Existence and exponential stability of periodic solution of delayed Cohen-Grossberg neural networks via impulsive control Academic Article

abstract

• 2014, The Natural Computing Applications Forum. This paper focuses on the existence, uniqueness and global exponential stability of periodic solution for CohenGrossberg neural networks (CGNN) with periodic coefficients and time-varying delays. Some novel delay-independent criteria are obtained by using contraction mapping theorem and comparison principle. The present results improve and extend those in many publications, and shows that under some delay-independent criteria, the CGNNs may admit a periodic solution, which is globally exponential stable via impulsive controller even if it is originally unstable or divergent. Two examples with numerical simulations are given to demonstrate the efficiency of theoretical results.

authors

• Huang, Tingwen

published proceedings

• NEURAL COMPUTING & APPLICATIONS

author list (cited authors)

• Qi, J., Li, C., & Huang, T.

citation count

• 2

complete list of authors

• Qi, Jiangtao||Li, Chuandong||Huang, Tingwen

publication date

• August 2015

publisher

• Springer Nature  Publisher

published in

• Neural Computing and Applications  Journal

keywords

• Cohen-grossberg Neural Network
• Comparison Principle
• Contraction Mapping Theorem
• Impulse
• Periodic Solution
• STABILITY
• Time-varying Delay

Digital Object Identifier (DOI)

• 10.1007/s00521-014-1793-8

start page

• 1369

end page

• 1377

volume

• 26

issue

• 6

URL

• http://dx.doi.org/10.1007/s00521-014-1793-8