A Fitting Robot for Variational Analysis
Alan \'O Cais, Derek Leinweber, Selim Mahbub, Tony Williams
TL;DR
This paper introduces an automated robot algorithm designed to optimize the variational analysis process by exploring parameter space and reducing human intervention in data fitting.
Contribution
The novel robot algorithm automates the variational analysis fitting process, enhancing reliability and efficiency in extracting distinct states from correlator data.
Findings
Successfully automates the variational analysis fitting process.
Increases reliability of state extraction from correlator data.
Reduces human involvement in data fitting procedures.
Abstract
We develop a robot algorithm to maximise the number of distinct states reliably extracted from correlator data using the variational analysis method. The robot explores the variational parameter space and attempts to remove, as far as possible, the human element from the fitting of the subsequent orthogonalised data.
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Taxonomy
TopicsModular Robots and Swarm Intelligence · Medical Image Segmentation Techniques · Manufacturing Process and Optimization