Global exponential stability of Clifford-valued recurrent neural networks

This paper investigates global exponential stability of a class of Clifford-valued recurrent neural networks. By using Brouwer's fixed point theorem, the existence of the equilibrium point of Clifford-valued recurrent neural networks is studied. A sufficient condition of global exponential stability is given by the method of the Clifford-valued variation parameter and inequality technique. Compared with the previous methods, our method does not resort to any Lyapunov function which is not easy to construct.