constrained L-1 estimation is an attractive alternative to both the unconstrained L1 estimation and the least square estimation. In this letter, we propose a cooperative recurrent neural network (CRNN) for solving L-1 estimation problems with general linear constraints. The proposed CRNN model combines four individual neural network models automatically and is suitable for parallel implementation. As a special case, the proposed CRNN includes two existing neural networks for solving unconstrained and constrained L-1 estimation problems, respectively. Unlike existing neural networks with penalty parameters, for solving the constrained L-1 estimation problem, the proposed CRNN is guaranteed to converge globally to the exact optimal solution without any additional condition. Compared with conventional numerical algorithms, the proposed CRNN has a low computational complexity and can deal with the L-1 estimation problem with degeneracy. Several applied examples show that the proposed CRNN can obtain more accurate estimates than several existing algorithms.

• 出版日期2008-3
• 单位福州大学