regression Merging observations in Gaussian Process Cross
I am using Gaussian process GP for regression. In my problem it is quite common for two or more data points vec x ^ 1 vec x ^ 2 ldots to be close to each other relatively to the length scales of the problem. Also observations can be extremely noisy.
Reinforcement learning with Gaussian processes for condition
As a general nonparametric model Gaussian process regression gains a reputation for its universality and good utilization of data which is easy to implement as well Ebden et al. 2008 . Gaussian processes have been widely adopted for modeling stochastic processes in reliability and maintenance studies.
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PDF Appli ion of Gaussian Process Regression for bearing
Further Gaussian Process Regression has been used to make the prognosis of degradation trend in bearings with a 95 confidence interval remove outliers from confidence value and estimate the
Gaussian ProcessBased Response Surface Method for Slope
A new response surface method RSM for slope reliability analysis was proposed based on Gaussian process GP machine learning technology. The method involves the approximation of limit state function by the trained GP model and estimation of failure probability using the firstorder reliability method FORM . A small amount of training samples were firstly built by the limited equilibrium
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Prediction of surface residual stress in end milling with
The Pcc value of 0.9436 and the R2 value of 0.9137 indi e the high reliability and robustness of the applied approach Furthermore Gaussian process regression has a better performance in terms of quantitative evaluation indi ors than other machine learning algorithms such as SVR AdaBoost and ANN.
Prediction of building electricity usage using Gaussian
The Gaussian Process Regression algorithms can be applied to the electrical energy predictions with different inputs such as different weather conditions and/or occupancy activities and because of the relatively steady schedules the most accurate results are obtained in the office buildings energy use prediction.
Framework of airfoil max lifttodrag ratio prediction using
In this paper a novel framework is proposed involving the Gaussian process regression and a hybrid feature mining process. The aim is to use the new framework to evaluate the maximum lifttodrag ratio of given airfoils under a turbulent flow condition where the Reynolds number is around 100000.
Gaussian Process Regression Technique to Estimate the Pile
A commonlyencountered problem in foundation design is the reliable prediction of the pile bearing capacity PBC . This study is planned to propose a feasible soft computing technique in this field i.e. the Gaussian process regression GPR for the PBC estimation. The established database includes 296 number of dynamic pile load test in the field where the most influential factors on the PBC