Nonlinear gradient neural network for solving system of linear equations. (English) Zbl 1469.68012
Summary: For purpose of solving system of linear equations (SoLE) more efficiently, a fast convergent gradient neural network (FCGNN) model is designed and discussed in this paper. Different from the design of the conventional gradient neural network (CGNN), the design of the FCGNN model is based on a nonlinear activation function, and thus the better convergence speed can be reached. In addition, the convergence upper bound of the FCGNN model is estimated and provided in details. Simulative results validate the superiority of the FCGNN model, as compared to the CGNN model for finding SoLE.
MSC:
| 68Q06 | Networks and circuits as models of computation; circuit complexity |
| 65F10 | Iterative numerical methods for linear systems |
| 68Q07 | Biologically inspired models of computation (DNA computing, membrane computing, etc.) |
Keywords:
systems of linear equations; fast convergence; nonlinear activation function; gradient neural network (GNN); performance evaluationReferences:
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