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Title: Local stability conditions for discrete-time cascade locally recurrent neural networks

Creator:

Patan, Krzysztof

Date:

2010

Resource Type:

artykuł

Contributor:

Korbicz, Józef - red. ; Uciński, Dariusz - red.

Group publication title:

AMCS, Volume 20 (2010)

Abstract:

The paper deals with a specific kind of discrete-time recurrent neural network designed with dynamic neuron models. Dynamics are reproduced within each single neuron, hence the network considered is a locally recurrent globally feedforward. A crucial problem with neural networks of the dynamic type is stability as well as stabilization in learning problems. The paper formulates local stability conditions for the analysed class of neural networks using Lyapunov?s first method. Moreover, a stabilization problem is defined and solved as a constrained optimization task. In order to tackle this problem, a gradient projection method is adopted. The efficiency and usefulness of the proposed approach are justified by using a number of experiments.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:46832

DOI:

10.2478/v10006-010-0002-x

Pages:

23-34

Source:

AMCS, Volume 20, Number 1 (2010) ; click here to follow the link

Language:

eng

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Subject and keywords:

locally recurrent neural network stability stabilization learning constrained optimization

Object collections:

Last modified:

Sep 7, 2021

In our library since:

Aug 10, 2018

Number of object content hits:

84

All available object's versions:

https://zbc.uz.zgora.pl/repozytorium/publication/54925

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Local stability conditions for discrete-time cascade locally recurrent neural networks Sep 7, 2021

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