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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yun-Chung Chu |
| Copyright Year | 2001 |
| Description | Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore (Yun-Chung Chu) |
| Abstract | As a nonlinear system, a recurrent neural network generally has an incremental gain different from its induced norm. While most of the previous research efforts were focused on the latter, this paper presents a method to compute an effective upper bound of the former for a class of discrete-time recurrent neural networks, which is not only applied to systems with arbitrary inputs but also extended to systems with small-norm inputs. The upper bound is computed by simple optimizations subject to linear matrix inequalities. |
| Sponsorship | IEEE Control Syst. Soc |
| Starting Page | 2732 |
| Ending Page | 2737 |
| File Size | 444366 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780370619 |
| DOI | 10.1109/CDC.2001.980685 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2001-12-04 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Recurrent neural networks Linear matrix inequalities Upper bound Computer networks Control system synthesis Nonlinear systems Neurons Lyapunov method Reduced order systems Ear |
| Content Type | Text |
| Resource Type | Article |
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