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Integrated modulation by multiple peptides, as opposed to the highly organised structure of cortical systems, appears to be a general feature of the limbic system [58], so the recent attempts to apply systems biology methodologies to advance our understanding of the multivariate control of GnRH secretion are increasingly important [59].
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Proposed criterion was applied to the multivariate controls of a distillation column, and a nonlinear biochemical reactor.
Proposed criterion was applied to the multivariate controls of a distillation column, and a non-linear chemical reactor, both reported in the literature.
Their general approach is now applied to a multivariate control chart instead of a univariate control chart.
It is recommended to apply multivariate control charts for the control of multiple product characteristics, but the measurement uncertainty can lead to incorrect decisions even in univariate case.
As a generalization of Shewhart-type control charts, most of multivariate control charts have been constructed based upon the multivariate normal distribution.
Our contribution is to determine the best combination of multivariate control charts, neural networks, Bayesian networks, expert systems.
So, to monitor successively the process, we suggest to use a software agent that can execute simultaneously a set of multivariate control charts and detect easily the process instability.
In this paper, we present the procedure to conduct the economic-statistical design of multivariate control charts for monitoring the process mean vector and covariance matrix simultaneously; i.e. to economically determine the optimum values of the three test parameters such that the statistical constraints (including the type I error probability and power) of the control chart can be satisfied.
We anticipate that a similar approach also can be used routinely for screening, batch control and similar analysis settings, for instance by using a multivariate control chart and determine presence of new peaks of significant intensity.
We have studied the sensitivity of three multivariate control charts to detect one-step permanent shift in any parameters of a mixed model linear profile.
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