Applied Mathematics and Mechanics

, Volume 26, Issue 3, pp 327–335 | Cite as

Global dynamics of delayed bidirectional associative memory (BAM) neural networks

  • Zhou Jin
  • Liu Zeng-rong
  • Xiang Lan


Without assuming the smoothness, monotonicity and boundedness of the activation functions, some novel criteria on the existence and global exponential stability of equilibrium point for delayed bidirectional associative memory (BAM) neural networks are established by applying the Liapunov functional methods and matrix-algebraic techniques. It is shown that the new conditions presented in terms of a nonsingular M matrix described by the networks parameters, the connection matrix and the Lipschitz constant of the activation functions, are not only simple and practical, but also easier to check and less conservative than those imposed by similar results in recent literature.

Key words

bidirectional associative memory (BAM) neural network global exponential stability Liapunov function 

Chinese Library Classification

O175 TN911 

2000 Mathematics Subject Classification

34K20 34K35 92B20 


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Copyright information

© Editorial Committee of Appl. Math. Mech. 2005

Authors and Affiliations

  1. 1.Institute of Applied MathematicsHebei University of TechnologyTianjinP.R. China
  2. 2.Institute of MathematicsFudan UniversityShanghaiP. R. China
  3. 3.Department of MathematicsShanghai UniversityShanghaiP.R. China
  4. 4.Department of PhysicsHebei University of TechnologyTianjinP. R. China

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