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 and defined on the basis of the error function which includes three appropriate matrices A,F,G. The 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.
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