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Selective ensemble learning is a learning algorithm, trains a number of based classifier and selects some of them to ensemble. Through the selective ensemble, the algorithm would be more effective than each single one and better than the algorithm that select all the based classifier, and the algorithm would have effective generalization ability. In this paper, we apply a multi-lever selective ensemble...
Gear mechanisms are an important element in a variety of industrial applications. An unexpected failure of the gear mechanism may cause significant economic losses. Efficient incipient faults detection and accurate faults diagnosis are therefore critical to machinery normal running. In this paper a novel method is presents to enhance the detection and diagnosis of gear multi-faults based on Autoregressive...
This paper studies the adaptive and optimal output feedback control problem using approximate dynamic programming. It is shown that, under the recursive algorithm, the control policy converges to its optimal value, up to a constant proportional to the magnitude of the inaccuracy caused by observation errors. On the basis of this result, direct adaptive output feedback strategies are developed for...
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