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.


68T05 Learning and adaptive systems in artificial intelligence
34K20 Stability theory of functional-differential equations
93D30 Lyapunov and storage functions
Full Text: DOI


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