Neural Computing and Applications

, Volume 28, Issue 4, pp 703–716 | Cite as

Stability analysis of two-dimensional neutral-type Cohen–Grossberg BAM neural networks

Original Article
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Accepted:

Abstract

Two-dimensional system model represents a wide range of practical systems, such as image data processing and transmission, thermal processes, gas absorption and water stream heating. Moreover, there are few dynamical discussions for the two-dimensional neutral-type Cohen–Grossberg BAM neural networks. Hence, in this paper, our purpose is to investigate the stability of two-dimensional neutral-type Cohen–Grossberg BAM neural networks. The first objective is to construct mathematical models to illustrate the two-dimensional structure and the neutral-type delays in Cohen–Grossberg BAM neural networks. Then, a sufficient condition is given to achieve the stability of two-dimensional neutral-type continuous Cohen–Grossberg BAM neural networks. Finally, simulation results are given to illustrate the usefulness of the developed criteria.

Keywords

Two-dimensional neutral-type Cohen–Grossberg BAM neural networks Global asymptotic stability Inequality technique Lyapunov functional 
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Notes

Acknowledgments

This work was jointly supported by the National Natural Science Foundation of China under Grant No. 61203146, the China Postdoctoral Fund under Grant No. 2013M541589, the Jiangsu Postdoctoral Fund under Grant No. 1301025B, and the Scientific Research Starting Project of SWPU under Grant Nos. 2014QHZ037.

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Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  1. 1.Department of Mathematics, College of ScienceSouthwest Petroleum UniversityChengduChina
  2. 2.Department of MathematicsSoutheast UniversityNanjingChina
  3. 3.Department of Mathematics, Faculty of ScienceKing Abdulaziz UniversityJeddahSaudi Arabia

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