Varying-parameter Zhang neural network for approximating some expressions involving outer inverses. (1st November 2020)

• Record Type: Journal Article
• Authors: Stanimirović, Predrag S.; Katsikis, Vasilios N.; Zhang, Zhijun; Li, Shuai; Chen, Jianlong; Zhou, Mengmeng
• Is Part Of: Optimization methods and software. Volume 35:Number 6(2020)
• Page Start: 1304
• Page End: 1330
• Publication Date: 2020-11-01
• Subjects: Zhang neural network -- generalized inverses -- super-exponential convergence -- time-varying matrix -- varying-parameter ZNN design; 68T05 -- 15A09 -- 65F20; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2019.1594806
• Languages: English
• ISSNs: 1055-6788

Abstract

Abstract : 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 C V P Z N N ( A, F, G ) and defined on the basis of the error function which includes three appropriate matrices A, F, G . The C V P Z N N ( 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.