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Artificial Cognitive System having a proactive studying function using an Uncertainty Measure based on Class Probability Output Networks and proactive studying method for the same
Artificial Cognitive System having a proactive studying function using an Uncertainty Measure based on Class Probability Output Networks and proactive studying method for the same
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机译:具有基于类概率输出网络的不确定性测度的具有主动学习功能的人工认知系统及其主动学习方法
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摘要
The present invention provides an artificial cognitive system having a proactive learning function using uncertainty measurement based on class probability output networks, and a proactive learning method for the same. The proactive learning method comprises: a first step for allowing an artificial cognitive control unit to form a body of knowledge for learning materials inputted in a video or audio format through a camera and a microphone and learning materials inputted by wire and wireless through an interface unit; a second step for allowing the artificial cognitive control unit to predict the reliability of a corresponding learning material according to the formed body of knowledge, based on a predetermined proactive knowledge propagation model, and executing proactive learning of a proactive knowledge propagation model type depending on the predicted result, after the first step; and a third step for analyzing proactive learning performance during the first step and supplementing and modifying a calculation model included in the proactive knowledge propagation model depending on the analysis result. The present invention as described above can develop a calculation model having a brain cognitive function by complexly using an information engineering technique and a brain cognitive scientific technique, and can execute proactive learning using uncertainty measurement based on class probability output networks for intelligent robots, thereby allowing the intelligent robots to improve a cognitive function by themselves. [Reference numerals] (12) Video signal processing module; (5) Audio signal processing module; (6) Artificial cognitive control unit; (7) Cognitive learning module; (8) Memory unit; (9) Interface unit
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