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**Exponential stability of continuous-time and discrete-time bidirectional associative memory networks with delays.**
*(English)*
Zbl 1062.68102

Summary: First, convergence of continuous-time Bidirectional Associative Memory (BAM) neural networks are studied. By using Lyapunov functionals and some analysis technique, delay-independent sufficient conditions are obtained for the networks to converge exponentially toward the equilibrium associated with the constant input sources. Second, discrete-time analogues of the continuous-time BAM networks are formulated and studied. It is shown that the convergence characteristics of the continuous-time systems are preserved by the discrete-time analogues without any restriction imposed on the uniform discretionary step size. An illustrative example is given to demonstrate the effectiveness of the obtained results.

### MSC:

68T05 | Learning and adaptive systems in artificial intelligence |

34K20 | Stability theory of functional-differential equations |

93D30 | Lyapunov and storage functions |

### Keywords:

Lyapunov functionals
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\textit{J. Liang} and \textit{J. Cao}, Chaos Solitons Fractals 22, No. 4, 773--785 (2004; Zbl 1062.68102)

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DOI

### References:

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