Anti-Periodic Synchronization of Clifford-Valued Neutral-Type Recurrent Neural Networks With D Operator
Jin Gao, Lihua Dai · IEEE Access · 2022
In this paper, a class of Clifford-valued neutral-type recurrent neural networks with$D$operator is explored. By using non-decomposition method and the Banach fixed point theorem, we obtain several sufficient conditions for the existence of anti-periodic solutions for Clifford-valued neutral-type recurrent neural networks with$D$operator. By using the proof by contradiction and inequality techniques, we obtain the global exponential synchronization of anti-periodic solutions for Clifford-valued neutral-type recurrent neural networks with$D$operator. Finally, we give one example to illustrate the feasibility and effectiveness of main results.