Long-short term memory neural network stability and stabilization using linear matrix inequalities

Shankar A. Deka, Dušan M. Stipanović, Boris Murmann, Claire J. Tomlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publication2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728103976
DOIs
StatePublished - Jan 1 2019
Event2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: May 26 2019May 29 2019

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2019-May
ISSN (Print)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
CountryJapan
CitySapporo
Period5/26/195/29/19

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Deka, S. A., Stipanović, D. M., Murmann, B., & Tomlin, C. J. (2019). Long-short term memory neural network stability and stabilization using linear matrix inequalities. In 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings [8702629] (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2019.8702629