Analog VLSI and neural systems
Analog VLSI and neural systems
A recurrent neural network for real-time matrix inversion
Applied Mathematics and Computation
Analysis and design of recurrent neural networks and their applications to control and robotic systems
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
| Hi-index | 0.00 |
Different from gradient-based neural networks, a special kind of recurrent neural network has recently been proposed by Zhang et al for real-time solution of time-varying problems. In this paper, we generalize such a design method to solving online the time-varying Sylvester equation. In comparison with gradient-based neural networks, the resultant Zhang neural network for solving time-varying Sylvester equation is designed based on a matrix-valued error function, instead of a scalar-valued error function. It is depicted in an implicit dynamics, instead of an explicit dynamics. Furthermore, Zhang neural network globally exponentially converges to the exact solution of the time-varying Sylvester equation. Simulation results substantiate the theoretical analysis and demonstrate the efficacy of Zhang neural network on time-varying problem solving, especially when using a power-sigmoid activation function.