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The "MYT approach" has been applied by Mani and Cooper (1999) for the variables identification.
Three key concerns frequently raised in the literature are: unobserved heterogeneity; omitted variables; identification problem.
This multi-agent system uses: multivariate control chart for abnormal detection, neural network for faults diagnosis, Bayesian network for variables identification and expert system for reconfiguration task.
Indeed, the literature suggests that prior outcomes often play a critical role in the plausibility of a "selection on observed variables" identification strategy.
D-PNN is a new type of neural network, which function approximation and dependence of variables identification is based on a generalization of data relations.
To reduce this number and to identify the relationship among the variables, the Bayesian networks have been applied for variables identification by Friedman (2000), Li et al.
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The underlying theory of the VI concept is that the state variables identifications (SVIDs) of process equipments can reflect the process quality effectively and loyally.
First is the issue of variable identification.
The IBNA sends the variable identification to the RA.
This agent simplifies the variable identification in the process.
The validity of these influence parameters is verified by an input variable identification method.
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