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Discrete Dynamics in Nature and Society
Volume 2016, Article ID 3460492, 8 pages
Research Article

Organization Learning Oriented Approach with Application to Discrete Flight Control

1School of Humanities, Economics and Law, Northwestern Polytechnical University, Xi’an 710072, China
2Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Received 25 December 2015; Accepted 28 February 2016

Academic Editor: Driss Boutat

Copyright © 2016 Lin Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In nature and society, there exist many learning modes; thus, in this paper the goal is to incorporate features from the social organizations to improve the learning of intelligent systems. Inspired by future prediction, in the high level, the discrete dynamics is further written into the equivalent prediction model which can provide the bridge from now to the future. In the low level, the efficiency could be improved in way of group learning. The philosophy is integrated into discrete neural flight control where the cascade dynamics is written into the prediction form and the minimal-learning-parameter technique is designed for parameter learning. The effectiveness of the proposed method is verified with simulation.