Exponential stability of inertial BAM neural networks with time-varying delay via periodically intermittent control Academic Article

abstract

• 2015, The Natural Computing Applications Forum. In this paper, we study global exponential stability problem for inertial BAM neural networks with time-varying delay via periodically intermittent control. By utilizing suitable variable substitution, the second-order system can be transformed into first-order differential equations. It is shown that the states of the inertial BAM neural networks with time-varying delay via periodically intermittent control can be globally exponential stabilized with a desired oribis under the designed intermittent controller. Finally, a typical example is chosen to illustrate the validation of the theoretical results.

authors

• Huang, Tingwen

published proceedings

• NEURAL COMPUTING & APPLICATIONS

author list (cited authors)

• Zhang, W., Li, C., Huang, T., & Tan, J.

citation count

• 52

complete list of authors

• Zhang, Wei||Li, Chuandong||Huang, Tingwen||Tan, Jie

publication date

• October 2015

publisher

• Springer Nature  Publisher

published in

• Neural Computing and Applications  Journal

keywords

• Exponential Stability
• Inertial Bam Neural Networks
• Intermittent Control
• Time-varying Delay

Digital Object Identifier (DOI)

• 10.1007/s00521-015-1838-7

start page

• 1781

end page

• 1787

volume

• 26

issue

• 7

URL

• http://dx.doi.org/10.1007/s00521-015-1838-7