Paper Title:
Globally Exponentially Stability of Discrete-Time Recurrent Neural Networks with Unsaturating Linear Activation Functions
  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.

  Info
Periodical
Key Engineering Materials (Volumes 467-469)
Edited by
Dehuai Zeng
Pages
731-736
DOI
10.4028/www.scientific.net/KEM.467-469.731
Citation
Z. B. Lin, Q. R. Tan, J. Li, "Globally Exponentially Stability of Discrete-Time Recurrent Neural Networks with Unsaturating Linear Activation Functions", Key Engineering Materials, Vols. 467-469, pp. 731-736, 2011
Online since
February 2011
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