Predictive neural networks control in the high accuracy DC voltage reference source

In the paper we compare the predictive abilities of several types of neural networks in order to use them for voltage correction in a high precision solid state DC voltage reference source (DCVRS). The observed time series are characterised as outputs of nonlinear dynamical system. Networks are trai...

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Main Authors: Nancovska, I., Hudoklin, D., Fefer, D., Jeglic, A.
Format: Conference Proceeding
Language:English
Subjects:
Feedback loop
Finite impulse response filter
Intelligent networks
Laboratories
Neural networks
Particle measurements
Predictive models
Process control
Stability
Voltage control
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creator Nancovska, I.
Hudoklin, D.
Fefer, D.
Jeglic, A.
description In the paper we compare the predictive abilities of several types of neural networks in order to use them for voltage correction in a high precision solid state DC voltage reference source (DCVRS). The observed time series are characterised as outputs of nonlinear dynamical system. Networks are trained until the correlation dimension and the leading Lyapunov exponent of the predicted signals reaches the values of the same invariant measures of the observed system.
doi_str_mv 10.1109/IMTC.1998.679789
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Feedback loop
Finite impulse response filter
Intelligent networks
Laboratories
Neural networks
Particle measurements
Predictive models
Process control
Stability
Voltage control
title Predictive neural networks control in the high accuracy DC voltage reference source
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