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Volume 32, Issue 3
Scale-Type Stability for Neural Networks with Unbounded Time-Varying Delays

Liangbo Chen & Zhenkun Huang

Ann. Appl. Math., 32 (2016), pp. 234-248.

Published online: 2022-06

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  • Abstract

This paper studies scale-type stability for neural networks with unbounded time-varying delays and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and global asymptotic stability of such neural networks on time scales are derived. The new results can extend the existing relevant stability results in the previous literatures to cover some general neural networks.

  • Keywords

global asymptotic stability, global exponential stability, neural networks, on time scales.

  • AMS Subject Headings

92B20

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{AAM-32-234, author = {Chen , Liangbo and Huang , Zhenkun}, title = {Scale-Type Stability for Neural Networks with Unbounded Time-Varying Delays}, journal = {Annals of Applied Mathematics}, year = {2022}, volume = {32}, number = {3}, pages = {234--248}, abstract = {

This paper studies scale-type stability for neural networks with unbounded time-varying delays and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and global asymptotic stability of such neural networks on time scales are derived. The new results can extend the existing relevant stability results in the previous literatures to cover some general neural networks.

}, issn = {}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/aam/20640.html} }
TY - JOUR T1 - Scale-Type Stability for Neural Networks with Unbounded Time-Varying Delays AU - Chen , Liangbo AU - Huang , Zhenkun JO - Annals of Applied Mathematics VL - 3 SP - 234 EP - 248 PY - 2022 DA - 2022/06 SN - 32 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/aam/20640.html KW - global asymptotic stability, global exponential stability, neural networks, on time scales. AB -

This paper studies scale-type stability for neural networks with unbounded time-varying delays and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and global asymptotic stability of such neural networks on time scales are derived. The new results can extend the existing relevant stability results in the previous literatures to cover some general neural networks.

Liangbo Chen & Zhenkun Huang. (2022). Scale-Type Stability for Neural Networks with Unbounded Time-Varying Delays. Annals of Applied Mathematics. 32 (3). 234-248. doi:
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