global exponential robust stability of static interval neural networks with time delay in the leakage term

global exponential robust stability of static interval neural networks with time delay in the leakage term

;Guiying Chen;Linshan Wang
Chemico-biological interactions 2014 Vol. 2014 pp. -
171
chen2014journalglobal

Abstract

The stability of a class of static interval neural networks with time delay in the leakage term is investigated. By using the method of M-matrix and the technique of delay differential inequality, we obtain some sufficient conditions ensuring the global exponential robust stability of the networks. The results in this paper extend the corresponding conclusions without leakage delay. An example is given to illustrate the effectiveness of the obtained results.

Citation

ID: 218123
Ref Key: chen2014journalglobal
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
218123
Unique Identifier:
10.1155/2014/972608
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet