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

Ke Chen and Chenfu Yi

Applied Mathematics and Computation, 2016, vol. 273, issue C, 969-975

Abstract: 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.

Keywords: Recursive neural dynamics; Online matrix inversion; Lyapunov stability theory; Steady-state error bound; Exponential convergence rate (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:273:y:2016:i:c:p:969-975

DOI: 10.1016/j.amc.2015.10.026

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