In this work, a GNN model is constructed to solve PSME. Through the intervention of appropriate activation functions, specifically monotonically increasing odd functions, the proposed model can effectively converge to the unknown matrix of the equation and complete the solution task.
The gradient neural network (GNN) method is a novel approach to solving matrices. Based on this method, this paper improves the gradient neural network (IGNN) ...
This paper considers neural network solutions of a category of matrix equation called periodic Sylvester matrix equation (PSME), which appear in the process ...
Aug 28, 2023 · Here, a gradient-based neural network (GNN) model is constructed for solving the discrete periodic Lyapunov matrix equation (DPLME) ...
An improved gradient neural network for solving periodic Sylvester matrix equations ... Conjugate gradient-based iterative algorithm for solving generalized ...
(Journal of the Franklin Institute, 2018), we adopt relaxation technique and introduce relaxation factors into the gradient based iterative (GI) algorithm, and ...
Abstract. A new type of activation function, named Li activation function, is used in gradient-based neural network (GNN) to solve Lyapunov matrix equation.
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This paper aims at finding a fixed-time solution to the Sylvester equation by using a gradient neural network (GNN). To reach this goal, ...
A new kind of iterative algorithm is proposed for constructing the least square solution for the equations. The basic idea is to develop the solution matrices ...
Gradient-based neural networks for solving periodic Sylvester matrix equations · A finite iterative algorithm for the general discrete-time periodic Sylvester ...