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

The aim of this paper is to present a a 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 invariant measures of dynamics, such as correlation dimension and 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 voltage reference element (VRE). A control loop is implemented to reduce the interference sensitivity of the reference source which contributes to enhancement of the robustness of the system and thereby the stability of the reference voltage     

Deterministic predictive models for DC voltage reference sourcecontrol

In this paper, we use predictive models for voltage correction in a high-precision DC voltage reference source (DCVRS) based on Zener diodes. Voltage reference elements, which compose the DCVRS, are improved by implementing a control loop with built-in predictors. Thus, the sensitivity of the system is reduced and, thereby, the stability of the DCVRS is improved. The predictive abilities of two different paradigms, neural-network-based predictors and difference equation predictors obtained by equation discovery system LAGRAMGE, are compared