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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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.↗
Accelerating Optimization and Reinforcement Learning with Quasi Stochastic Approximation
The paper sets out to obtain precise convergence rates for quasi-stochastic approximation (QSA), with applications to optimization and reinforcement learning.
approximation algorithms↗
Revisiting the ODE Method for Recursive Algorithms: Fast Convergence Using Quasi Stochastic Approximation
Several decades ago, Profs. Sean Meyn and Lei Guo were postdoctoral fellows at ANU, where they shared interest in recursive algorithms. It seems fitting to celebrate Lei Guo's 60th birthday with a review of the ODE Method and its recent evolution. The method has been regarded as a technique for algorithm analysis. It is argued that this viewpoint is backwards: The original stochastic approximation method was surely motivated by an ODE, and tools for analysis came much later (based on establishing robustness of Euler approximations). The paper presents a brief survey of recent research in machine learning that shows the power of algorithm design in continuous time, following by careful approximation to obtain a practical recursive algorithm. While these methods are usually presented in a stochastic setting, this is not a prerequisite. In fact, recent theory shows that rates of convergence can be dramatically accelerated by applying techniques inspired by quasi Monte-Carlo. Subject to conditions, the optimal rate of convergence can be obtained by applying the averaging technique of Polyak and Ruppert. The conditions are not universal, but theory suggests alternatives to achieve acceleration. The theory is illustrated with applications to gradient-free optimization, and policy gradient algorithms for reinforcement learning.
learning and adaptive systems in artificial intell↗
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.↗