Estimation of the parameters of sampled-data systems by means of stochastic approximation.
Stochastic approximation applied to sampled data system parameters including sampling interval
Bekey, G. A.↗
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Results for “stochastic approximation”
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Identifying solutions of distributed parameter systems by stochastic approximation
Stochastic approximation for identification of distributed parameter system solutions described by linear partial differential equations
Badavas, P. C.↗
Stochastic Approximation and Its Engineering Applications
Stochastic approximation and random error on convergence
Hampton, R. L. T.↗
A stochastic approximation scheme with accelerated convergence properties
Stochastic approximation scheme with accelerated convergence properties
Newbold, P. M.↗
Identification of distributed parameter systems by stochastic approximation.
Linear distributed parameter system identification by stochastic approximation, obtaining constant parameters sequentially
Badavas, P. C.↗
Identification of human operator models by stochastic approximation
Human operator models parameter estimation by stochastic approximation, considering continuous and sampled data models
Bekey, G. A.↗
A stochastic approximation algorithm for estimating mixture proportions
A stochastic approximation algorithm for estimating the proportions in a mixture of normal densities is presented. The algorithm is shown to converge to the true proportions in the case of a mixture of two normal densities.
Sparra, J.↗
A note on the maximum sample excursions of stochastic approximation processes.
Maximum sample excursions of Kiefer-Wolfowitz stochastic approximation processes
APPROXIMATION METHOD↗
Stochastic approximation algorithms for linear discrete-time system identification.
Stochastic approximation algorithms for identification of linear discrete time systems
Saridis, G. N.↗
Estimation of the parameters of sampled-data systems by stochastic approximation
Parameter estimation of sampled data control systems by stochastic approximation
Neal, C. B.↗
Stochastic approximation algorithms for linear discrete-time system identification.
Stochastic approximation algorithms for adaptive linear discrete time system identification using noisy input
Saridis, G. N.↗
Two stochastic approximation procedures for identifying linear systems.
Robbins-Monro stochastic approximation method using algorithms for identifying finite memory time-discrete time-stationary linear system from noisy input-output measurements
Holmes, J. K.↗
Stochastic approximation algorithms for system identification, estimation, and decomposition of mixtures.
Stochastic approximation algorithms derived to identify memoryless systems and obtain decomposition of mixtures of probability density functions
Fu, K. S.↗
Stochastic approximation algorithms for system identification, estimation, and decomposition of mixtures.
Stochastic approximation procedure minimizing mean square error criterion for system identification, estimation and decomposition of mixtures
Fu, K. S.↗
Multidimensional stochastic approximation using locally contractive functions
A Robbins-Monro type multidimensional stochastic approximation algorithm which converges in mean square and with probability one to the fixed point of a locally contractive regression function is developed. The algorithm is applied to obtain maximum likelihood estimates of the parameters for a mixture of multivariate normal distributions.
Lawton, W. M.↗
Two stochastic approximation procedures for identifying linear systems.
Identification of finite memory, time discrete linear systems by Kiefer-Wolfowitz stochastic approximation procedures, presenting two algorithms for sequential identification
Holmes, J. K.↗
Estimation of correlation functions by stochastic approximation.
Consideration of the autocorrelation function of a zero-mean stationary random process. The techniques are applicable to processes with nonzero mean provided the mean is estimated first and subtracted. Two recursive techniques are proposed, both of which are based on the method of stochastic approximation and assume a functional form for the correlation function that depends on a number of parameters that are recursively estimated from successive records. One technique uses a standard point estimator of the correlation function to provide estimates of the parameters that minimize the mean-square error between the point estimates and the parametric function. The other technique provides estimates of the parameters that maximize a likelihood function relating the parameters of the function to the random process. Examples are presented.
Habibi, A.↗
Estimation of optical flow in airborne electro-optical sensors by stochastic approximation
The essence of motion or range estimation by passive electrooptical means is the ability to determine the correspondence of picture elements in pairs of image frames and to estimate their coordinates and their disparity (relative shifts) in the image plane of an electrooptical imaging sensor. The disparity can be in successive frames due to self-motion or in simultaneous frames of a stereo pair. A key issue is to provide these estimates on-line. This paper describes the theoretical background of such an interframe shift estimator. It is based on a stochastic gradient algorithm, specifically implementing a form of stochastic approximation, which can achieve rapid convergence of the shift estimate. Analytical and numerical simulation examples for random texture and isolated features validate the feasibility and the effectiveness of the estimator.
Merhav, S. J.↗