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

The aim of this paper is to present a non-typical application of predictive models for voltage correction in a high precision solid-state DC voltage reference source (DCVRS). Several types of neural networks are trained until the correlation dimension and the leading Lyapunov exponent of the predicted signals reach the values of the same invariant measures of the original signals. The predictive models are used as a segment in the software controlled VRE. A control loop is implemented to reduce the sensitivity of the reference source which contributes to enhancement of the robustness of the system and thereby the stability of the reference voltage.