Randomized algorithms ; learning theory ; robust control 隨機化算法學習理論魯棒控制
Robust control design based on randomized algorithms 基于隨機化算法的魯棒控制器設計
This paper combines learning theory with robust control and discusses robust control design problems involving real parameter uncertainty in control systems based on randomized algorithms 將學習理論與魯棒控制相結(jié)合,采用隨機化算法針對實參數(shù)不確定系統(tǒng)討論了魯棒控制器的設計問題。
This paper studies the designing principle and common property of random algorithm by analyzing some instances of algorithms , and gives out a general designing method and principles for constructing random algorithm 摘要通過常用算法的實現(xiàn)實例和實驗結(jié)果,分析隨機化算法的基本原理和共同性質(zhì),提出設計隨機化算法的一般方法,并指出隨機化算法的適用范圍和有效的隨機化算法應具備的特點。
It is shown that randomized algorithms can decrease the computational complexity dramatically instead of seeking worst case guarantees . in addition , examples in this paper show that employing randomized algorithms is very efficient and has obvious advantages especially when uncertain interval parameters appear multilinearly or nonlinearly in the characteristic polynomial coefficients 研究表明,在不考慮最壞情況的意義下,隨機化算法可以顯著降低計算復雜性,另外,當不確定區(qū)間參數(shù)以多線性或非線性的方式出現(xiàn)在特征多項式系數(shù)中時,采用隨機化算法具有明顯的優(yōu)點并且是非常有效的,文中給出了計算實例。