New results on finite‐time stability of fractional‐order Cohen–Grossberg neural networks with time delays
Feifei Du, Jun‐Guo Lu
Abstract
Abstract The finite‐time stability (FTS) of fractional‐order delayed Cohen–Grossberg neural networks (FODCGNNs) with the order ℘ ∈ (1, 2) is investigated in this study. Based on the fractional‐order delayed Gronwall inequality (FODGI), a new sufficient condition to guarantee the FTS of FODCGNNs is established, which reduces the conservation of the existing criterion. Finally, one numerical example is exhibited to illustrate the effectiveness and less conservativeness of the obtained results.
Topics & Concepts
Artificial neural networkStability (learning theory)Order (exchange)Control theory (sociology)MathematicsApplied mathematicsComputer scienceArtificial intelligenceEconomicsMachine learningFinanceControl (management)Neural Networks Stability and Synchronizationstochastic dynamics and bifurcationNonlinear Dynamics and Pattern Formation