OD target parameter was electrical efficiency for some rotation speeds.
We propose a targeted MLE that combines machine learning to smooth across the cells while targeting the fit towards the single valued target parameter of interest.
The development and validation of a new gasoline surrogate using laminar flame speed as a target parameter is presented.
Multiple-input multiple-output (MIMO) radars offer higher resolution, better target detection, and more accurate target parameter estimation.
As input parameters, neural network considered the reservoir temperature, fluid composition and injected gas composition and MMP as target parameter.
With the workflow or response surface model, the estimation and optimization of target parameter could be achieved.
For K-way multiplicative interaction being 0, the statistical target parameter is only defined when the probability on each of the 2^K cells is positive, and the MLE naturally suffers from the curse of dimensionality.
The target quantity is the probability that the vector is not equal to 0. We focus on models assuming a single constraint so that the target quantity is identified by a nonparametric statistical target parameter.
In these models, the target parameter was the thermal conductivity ratio, and the nanoparticle volume concentration, temperature and Al2O3 nanoparticle size were considered as the input (design) parameters.
As a target parameter we evaluated the radiation cross-talk, which is the amount of erroneously counted interactions from photons which have undergone Compton scattering in neighbouring crystals.
Model perturbations in the annealing procedure are made by drawing from a probability distribution for the target parameter conditioned to the geophysical data.
