Global Lagrange stability for inertial neural networks with mixed time-varying delays

This paper concerns with the global Lagrange stability of inertial neural networks with discrete and distributed time-varying delays. By choosing a proper variable substitution, the inertial neural networks can be rewritten as a first-order differential system. Based on the Lyapunov functional method, inequality techniques and analytical method, several sufficient conditions are derived to guarantee the global exponential stability of the inertial neural networks in Lagrange sense. Meanwhile, the global exponential attractive set is also given. Simulation results demonstrate the effectiveness of the theoretical results. HighlightsNeural networks with the inertial terms are discussed.Lagrange stability is investigated.Inertial neural networks with discrete and distributed time delay are studied.The neuron activation function discussed in the paper is neither bounded nor monotonically non-decreasing.

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