Journal of inequalities and applications | |
Peak-to-peak exponential direct learning of continuous-time recurrent neural network models: a matrix inequality approach | |
Choon Ki Ahn1  | |
关键词: exponential peak-to-peak norm performance; training law; dynamic neural network models; disturbance; matrix inequality; | |
DOI : 10.1186/1029-242X-2013-68 | |
学科分类:数学(综合) | |
来源: SpringerOpen | |
【 摘 要 】
The purpose of this paper is to propose a new peak-to-peak exponential direct learning law (P2PEDLL) for continuous-time dynamic neural network models with disturbance. Dynamic neural network models trained by the proposed P2PEDLL based on matrix inequality formulation are exponentially stable, with a guaranteed exponential peak-to-peak norm performance. The proposed P2PEDLL can be determined by solving two matrix inequalities with a fixed parameter, which can be efficiently checked using existing standard numerical algorithms. We use a numerical example to demonstrate the validity of the proposed direct learning law.
【 授权许可】
CC BY
【 预 览 】
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RO201902012403827ZK.pdf | 464KB | download |