采用伴随-BP技术, 将微分对策的两点边值求解问题转化为两个神经网络的学习问题, 训练后 的两个神经网络分别作为对策双方的最优控制器在线使用, 避免了直接求解复杂的两点边值问题。对追 逃微分对策问题的仿真结果表明, 该方法对初始条件和噪声具有较好的鲁棒性。

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含有人脸表情的大量数据，训练集和测试集；基于RT-Thread开发，实现人脸表情的实时识别，此文件含有实现源码。

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Abstract——The distributed power generation systems based on the renewable energy sources, such as solar photovoltaic, wind power, geothermal power, have been the focus of the world in the latest years. The grid-connected inverter is important power electronic implements, used to transfer the power from the DPGS to the public grid, and the quality of its output power has been studied a lot. The repetitive control technique based on the internal model principle, can achieve low steady-state tracking error and total harmonic distortion (THD) in AC systems, so it is widely adopted in the inverter systems. However in the practical applications, there are still some issues needs solving when using the repetitive control technique. To suppress the current distortion, a control strategy of fundamental current and harmonics controlled separately is adopted. On the synchronous rotating coordinate, the fundamental component is direct value and the harmonic component is alternating value. Proportion-integral (PI) values above which can achieved output controlJer and proportion resonant (PR) controller are adopted to control the power adjustment and zero error control of harmonic restrain.