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- James C. Spall
- IEEE Trans. Automat. Contr.
- 2000

Stochastic approximation (SA) has long been applied for problems of minimizing loss functions or root finding with noisy input information. As with all stochastic search algorithms, there are adjustable algorithm coefficients that must be specified, and that can have a profound effect on algorithm performance. It is known that choosing these coefficients… (More)

- James C. Spall
- Automatica
- 1997

- James C. Spall
- 2005

The Fisher information matrix summarizes the amount of information in the data relative to the quantities of interest. There are many applications of the information matrix in modeling, systems analysis, and estimation, including confidence region calculation, input design, prediction bounds, and “noninformative” priors for Bayesian analysis. This article… (More)

Consider the problem of developing a controller for general (nonlinear and stochastic) systems where the equations governing the system are unknown. Using discrete-time measurements, this paper presents an approach for estimating a controller without building or assuming a model for the system (including such general models as differential/difference… (More)

- James C. Spall
- Automatica
- 1995

- James C. Spall, John A. Cristion
- IEEE Trans. Systems, Man, and Cybernetics, Part B
- 1997

This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The approach here is based on using the output error of the system to train the NN controller without the need to assume or construct a separate model (NN or other type) for the unknown process dynamics. To implement such a… (More)

- James C. Spall
- 2007 41st Annual Conference on Information…
- 2007

It is known that a stochastic approximation (SA) analogue of the deterministic Newton-Raphson algorithm provides an asymptotically optimal or near-optimal form of stochastic search. In a recent paper, Spall (2006) introduces two enhancements that generally improve the quality of the estimates for underlying Jacobian (Hessian) matrices, thereby improving the… (More)

- James C. Spall
- Winter Simulation Conference
- 1999

Multivariate stochastic optimization plays a major role in the analysis and control of many real-world systems. In almost all large-scale practical optimization problems, it is necessary to use a mathematical algorithm that iteratively seeks out the solution because an analytical (closed-form) solution is rarely available. In the above spirit, the… (More)

- James C. Spall
- 2004

General Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 Formal Problem Statement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Contrast of… (More)

We present a stochastic approximation algorithm based on penalty function method and a simultaneous perturbation gradient estimate for solving stochastic optimization problems with general inequality constraints. We present a general convergence result that applies to a class of penalty functions including the quadratic penalty function, the augmented… (More)