TY - GEN
T1 - Nonlinearity Activated Noise-Tolerant Zeroing Neural Network for Real-Time Varying Matrix Inversion
AU - Duan, Wenhui
AU - Jin, Long
AU - Hu, Bin
AU - Lu, Huiyan
AU - Liu, Mei
AU - Li, Kene
AU - Xiao, Lin
AU - Yi, Chenfu
N1 - Publisher Copyright:
© 2018 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2018/10/5
Y1 - 2018/10/5
N2 - Real-time varying matrix inversion is widely used in the fields of science and engineering, e.g., image processing, signal processing and robot technology, etc. In this paper, a nonlinearity activated noise-tolerant zeroing neural network (NANTZNN) is constructed and employed to the time-dependent matrix inversion in the noisy environment. Compared with the gradient approach related neural network (GNN) and the existing noise-tolerant zeroing neural network (NTZNN), the proposed NANTZNN model is activated by specially-constructed nonlinear activation functions, and thus possesses the better convergence performance. Additionally, theoretical analyses are provided to guarantee the convergence of the proposed model. Finally, simulations are conducted to demonstrate the efficiency and superiority of the NANTZNN model for time-dependent matrix inversion, as compared with the NTZNN model.
AB - Real-time varying matrix inversion is widely used in the fields of science and engineering, e.g., image processing, signal processing and robot technology, etc. In this paper, a nonlinearity activated noise-tolerant zeroing neural network (NANTZNN) is constructed and employed to the time-dependent matrix inversion in the noisy environment. Compared with the gradient approach related neural network (GNN) and the existing noise-tolerant zeroing neural network (NTZNN), the proposed NANTZNN model is activated by specially-constructed nonlinear activation functions, and thus possesses the better convergence performance. Additionally, theoretical analyses are provided to guarantee the convergence of the proposed model. Finally, simulations are conducted to demonstrate the efficiency and superiority of the NANTZNN model for time-dependent matrix inversion, as compared with the NTZNN model.
KW - Bounded random noise
KW - Computer simulation verification
KW - Noise environment
KW - Nonlinearity activated noise-tolerant zeroing neural network (NANTZNN)
KW - Real-time varying matrix inversion
UR - http://www.scopus.com/inward/record.url?scp=85056105852&partnerID=8YFLogxK
U2 - 10.23919/ChiCC.2018.8483798
DO - 10.23919/ChiCC.2018.8483798
M3 - Conference contribution
AN - SCOPUS:85056105852
T3 - Chinese Control Conference, CCC
SP - 3117
EP - 3122
BT - Proceedings of the 37th Chinese Control Conference, CCC 2018
A2 - Chen, Xin
A2 - Zhao, Qianchuan
PB - IEEE Computer Society
T2 - 37th Chinese Control Conference, CCC 2018
Y2 - 25 July 2018 through 27 July 2018
ER -