Common asymptotic behavior of solutions and almost periodicity for discontinuous, delayed, and impulsive neural networks

Authors:
Walter Allegretto;Duccio Papini;Mauro Forti
Affiliations:
Department of Mathematics, University of Alberta, Edmonton, AB, Canada;Department of Information Engineering, University of Siena, Siena, Italy;Department of Information Engineering, University of Siena, Siena, Italy
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

The paper considers a general neural network model with impulses at a given sequence of instants, discontinuous neuron activations, delays, and time-varying data and inputs. It is shown that when the neuron interconnections satisfy an M-matrix condition, or a dominance condition, then the state solutions and the output solutions display a common asymptotic behavior as time t → +∞. It is also shown, via a new technique based on prolonging the solutions of the delayed neural network to -∞, that it is possible to select a unique special solution that is globally exponentially stable and can be considered as the unique global attractor for the network. Finally, this paper shows that for almost periodic data and inputs the selected solution is almost periodic; moreover, it is robust with respect to a large class of perturbations of the data. Analogous results also hold for periodic data and inputs. A by-product of the analysis is that a sequence of almost periodic impulses is able to induce in the generic case (nonstationary) almost periodic solutions in an otherwise globally convergent nonimpulsive neural network. To the authors' knowledge the results in this paper are the only available results on global exponential stability of the unique periodic or almost periodic solution for a general neural network model combining three main features, i.e., impulses, discontinuous neuron activations and delays. The results in this paper are compared with several results in the literature dealing with periodicity or almost periodicity of some subclasses of the neural network model here considered and some hints for future work are given.