Robust Stability of Cohen–Grossberg Neural Networks via State Transmission Matrix

Abstract

This brief is concerned with the global robust exponential stability of a class of interval Cohen-Grossberg neural networks with both multiple time-varying delays and continuously distributed delays. Some new sufficient robust stability conditions are established in the form of state transmission matrix, which are different from the existing ones. Furthermore, a sufficient condition is also established to guarantee the global stability for this class of Cohen-Grossberg neural networks without uncertainties. Three examples are used to show the effectiveness of the obtained results.

A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks

“…The expressions of the stability criteria are different due to different analysis and proof methods, such as M-matrix [16], [28], [38], [149], [182], [191], [301]; algebraic inequality [20], [33], [38]- [40], [85], [143], [166], [174], [188], [189], [203], [229], [299], [306], [326]; matrix norm [273], [290], [315]; additive diagonal stability [7], [109], [156]; LMI [39], [43], [94], [121], [137]- [139], [162], [177], [185], [294], [309], [310], [312], [313], [315], [323]; matrix measure [63], [225]; and spectral radius [259],…”

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