The effect of parameter uncertainties on the optimal control of a stochastic system manifests itself in the following: it can cause the control to be more cautious or to probe in order to enhance the identification of the system while in operation.
A note on global optimization in adaptive control, econometrics and macroeconomics
β Scribed by Marco P. Tucci
- Publisher
- Elsevier Science
- Year
- 2002
- Tongue
- English
- Weight
- 260 KB
- Volume
- 26
- Category
- Article
- ISSN
- 0165-1889
No coin nor oath required. For personal study only.
β¦ Synopsis
One of the most cited examples in the literature on global optimization in econometrics is Maddala and Nelson's (Econometrica 42 (1974) 1013) attempt to maximize the likelihood function of a disequilibrium model. On p. 1026, they write "in ... all cases the ... hill climbing method converged but in each case to a di erent value, thus suggesting the existence of multiple maxima. Of all these peaks we picked the highest". EZGRAD, the algorithm presented in these pages, works in the same way. It starts the gradient procedure from several points and picks the optimum (highest or lowest) peak. Its main advantages are: (i) ease to use, (ii) adaptability to the function under investigation and (iii) suitability for parallel computing.
π SIMILAR VOLUMES
Adaptive control is applied to a particular class of SISO discrete-time non-linear systems. Global boundedness and convergence are obtained by introducing a modification to a classical adaptive scheme.
We establish a range of sufficient conditions for (proper) Pareto optimality of all points in natural domains of multicriteria optimization problems.