Abstract. Wang proposed a gradient-based neural network (GNN) to solve online matrix-inverses. Global asymptotical convergence was shown for such a neural ...

N.C.F. Carneiro, L.P. Caloba, A new algorithm for analog matrix inversion, in: Proceedings of the 38th Midwest Symposium on Circuits and Systems, 1995, pp. 401– ...

In [22], a gradient-based neural network is proposed for matrix inversion, and its global exponential convergence performance and stability are analyzed. ...

Global exponential convergence and stability of gradient-based neural network for online matrix inversion. Wang proposed a gradient-based neural network (GNN) ...

PDF | This technical note presents theoretical analysis and simulation results on the performance of a classic gradient neural network (GNN), which was.

Mar 17, 2017 · Wang, Global exponential convergence and stability of gradient-based neural network for online matrix inversion, Applied Mathematics and ...

Index Terms—Global exponential convergence rate, gradient neural net- works, performance analysis, residual error bound, time-varying matrix inversion. I.

Zhang, Y., Chen, K.: Global Exponential Convergence and Stability of Wang Neural Network for Solving Online Linear Equations. ... Gradient-Based Neural System for ...

analysis shows that GNN can converge in finite time, while it can converge only in infinite time with two conventional activation functions — linear and power- ...

Recommendations. Global exponential convergence and stability of gradient-based neural network for online matrix inversion. Wang proposed a gradient-based ...