Topics in matrix analysis
Analog VLSI and neural systems
Analog VLSI and neural systems
Neurocomputing: foundations of research
Neurocomputing: foundations of research
A Systolic Architecture for Fast Dense Matrix Inversion
IEEE Transactions on Computers
Recurrent neural networks for nonlinear output regulation
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
A recurrent neural network for solving Sylvester equation with time-varying coefficients
IEEE Transactions on Neural Networks
Design and analysis of a general recurrent neural network model for time-varying matrix inversion
IEEE Transactions on Neural Networks
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Different from gradient neural networks (GNN), a special kind ofrecurrent neural networks has been proposed recently by Zhanget alfor solving online linear matrix equations withtime-varying coefficients. Such recurrent neural networks, designedbased on a matrix-valued error-function, could achieve globalexponential convergence when solving online time-varying problemsin comparison with gradient neural networks. This paperinvestigates the MATLAB simulation of Zhang neural networks (ZNN)for real-time solution of linear time-varying matrix equationAXB- C= 0. Gradient neural networks aresimulated and compared as well. Simulation results substantiate thetheoretical analysis and efficacy of ZNN on linear time-varyingmatrix equation solving.