Peak-to-peak exponential direct learning of continuous-time recurrent neural network models: a matrix inequality approach
- Authors
- Ahn, Choon Ki; Song, Moon Kyou
- Issue Date
- 2013
- Publisher
- SPRINGER INTERNATIONAL PUBLISHING AG
- Keywords
- exponential peak-to-peak norm performance; training law; dynamic neural network models; disturbance; matrix inequality
- Citation
- JOURNAL OF INEQUALITIES AND APPLICATIONS
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF INEQUALITIES AND APPLICATIONS
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/133799
- DOI
- 10.1186/1029-242X-2013-68
- ISSN
- 1025-5834
- Abstract
- 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.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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