Among them, evolutionary polynomial regression (EPR) is one that can operate on large quantities of data in order to capture nonlinear and complex interactions between the variables of the system. In this study, the evolutionary data-mining technique was used to derive new PTFs and different ...
摘要: Establishes an empirical formula relating temperature to saturated humidity ratio under standard atmospheric pressure by means of polynomial regression. Analyzes the calculating error in common engineering temperature range, and the error is less than 2.1 percent....
With a bilevel regression model, Miller et al. [25] estimated the number of FtB passengers and calculated the shortage of cumulative capacity. However, the studies discussed above did not consider transfer passengers in a large metro network. In addition to FtB, it is also important to ...
A new data-mining approach is presented for modelling of the stress鈥搒train and volume change behaviour of unsaturated soils considering temperature effects. The proposed approach is based on the evolutionary polynomial regression (EPR), which unlike some other data-mining techniques, generates a ...
(GA) engine to find the best form of function structure; second, it performs a least squares regression to find adjustable parameters, for each combination of inputs (terms in the polynomial structure).Findings - EPR-based models were capable of generalising the training to predict the behaviour...
The program also performs low- and high-degree polynomial curve fit up to six-degree polynomial to create the desired model. The models' accuracy was validated using the rest of the datasets, and their efficiency was tested against some commonly used correlations utilizing average absolute relative...
Next, adopting genetic programming and the least square method in the framework of the evolutionary polynomial regression technique, high-accuracy predictive equations are developed for the estimation of rumax. Based on the results of a three-dimensional, graphical, multiple-variable paramet...
This paper deals with robust regression and subspace estimation and more precisely with the problem of minimizing a saturated loss function. In particular, we focus on computational complexity issues and show that an exact algorithm with polynomial time-complexity with respect to the number of data ...
6.2. Evolutionary Polynomial Regression Modeling (EPR) The evolutionary polynomial regression (EPR) modeling approach known as a symbolic grey box technique can make structured model expressions for a given dataset. It is a hybrid regression method, which employs the genetic programming symbolic regressio...
6.2. Evolutionary Polynomial Regression Modeling (EPR) The evolutionary polynomial regression (EPR) modeling approach known as a symbolic grey box technique can make structured model expressions for a given dataset. It is a hybrid regression method, which employs the genetic programming symbolic regressio...