Sentence examples for estimation of gradient from inspiring English sources

Nevertheless, the scale space estimation of derivatives allows an accurate and noise robust estimation of gradient and curvatures at different scales.

We propose the iterative stochastic ensemble method (ISEM) as a general method for parameter estimation based on stochastic estimation of gradients using an ensemble of directional derivatives.

Estimation of the gradient is a key issue in extremum-seeking methods.

For example, circular differential edge operators are effective in minimising the angular error in the estimation of image gradient direction.

This estimator is then differentiated by means of the score function approach which enables the estimation of the gradient of the failure probability without any additional call to the limit-state function (nor its Kriging emulator).

The drawback of this algorithm lies in the estimation of the gradient vector of the limit-state function, which is often carried out by finite differences for non-explicit functions, resulting from the Finite Element Method for instance.

If the perturbation chosen in the estimation of the gradient vector gives a variation of the output lower than the numerical accuracy of the limit-state function, the algorithm could lead to erroneous results or even not converge.

An LMS algorithm uses instantaneous values for the estimation of a gradient, and an NLMS algorithm normalizes the step size parameter by the signal power.

As a specific case of application, we evaluated the estimation of the gradient and unevenness of sidewalks by machine learning from the sensor data during the passage of strollers, verifying the effectiveness of this technique.

As a specific application case, we evaluated the estimation of the gradient and unevenness of sidewalks by machine learning from the sensor data during the passage of strollers, and verified the effectiveness of this method.

As a specific application case, we evaluate the estimation of the gradient and the unevenness of sidewalks through machine learning from the sensor information and users' subjective information during the passage of strollers to show the effectiveness of this technique.