نتایج جستجو برای: genetic algorithm multiple linear regression
تعداد نتایج: 2493640 فیلتر نتایج به سال:
Using monthly test day records the genetic parameters of Iranian Holstein cattle in first lactation were studied. Data of 277400 test-day milk records from 65320 cows and 2210 sires were analyzed by an animal random regression model using restricted maximum likelihood methodology. The model included herd-test-date, interaction between year-season of calving, days in milk (linear and quadratic) ...
This paper examines the performance of seven neural network architectures in classifying and detecting novel events contained within data collected from turbine sensors. Several different multi-layer perceptrons were built and trained using back propagation, conjugate gradient and Quasi-Newton training algorithms. In addition, Linear networks, Radial Basis Function networks, Probabilistic netwo...
The effects of genetic variants on warfarin dosing vary among different ethnic groups, especially in the Chinese population. The objective of this study was to recruit patients through a rigorous experimental design and to perform a comprehensive screen to identify gene polymorphisms that may influence warfarin dosing in northern Han Chinese patients with mechanical heart valve replacement. Con...
there are many real problems in which multiple responses should be optimized simultaneously by setting of process variables. one of the common approaches for optimization of multi-response problems is desirability function. in most real cases, there is a correlation structure between responses so ignoring the correlation may lead to mistake results. hence, in this paper a robust approach based ...
In this work, a Genetic Algorithm boosted Least Square Support Vector Machine model by a set of linear equations instead of a quadratic program, which is improved version of Support Vector Machine model, was used for estimation of 98 pure compounds second virial coefficient. Compounds were classified to the different groups. Finest parameters were obtained by Genetic Algorithm method ...
INTRODUCTION Today’s genetic evaluation schemes involve models comprising multiple, correlated additive genetic effects for each animal. These can be multi-trait (MT) models or random regression (RR) models which model trajectories in traits recorded repeatedly per animal through a set of RR coefficients. Often we are interested in linear functions of the resulting breeding value (EBV) estimate...
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