Multistability of equilibrium points and periodic solutions for Clifford‐valued memristive Cohen–Grossberg neural networks with mixed delays

Abstract

Based on the relevant theories of M‐matrix and the Lyapunov stability technique, this paper investigates the multistability of equilibrium points and periodic solutions for Clifford‐valued memristive Cohen–Grossberg neural networks. With the help of Cauchy convergence criterion, the exponential stability inequality is derived. The system has locally exponentially stable equilibrium points and periodic solutions, which greatly increases the number of solutions compared with the existing Cohen–Grossberg neural networks, where the is a Clifford‐valued and there is no limitation of linearity and monotonicity for activation functions. Furthermore, the attraction basins of stable periodic solutions are obtained, and it is proved that the basins can be enlarged. It is worth mentioning that the results can also be used to discuss the multistability of equilibrium points, periodic solutions, and almost periodic solutions for real‐valued, complex‐valued, and quaternion‐valued memristive Cohen–Grossberg neural networks. Finally, two numerical examples with simulations are taken to verify the theoretical analysis.