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car_pole_system_upload_ver

于 2014-01-23 发布 文件大小:309KB
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  Reinforcement Learning 中以 Q learning 學習的倒單擺實驗。以x,x_dot,theta,theta_dot作為狀態參數 state,利用model模擬之結果做Q值更新,產生Q table,對學習平衡控制。結果以csv檔作為輸出,可以由使用者作圖觀察變化。(Reinforcement Learning , utilize Q learning for An inverted pendulum system control。x,x_dot,theta,theta_dot as state variable,use model result renew Q table,control learning of balance pole 。output csv file contain trail(times),balance time(sec).it s better observe the change if user plot it. )

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    the thesis put forwards three detection methods.the thesis analyzes and compares them on voltage distortion effects, real-time character, application of use and complexity of algorithm , The thesis has done the real-time dynamic simulation using the MATLAB software ,which is based on three detection methods , and the results show the correctness of active filter scheme, and advantages and disadvantages of three detection methods.
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    Ce programme nécessite le matériel suivant
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