期刊
INFORMATION SCIENCES
卷 611, 期 -, 页码 494-503出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2022.08.061
关键词
Time-varying Lyapunov equation; Finite-time convergence; Gradient neural network method; Bounded additive time-varying noise; Dynamic neural network
资金
- Natural Science Foundation of Guangdong Province [2022A1515010976]
- Young Scholar Program of Pazhou Lab [PZL2021KF0022]
- Science and Technology Program of Guangzhou [202201010457]
This paper proposes an improved gradient neural network (IGNN) method by introducing additional nonlinearity to address the issue of inaccurate solution in traditional GNN models when dealing with time-varying Lyapunov equations (LEs). Simulation results demonstrate that the IGNN method achieves finite-time convergence even in the presence of bounded additive time-varying noises.
Dynamic neural networks are efficient for solving algebraic equations. Among them, the gradient neural network (GNN) has the lowest model complexity. The conventional GNN models have exponential convergence when dealing with static Lyapunov equations (LEs), but fail to find the accurate solution when the solutions change with time. To fill this gap, in this paper, we propose an improved GNN method by introducing additional nonlinearity into a traditional GNN model for handling time-varying LEs. It is shown that the proposed improved GNN (IGNN) method has finite-time convergence when it is applied to time-varying LEs, even if there are bounded additive time-varying noises. Simulation results demonstrate the efficacy and advantages of the proposed method over the existing GNN methods and other dynamic neural network methods for time-varying LEs solving. (C) 2022 Elsevier Inc. All rights reserved.
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