This paper presents a new kind of recurrent neural network proposed by Zhang et al. for solving online Lyapunov equation with time-varying coefficient matrices. Global exponential convergence could be achieved by such a recurrent neural network when solving the time-varying problems in comparison with gradient neural networks (GNN). MATLAB simulation of both neural networks for the real-time solution of time-varying Lyapunov equation is then investigated through several important techniques. Computer-simulation results substantiate the theoretical analysis and demonstrate the efficacy of such a Zhang neural network (ZNN) on time-varying Lyapunov equation solving, especially when using power-sigmoid activation functions. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Zhang, Y., Yue, S., Chen, K., & Yi, C. (2008). MATLAB simulation and comparison of Zhang neural network and gradient neural network for time-varying Lyapunov equation solving. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5263 LNCS, pp. 117–127). Springer Verlag. https://doi.org/10.1007/978-3-540-87732-5_14
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