Periodicity and stability for variable-time impulsive neural networks. Academic Article

abstract

• The paper considers a general neural networks model with variable-time impulses. It is shown that each solution of the system intersects with every discontinuous surface exactly once via several new well-proposed assumptions. Moreover, based on the comparison principle, this paper shows that neural networks with variable-time impulse can be reduced to the corresponding neural network with fixed-time impulses under well-selected conditions. Meanwhile, the fixed-time impulsive systems can be regarded as the comparison system of the variable-time impulsive neural networks. Furthermore, a series of sufficient criteria are derived to ensure the existence and global exponential stability of periodic solution of variable-time impulsive neural networks, and to illustrate the same stability properties between variable-time impulsive neural networks and the fixed-time ones. The new criteria are established by applying Schaefer's fixed point theorem combined with the use of inequality technique. Finally, a numerical example is presented to show the effectiveness of the proposed results.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Li, H., Li, C., & Huang, T.

citation count

• 42

complete list of authors

• Li, Hongfei||Li, Chuandong||Huang, Tingwen

publication date

• October 2017

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Comparison Principle
• Global Exponential Stability
• Neural Networks
• Neural Networks, Computer
• Periodic Solution
• Periodicity
• Time Factors
• Variable-time Impulses

PubMed Central ID

• 28732232

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2017.06.006

start page

• 24

end page

• 33

volume

• 94

URL

• http://dx.doi.org/10.1016/j.neunet.2017.06.006