Statistical learning control of uncertain systems: theory and algorithms

被引:9
|
作者
Koltchinskii, V [1 ]
Abdallah, CT
Ariola, M
Dorato, P
机构
[1] Univ New Mexico, Dept Math & Stat, Albuquerque, NM 87131 USA
[2] Univ New Mexico, Dept EECE, Albuquerque, NM 87131 USA
[3] Univ Naples Federico II, Dipartimento Informat & Sistemist, Naples, Italy
基金
美国国家科学基金会;
关键词
empirical processes; statistical learning; robust control; optimization;
D O I
10.1016/S0096-3003(99)00283-0
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
It has recently become clear that many control problems are too difficult to admit analytic solutions. New results have also emerged to show that the computational complexity of some "solved" control problems is prohibitive, Many of these control problems can be reduced to decidability problems or to optimization questions. Even though such questions may be too difficult to answer analytically, or may not be answered exactly given a reasonable amount of computational resources, researchers have shown that we can "approximately'' answer these questions "most of the time", and have "high confidence" in the correctness of the answers. (C) 2001 Elsevier Science Inc. All rights reserved.
引用
收藏
页码:31 / 43
页数:13
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