Sentence examples for general regression process from inspiring English sources

The general regression process is first explained and illustrated with a study of the Marangoni effect.

The general regression process is first explained and then illustrated on different examples: a purely numerical test function, a limited angle electromagnetic actuator and a flexible mechanical hinge.

The general regression neural network (GRNN) proposed by Specht [40] is a nonlinear regression method that is able to process data with high mapping capability within a flexible network.

A general variance minimization method is presented to avoid the need for determination of various coefficients and exponents used in regression process.

This work presents a new approach, regression analysis, and develops a general regression model.

The general regression neural network (GRNN) performs non-linear regressions when the target variables are continuous.

As an illustration of this evolutionary symbolic regression process, the inverse kinematic models of the PUMA and the GMF Arc Mate are approximated before to apply the algorithm to general 6R manipulators.

A general regression neural network model was developed to predict the responses of lactose crystallinity and free fat content from the processor screw speed, process temperature, milk powder feed rate and lecithin addition rate.

We also combined the NARX model and the general regression neural network (GRNN) as a hybrid model to predict hysteretic behaviour of the SSD of which the damper design was hidden from the model training process.

This approach is formulated for a general regression model and, then is explored through several regression models.

A general regression neural network architecture was found to be the least suitable for grain classification.