Global Stability of Bidirectional Associative Memory Neural Networks With Multiple Time-Varying Delays. Academic Article

abstract

• This article investigates the global stability of bidirectional associative memory neural networks with discrete and distributed time-varying delays (DBAMNNs). By employing the comparison strategy and inequality techniques, global asymptotic stability (GAS) and global exponential stability (GES) of the underlying DBAMNNs are of concern in terms of p -norm ( p 2 ). Meanwhile, GES of the addressed DBAMNNs is also analyzed in terms of 1-norm. When distributed time delay is neglected, the GES of the corresponding bidirectional associative memory neural networks is presented as an M -matrix, which includes certain existing outcomes as special cases. Two examples are finally provided to substantiate the validity of theories.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Cybern

author list (cited authors)

• Sheng, Y., Zeng, Z., & Huang, T.

citation count

• 16

complete list of authors

• Sheng, Yin||Zeng, Zhigang||Huang, Tingwen

publication date

• June 2022

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE TRANSACTIONS ON CYBERNETICS  Journal

keywords

• Algorithms
• Neural Networks, Computer
• Time Factors

Digital Object Identifier (DOI)

• 10.1109/TCYB.2020.3011581

start page

• 4095

end page

• 4104

volume

• 52

issue

• 6

URL

• http://dx.doi.org/10.1109/tcyb.2020.3011581