Global convergence for Cohen-Grossberg neural networks with discontinuous activation functions
Summary: Cohen-Grossberg neural networks with discontinuous activation functions are considered. Using \(M\)-matrices and a generalized Lyapunov-like approach, the uniqueness is proved for state solutions and corresponding output solutions and equilibrium points and corresponding output equilibrium points of the considered neural networks. Moreover, global exponential stability of equilibrium points is obtained. Furthermore, by the contraction mapping principle, uniqueness and global exponential stability of the limit cycles are proved. Finally, an example is given to illustrate the effectiveness of the obtained results.
scientific article; zbMATH DE number 6134015
scientific article