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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.