In module one, we use a three-layered perceptron neural network that is presented by Amiri et al. (2015) for monitoring the multivariate-attribute process variability.
A multivariate technique, partial least squares (PLS) demonstrated a divergent Cg25-mediated network that differentiated temperamentally negative (NAS) from temperamentally positive (PAS) subjects providing a potential neural link between these specific combinations of trait affective styles and vulnerability to depression.
An optimization study was performed combining a multivariate experimental design and Neuronal Networks that included the following variables: the initial concentrations of H2O2, Fe(II) and oxalic acid (H2C2O4), temperature and solar power.
An optimization study was performed combining a multivariate experimental design and Neuronal Networks that included the following variables: initial concentrations of H2O2, catalyst Fe (II) and oxalic acid (H2C2O4), temperature and solar power.
An optimization study was performed combining a multivariate experimental design and neuronal networks that included the following variables: pH, temperature, solar power, air flow and initial concentrations of H2O2, Fe(II) and oxalic acid.
Analyses were conducted within a multivariate experimental design combined with neural networks that included the following variables: initial concentrations of tert-butyl alcohol, 1,4-benzoquinone, sodium azide and potassium iodide.
Multivariate image analysis using PCA permits the characterization of different networks that include brain areas changing brain activity in related to a given task and brain areas contributing to the function without necessarily changing their activity [ 28- 31, 67].
Temporal (Dynamic) multivariate networks consist of objects and relationships with a variety of attributes, and the networks change over time.
We investigated the robustness of this behavior to simultaneous perturbations in the network parameters using a novel multivariate approach that integrates global sensitivity analysis with decision-tree methods.
Hence, as has been suggested recently by Abrams et al. (2012), inconsistencies in the literature may be reconciled by the use of more sensitive multivariate methods that allow the identification of a wider intelligibility network.
