TY - GEN
T1 - Synchronization and Adaptive Control for Coupled Fractional-Order Reaction-Diffusion Neural Networks with Multiple Weights
AU - Du, Xinyu
AU - Wang, Jinliang
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In this paper, a type of coupled fractional-order reaction-diffusion neural networks (CFORDNNs) with multiple weights is presented. By utilizing Laplace inverse transform and some inequality techniques, a sufficient condition is given to make sure the proposed network model is synchronized. On the other hand, in order to guarantee the synchronization of the network model, an appropriate adaptive control strategy is also developed. Finally, the effectiveness of the proposed adaptive control scheme is substantiated by employing a numerical example and its simulation results.
AB - In this paper, a type of coupled fractional-order reaction-diffusion neural networks (CFORDNNs) with multiple weights is presented. By utilizing Laplace inverse transform and some inequality techniques, a sufficient condition is given to make sure the proposed network model is synchronized. On the other hand, in order to guarantee the synchronization of the network model, an appropriate adaptive control strategy is also developed. Finally, the effectiveness of the proposed adaptive control scheme is substantiated by employing a numerical example and its simulation results.
KW - anti-synchronization
KW - complex-valued neural networks
KW - delayed memristive neural networks
KW - multi-weighted
UR - https://www.scopus.com/pages/publications/85123439031
U2 - 10.1109/ICNC52316.2021.9608713
DO - 10.1109/ICNC52316.2021.9608713
M3 - 会议稿件
AN - SCOPUS:85123439031
T3 - 2021 International Conference on Neuromorphic Computing, ICNC 2021
SP - 345
EP - 350
BT - 2021 International Conference on Neuromorphic Computing, ICNC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Neuromorphic Computing, ICNC 2021
Y2 - 15 October 2021 through 17 October 2021
ER -