Abstract
A global asymptotic stability condition for Long Short-Term Memory neural networks is presented in this paper. A linear matrix inequality optimization problem is used to describe this global stability condition. The linear matrix inequality formulation can be viewed as a way for stabilization of Long Short-Term Memory neural networks since the networks' weight matrices and biases can be essentially treated as control variables. The condition and how to compute numerical values for the weight matrices and biases are illustrated by some examples.
Original language | English |
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Title of host publication | 2019 IEEE International Symposium on Circuits and Systems (ISCAS) |
Publisher | IEEE |
Pages | 1-4 |
Number of pages | 4 |
ISBN (Print) | 978-1-7281-0397-6 |
DOIs | |
Publication status | Published - 29 May 2019 |
MoE publication type | A4 Conference publication |
Event | IEEE International Symposium on Circuits and Systems - Sapporo, Japan, Sapporo, Japan Duration: 26 May 2019 → 29 May 2019 |
Conference
Conference | IEEE International Symposium on Circuits and Systems |
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Abbreviated title | ISCAS |
Country/Territory | Japan |
City | Sapporo |
Period | 26/05/2019 → 29/05/2019 |
Keywords
- Asymptotic stability
- Numerical stability
- Stability analysis
- Mathematical model
- Linear matrix inequalities
- Neural networks
- Chaos