Globally Exponentially Stability of Discrete-Time Recurrent Neural Networks with Unsaturating Linear Activation Functions-Reference-Cited by-同舟云学术

Globally Exponentially Stability of Discrete-Time Recurrent Neural Networks with Unsaturating Linear Activation Functions

Published:2011-02 Issue: Volume:467-469 Page:731-736
ISSN:1662-9795
Container-title:Key Engineering Materials
language:
Short-container-title:KEM

Author:

Lin Zong Bing¹,Tan Qian Rong¹,Li Jun¹

Affiliation:

1. PanZhiHua University

Abstract

Globally exponentially stability (GES) of a class of discrete- time recurrent neural networks with unsaturating linear activation functions is studied. Based on matrix eigenvalue, a new definition of GES is presented. By applying matrix theory, some conditions for GES are obtained. Simultaneously, those conditions are proved without energy functions.

Publisher

Trans Tech Publications, Ltd.

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Link

https://www.scientific.net/KEM.467-469.731.pdf

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