Method for controller synthesis is proposed as a combined form of timevariant input-output linearization method with parameter estimation.
The idea is to be able to apply this new ACO method with parameter adaptation to a wide variety of problems without the need of finding the best parameters for each particular problem.
In addition, note that the saturation root mean square, average error and standard deviation decrease as we go as the following methods are used: (1) conventional methods to (2) CAPE method with parameter (a) equals to one, (3) 3D method, and (4) CAPE with, a, variable.
To simplify this model and find the features with the most predictive power we then used MAXR (Maximum R Improvement) selection method, with parameter STOP = 3, to determine the best 3 variable model for predicting p63 occupancy.
For the complete set of SNPs, the tan-correction method with parameter n = 0.5 performed best with a 90% quantile (median) error of 0.122 0.0422) compared to 0.25 (0.138) using uncorrected RAS values obtained from the Birdseed2 calling algorithm.
Since respondent age was missing for approximately 5% of the study sample, the logistic regression analyses were performed using a multiple imputation approach (10 imputed datasets constructed using Markov Chain Monte Carlo method) with parameter estimates adjusted using Rubin's rules [ 6].
The stability equation method together with parameter plane method provides a useful tool for the establishment of necessary conditions to sustain a limit cycle directly in the constructed controller coefficient plane.
In this study, we applied the representative methods PLINK (with parameter "epistasis") [ 10], PLINK-fast (PLINK with parameter "fast-epistasis"), BOOST [ 11], EDCF [ 12], and SIXPAC [ 22] on the simulation datasets.
A bivariate rational interpolation method with parameters was created in an earlier work which was based on function values only.
For J48-GA feature selection GeneticSearch was the search method with parameters: crossoverProb = 0.8, maxGeneration = 100, mutationProb = 0.01, and Population size = 100, as usually implemented [24, 26].
