The absolute magnitude of Dll4 concentration could not be predicted in the current model, based on existing rules (Table 3); these rules can be amended as experimental data becomes available.
We also compared our model based on generalized rules with the model described by the domain expert rules 5 (cf. Table 13).
From these fundamental mechanisms, a cognitive model based on behavioral rules has been developed to describe collective behaviors of a group of bubbles.
Here, we will propose to offer these non-registered users a natural inference model based on uncertainty rules that allows them to infer themselves their own recommendations.
A model based on fuzzy rules to simulate the behavior of the firms, is presented under the assumption of determined input parameters previously detected and an algorithm is developed to achieve the minimal structure of the model.
In this paper, we will employ approach based on the relation of indiscernibility and rough sets theory to study certain questions concerning the design of model based on if then rules, from low-level data including 36 parameters, one of them leptin.
We present a mathematical model based on behavioral rules derived from experiments.
Material properties of FG-CNTRC shells are assumed to be graded through the thickness and estimated according to an equivalent continuum model based on the rule of mixture.
A model based on the rule of mixtures has been presented to predict the split tensile strength of the specimens to compare the effect of nanoparticles on split tensile strength of the specimens.
Single-walled carbon nanotubes (SWCNTs) are selected as the reinforcement and the effective material properties of the FG-CNTRC plates are evaluated by an equivalent continuum model based on the rule of mixture.
A single-parameter random graph model based on this rule predicts numerous features of the cortical network: (1) the existence of a network core and the distribution of cliques, (2) global and local binary properties, (3) global and local weight-based communication efficiencies modeled as network conductance, and (4) overall wire-length minimization.
