论文标题
使用长期 - 内存（LSTM）对DC-DC降压转换器进行建模
Modelling of a DC-DC Buck Converter Using Long-Short-Term-Memory (LSTM)
论文作者
论文摘要
人工神经网络使识别黑盒模型成为可能。基于复发性非线性自回归外源性神经网络，该研究提供了一种模拟DC-DC功率转换器的静态和动态行为的技术。该方法采用算法使用雄鹿转换器的输入和输出（电流和电压）训练神经网络。使用现实的Simulink编程的非同步降压转换器模型和实验发现的模拟数据对该技术进行了验证。通过将神经网络的预测输出与系统的实际输出进行比较，从而确定了技术的正确性，从而确认了建议的策略。模拟发现证明了所提出的黑盒方法的实用性和精度。
Artificial neural networks make it possible to identify black-box models. Based on a recurrent nonlinear autoregressive exogenous neural network, this research provides a technique for simulating the static and dynamic behavior of a DC-DC power converter. This approach employs an algorithm for training a neural network using the inputs and outputs (currents and voltages) of a Buck converter. The technique is validated using simulated data of a realistic Simulink-programmed nonsynchronous Buck converter model and experimental findings. The correctness of the technique is determined by comparing the predicted outputs of the neural network to the actual outputs of the system, thereby confirming the suggested strategy. Simulation findings demonstrate the practicability and precision of the proposed black-box method.
