Pages that link to "Item:Q3690039"

The following pages link to Gradient procedures for stochastic approximation with dependent noise and their asymptotic behaviour (Q3690039):

Displaying 12 items.

• Stochastic approximation with long range dependent and heavy tailed noise (Q383264) (← links)
• Extremum seeking under stochastic noise and applications to mobile sensors (Q608424) (← links)
• Asymptotic properties of stochastic approximation procedures in the case of correlated noise (Q1082766) (← links)
• Convergence of stochastic approximation procedures with dependent noise (Q1262667) (← links)
• Asymptotic properties of quasi-optimal algorithms of stochastic approximation under unknown density of noise (Q1287252) (← links)
• Strong points of weak convergence: A study using RPA gradient estimation for automatic learning (Q1301439) (← links)
• Estimation of the minimum point of an unknown function observed in the presence of dependent noise (Q1320661) (← links)
• Smooth dependence and the rate of convergence of the stochastic algorithm maximizing Gaussian probability (Q1324878) (← links)
• Algorithmes stochastiques à bruit dépendant (Dependent noise for stochastic algorithms). (Q1415531) (← links)
• Robust identification under correlated and non-Gaussian noises: WMLLM procedure (Q2289050) (← links)
• Complete convergence of stochastic approximation algorithm in ℝ^<i>d</i>under random noises (Q2816634) (← links)
• (Q5461899) (← links)