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Each network is trained using a stochastic approximation method, which can be viewed as a fast alternative to back-propagation based algorithm.
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The developed weight adaptive laws use a stochastic approximation algorithm.
Adding the random effect can be implemented with Monolix, the standard software for mixed-effect modeling in a population context that uses a stochastic approximation of EM (SAEM) algorithm to simulate the unobserved individual parameters [ 33].
In short, unless one thinks the brain uses a stochastic approximation scheme to make optimal decisions on the basis of a single trial or sequence, there is no reason to invoke RPE.
Using a stochastic simulation experiment we also confirm that the asymptotic Gaussian approximation holds.
This implies using a stochastic imputation.
When simulating hybrid systems, and thus unavoidably introducing some numerical error, a progressive tracking of this error can be exploited to discern the properties of the system, i.e., it can be used to introduce a stochastic approximation of the original hybrid system, the simulation of which would give a more complete representation of the possible trajectories of the system.
Using an accelerated stochastic approximation method [24], we obtained a rough estimate of suppressed stimulus Michelson contrast that enabled around 75% correct localization.
In the first phase we employed an adaptive staircase to manipulate stimulus contrast using an accelerated stochastic approximation algorithm as described by Kesten (1958).
A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations.
A stochastic approximation method was presented in order to effectively identify the required control parameters.
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