文摘
This paper studies the mean-square exponential input-to-state stability of delayed Cohen–Grossberg neural networks with Markovian switching. By using the vector Lyapunov function and property of lsi5" class="mathmlsrc">lass="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S089360801630096X&_mathId=si5.gif&_user=111111111&_pii=S089360801630096X&_rdoc=1&_issn=08936080&md5=3f5486f4a39f6b33a820afde8eab1396" title="Click to view the MathML source">Mlass="mathContainer hidden">lass="mathCode">-matrix, two generalized Halanay inequalities are established. By means of the generalized Halanay inequalities, sufficient conditions are also obtained, which can ensure the exponential input-to-state stability of delayed Cohen–Grossberg neural networks with Markovian switching. Two numerical examples are given to illustrate the efficiency of the derived results.