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In this paper we consider the kernel estimators of a distribution functiondefined by the stochastic approximation algorithm when the observation arecontamined by measurement errors. It is well known that this estimators dependsheavily on the choice of a smoothing parameter called the bandwidth. We proposea specific second generation plug-in method of the deconvolution kerneldistribution estimators defined by the stochastic approximation algorithm. Weshow that, using the proposed bandwidth selection and the stepsize whichminimize the MISE (Mean Integrated Squared Error), the proposed estimator willbe better than the classical one for small sample setting when the errorvariance is controlled by the noise to signal ratio. We corroborate thesetheoretical results through simulations and a real dataset.

Bandwidth selection in deconvolution kernel distribution estimators defined by stochastic approximation method with Laplace errors