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Traditional control methods of two-wheeled robot are usually model-based and require the robot’s precise mathematic model which is hard to get. A sensorimotor self-learning model named SMM TWR is presented in this paper to handle these problems. The model consists of seven elements: the discrete learning time set, the sensory state set, the motion set, the sensorimotor mapping, the state orientation unit, the learning mechanism and the model’s entropy. The learning mechanism for SMM TWR is designed based on the theory of operant conditioning (OC), and it adjusts the sensorimotor mapping at every learning step. This helps the robot to choose motions. The leaning direction of the mechanism is decided by the state orientation unit. Simulation results show that with the sensorimotor model designed, the robot is endowed the abilities of self-learning and self-organizing, and it can learn the skills to keep itself balance through interacting with the environment. 相似文献
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为更加可以体现吉林市建设实际的土地需求。对吉林市分别采用多元回归分析和灰色预测模型、平均增长率法、经济计量预测。结果表明,通过各类建设用地预测汇总得出2016年吉林市建设用地为183590.65hm~2,最为接近吉林市2016年实际值。多元回归分析法预测结果数据最大。灰色预测模型适预测结果数据偏小;预测各种建设用地时,采用多种预测方法通过定性定量分析更加可以符合吉林市建设用地需求。 相似文献
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