Analog VLSI and neural systems
Analog VLSI and neural systems
Explicit solution of Sylvester and Lyapunov equations
Mathematics and Computers in Simulation
Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
On the stability analysis of nonlinear systems using polynomial Lyapunov functions
Mathematics and Computers in Simulation
Multi-criteria optimization in nonlinear predictive control
Mathematics and Computers in Simulation
New delay-dependent exponential stability criteria of BAM neural networks with time delays
Mathematics and Computers in Simulation
A neural network for solving a convex quadratic bilevel programming problem
Journal of Computational and Applied Mathematics
SIAM Journal on Matrix Analysis and Applications
Improved gradient-based neural networks for online solution of Lyapunov matrix equation
Information Processing Letters
Mathematical and Computer Modelling: An International Journal
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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A new kind of recurrent neural network is presented for solving the Lyapunov equation with time-varying coefficient matrices. Different from other neural-computation approaches, the neural network is developed by following Zhang et al.'s design method, which is capable of solving the time-varying Lyapunov equation. The resultant Zhang neural network (ZNN) with implicit dynamics could globally exponentially converge to the exact time-varying solution of such a Lyapunov equation. Computer-simulation results substantiate that the proposed recurrent neural network could achieve much superior performance on solving the Lyapunov equation with time-varying coefficient matrices, as compared to conventional gradient-based neural networks (GNN).