TY - JOUR
T1 - Varying-parameter Zhang neural network for approximating some expressions involving outer inverses
AU - Stanimirović, Predrag S.
AU - Katsikis, Vasilios N.
AU - Zhang, Zhijun
AU - Li, Shuai
AU - Chen, Jianlong
AU - Zhou, Mengmeng
PY - 2019/1/1
Y1 - 2019/1/1
N2 - A varying-parameter ZNN (VPZNN) neural design is defined for approximating various generalized inverses and expressions involving generalized inverses of complex matrices. The proposed model is termed as CVPZNN(A, F, G) and defined on the basis of the error function which includes three appropriate matrices A,F,G. The CVPZNN(A, F, G) evolution design includes so far defined VPZNN models for computing generalized inverses and also generates a number of matrix expressions involving these generalized inverses. Global and super-exponential convergence properties of the proposed model as well as behaviour of its equilibrium state are investigated. Main contribution of the defined model is its generality. Most important particular cases of the defined model are presented in order to show this fact explicitly. Presented simulation results illustrate generality and effectiveness of the discovered ZNN evolution design.
AB - A varying-parameter ZNN (VPZNN) neural design is defined for approximating various generalized inverses and expressions involving generalized inverses of complex matrices. The proposed model is termed as CVPZNN(A, F, G) and defined on the basis of the error function which includes three appropriate matrices A,F,G. The CVPZNN(A, F, G) evolution design includes so far defined VPZNN models for computing generalized inverses and also generates a number of matrix expressions involving these generalized inverses. Global and super-exponential convergence properties of the proposed model as well as behaviour of its equilibrium state are investigated. Main contribution of the defined model is its generality. Most important particular cases of the defined model are presented in order to show this fact explicitly. Presented simulation results illustrate generality and effectiveness of the discovered ZNN evolution design.
KW - generalized inverses
KW - super-exponential convergence
KW - time-varying matrix
KW - varying-parameter ZNN design
KW - Zhang neural network
UR - http://www.scopus.com/inward/record.url?scp=85063592531&partnerID=8YFLogxK
U2 - 10.1080/10556788.2019.1594806
DO - 10.1080/10556788.2019.1594806
M3 - Journal article
AN - SCOPUS:85063592531
SN - 1055-6788
JO - Optimization Methods and Software
JF - Optimization Methods and Software
ER -