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.
|Title of host publication
|2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Published - 2019
|2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: May 26 2019 → May 29 2019
|Proceedings - IEEE International Symposium on Circuits and Systems
|2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
|5/26/19 → 5/29/19
ASJC Scopus subject areas
- Electrical and Electronic Engineering