Robustness analysis of a hybrid of recursive neural dynamics for online matrix inversion

Robustness analysis on a recently-proposed hybrid recurrent neural dynamics.Global stability of the analysed model under large realisation error.Steady-state solution error bound is estimated.Global exponential convergence rate to the error bound is analysed. Encouraged by superior convergence performance achieved by a recently-proposed hybrid of recursive neural dynamics for online matrix inversion, we investigate its robustness properties in this paper when there exists large model implementation errors. Theoretical analysis shows that the perturbed dynamic system is still global stable with the tight steady-state bound of solution error estimated. Moreover, this paper analyses global exponential convergence rate and finite convergence time of such a hybrid dynamical model to a relatively loose solution error bound. Computer simulation results substantiate our analysis on the perturbed hybrid neural dynamics for online matrix inversion when having large implementation errors.