A new type of recurrent neural networks for real-time solution of Lyapunov equation with time-varying coefficient matrices

Chenfu Yi, Yunong Zhang and Dongsheng Guo

Mathematics and Computers in Simulation (MATCOM), 2013, vol. 92, issue C, 40-52

Abstract: 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).

Keywords: Recurrent neural networks; Lyapunov equation; Time-varying; Exponential convergence; Implicit dynamics (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:92:y:2013:i:c:p:40-52

DOI: 10.1016/j.matcom.2013.04.019

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