Abstract
A system is proposed which combines hypothesis testing and prediction to estimate the future value of a stochastic process whose statistics are known only to belong to some finite set of possible hypotheses. Bayes optimization of the individual components is performed and system performance is discussed for a modified version of the usual mean-squared error predictor cost functions. An example is given illustrating various features of the system's performance for a specific choice of input hypotheses.
Original language | English (US) |
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Article number | 4044823 |
Pages (from-to) | 552-556 |
Number of pages | 5 |
Journal | Proceedings of the IEEE Conference on Decision and Control |
DOIs | |
State | Published - Jan 1 1971 |
Event | 1971 IEEE Conference on Decision and Control, CDC 1971 - Including the 10th Symposium on Adaptive Process - Miami Beach, United States Duration: Dec 15 1971 → Dec 17 1971 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization