System decomposition method-based exponential stability of Clifford-valued BAM delayed neural networks

This study explores new theoretical results for the global exponential stability of bidirectional associative memory delayed neural networks in Clifford domain. By considering time-varying delays, a general class Clifford-valued is formulated, which encompasses real-, complex-, and quaternion-valued network models as special cases. To analyze stability, we first decompose considered n -dimensional into 2 m real-valued networks, avoids inconvenience caused by non-commutativity multiplication numbers. Subsequently, establish sufficient conditions to guarantee existence, uniqueness, equilibrium points constructing Lyapunov functional applying homeomorphism theory. Finally, provide numerical example accompanied simulation illustrate validity obtained results. The present remain valid even when degenerate networks.