Case study of the predictive models used for stability improvement of the DC voltage reference source

The aim of this paper is to present a atypical application of predictive mo dels for voltage correction in a high-precision solid-state de voltage refe rence source (DCVRS), Several types of neural networks are trained until th e invariant measures of dynamics, such as correlation dimension and leading Lyapunov exponent of the predicted signals, reach the values of the same i nvariant measures of the original signals. The predictive models are used a s a segment in the software-controlled voltage reference element (VRE), A c ontrol loop is implemented to reduce the interference sensitivity of the re ference source which contributes to enhancement of the robustness of the sy stem and thereby the stability of the reference voltage.