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To obtain such models the algorithm presented herein can be used as a starting point.
Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes.
To reduce the computation time required to fit the PLSR models, the algorithm to find the optimal number of components was modified as follows.
In order to find relevant models, the algorithm performs several searches based on different data, then performs an inclusive disjunction (OR) to combine the results.
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To tackle the above problems of the SVM model, the algorithm proposed in this paper is optimized from three aspects.
Based on the standard SVM model, the algorithm of cluster analysis is merged and the penalty factor is corrected.
In order to calibrate the channel model, the algorithm requires multiple iterative computations, so large amount of energy is consumed.
Moreover, in this model, the algorithm is simple and intuitive and offers good interpretation for the results.
Without taking into account the weighted model, the algorithm is not suitable for searching islanding cutsets of power systems.
Further details about the model, the algorithm and a MatLab implementation are provided in http://bioinf.boku.ac.at/alexp/robmca.html.html
After assembling the single best-scoring core model, the algorithm proceeds as for a single template (Section 2.1.4).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com