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標題: 估測、適應控制與系統分析之訊息理論方法
Estimation, Adaptive Control and System Analysis: An Information Theoretic Approach
作者: 陳益生
關鍵字: Entropy;熵數;Information Theory;Kalman filter;adaptive optimal control;system analysis;estimation;訊息理論;卡爾曼濾波器;最佳適應控制;系統分析;估測
出版社: 電機工程學系

This thesis uses information theoretic approach to study the problems of optimal state estimation, discrete-time adaptive optimal control and some system analysis issues.
For continuous time and sampled data linear Gaussian systems, it is proved that Kalman filters are the optimal filters not only for minimum mean square error measure, but also for the information theoretic measures introduced in the thesis. The investigation of minimum error entropy is meaningful only when the filter is unbiased. A Kalman-filter-like algorithm to a minimum error entropy filter for a linear Gaussian system is presented.
Entropy formulations for deterministic and stochastic adaptive optimal control problems are derived. The resulting expressions are derived in detail and their physical meanings are provided. These results show that the minimum (optimal) cost function which is the same as the total entropy of the system is equal to the cost (entropy) of adaptive control minus the equivocation of the knowledge that the system is controlled.
Three information partition laws for systems and input are developed and their physical meanings are interpreted. Two examples are used for illustration of implications of the proposed laws.
Appears in Collections:電機工程學系所

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