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The diagnosability and conditional diagnosability of many interconnection networks have been studied using various diagnosis models.
So, designing accurate and robust clinical diagnosis models for such datasets is a challenging task in data mining.
Later, adaptive and distributed diagnosis models and algorithms appeared that allowed the theory to be implemented to monitor computer networks [49].
A critical issue in the applications of cognitive diagnosis models (CDMs) is how to construct a feasible test that achieves the optimal statistical performance for a given purpose.
Most diagnosis models assume that a fault-free unit executes tests and reports test results reliably, i.e., a fault-free tester can always correctly determine whether the tested unit is faulty or fault-free [15, 16].
We use a Polynomial Regression approach for tuning the performance of an ensemble of low-fidelity ODE diagnosis models such that we achieve the target of embedded processing limits.
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The software itself is based on the "organizational culture diagnosis" model developed by Charles O'Reilly from the Graduate School of Business at Stanford University.
The fault diagnosis model of multiple faults is given based on evidence theory.
The first system-level diagnosis model, the PMC model [46], was proposed 50 years ago.
Therefore, SVM is particularly suitable for establishing a fault diagnosis model.
DGRP is based on a distributed and adaptive system-level diagnosis model.
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