Improved delay-dependent stability for neural networks with mixed time-varying delays

This paper proposes improved delay-dependent stability criteria for neural networks with mixed time-varying delays as well as generalized activation functions. By constructing a novel Lyapunov functional and using Jensen inequality, improved stability criteria are derived to guarantee the globally asymptotic stability of the delayed neural networks. The criteria improve over some existing ones in that they have fewer matrix variables yet less conservatism, which is established theoretically. A numerical example is given to show the advantages of the proposed method in effectiveness and conservativeness.