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Approximation-error-ADP-based optimal tracking control for chaotic systems with convergence proof 下载免费PDF全文
In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming (ADP) algorithm. Via the system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then the novel optimal tracking control method is proposed. It is shown that for the iterative ADP algorithm with finite approximation error, the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some convergence conditions. Two examples are given to demonstrate the validity of the proposed optimal tracking control scheme for chaotic systems. 相似文献
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We investigate how the way of breaking links and establishing links impacts cooperation by comparing four coevolutionary rules. In the rules, a player chooses to break an existing link according to his environment, and the establishment of a new link depends on the environment of the potential neighbor. It is found that the way of breaking links plays a key role in promoting cooperation. The way of establishing links has a negligible effect on cooperation, but determines the population structure. Our results may provide some insights into understanding the evolution of cooperation. 相似文献
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