Numerical Methods and Approximation and Modelling Problems in Stochastic Control Theory.
Abstract
Many areas of stochastic control were studied in this period. Monte-Carlo algorithms of type appearing in adaptive systems were studied and stability, convergence and rate of convergence results obtained. The analytical techniques developed for this purpose have a wide applicability in the study of similar algorithms. Robust computationally oriented approximations to optimal non-linear filters were developed. A deep study of how to find the Markov system that best approximates a given non-linear system with a wide band-width input was undertaken and very good results obtained. The problem is important in control and communication theory because it is usually desired to use the many available methods for analyzing Markov systems. The results obtained seem to be the best available so far and application to control and communication theory are being pursued. Optimal stochastic control problems under the condition of partial information on the system state were studied and a new formulation, similar to that of the non-linear filtering problem, developed. The approach seems promising in providing qualitative information on this difficult class of problems. Part of the technique uses measure-valued processes in a way which has wider applicability to problems involving distributed states or information. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 15, 1979
- Accession Number
- ADA078288
Entities
People
- Harold J. Kushner
- Wendell Fleming
Organizations
- Brown University