A new robust training law for dynamic neural networks with external disturbance: an LMI approach
type: Item
instance of: scholarly article
title: A new robust training law for dynamic neural networks with external disturbance: an LMI approach
zbMATH Open document ID: 1206.93101
DOI: 10.1155/2010/415895
author: Choon Ki Ahn
published in: Discrete Dynamics in Nature and Society
publication date: 2011-02-09T00:00:00Z
full work available at URL: https://eudml.org/doc/231292
review text: Summary: A new robust training law, which is called an Input/Output-to-State Stable Training Law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on Linear Matrix Inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.
Mathematics Subject Classification ID: 93D25
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 93C73
Mathematics Subject Classification ID: 93D20
zbMATH DE Number: 5848776
zbMATH Keywords: robust training
zbMATH Keywords: input/output-to-state stable training
zbMATH Keywords: dynamic neural networks
zbMATH Keywords: external disturbance
zbMATH Keywords: exponential stability
zbMATH Keywords: linear matrix inequality
Wikidata QID: Q58650805
MaRDI profile type: Publication
OpenAlex ID: W2139662816
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description: scientific article; zbMATH DE number 5848776