"深度学习在电离层传播条件预测算法中的应用研究"

Abstract Deep Learning is a rapidly evolving field within artificial intelligence research, characterized by its use of neural network models with multiple hidden layers. As the basic theory of Deep Learning continues to mature, its practical applications across various domains are also expanding. This paper focuses on the research and development of a Deep Learning-based algorithm for predicting ionospheric propagation conditions. The research study explores the application of Deep Learning in forecasting ionospheric propagation conditions, leveraging the capabilities of neural network models to extract essential features from large datasets. By utilizing a training dataset composed of massive amounts of data, the network is trained to identify patterns and trends within the data, ultimately enabling accurate predictions of ionospheric propagation conditions. The algorithm proposed in this study demonstrates the potential for Deep Learning to enhance the accuracy and effectiveness of ionospheric propagation predictions. By harnessing the power of neural networks with multiple hidden layers, the algorithm is able to capture complex relationships within the data, leading to more precise forecasts of ionospheric conditions. Overall, the research highlights the importance of Deep Learning in advancing the field of ionospheric propagation prediction. With further development and refinement, Deep Learning algorithms have the potential to revolutionize the way we forecast and understand ionospheric conditions, ultimately shaping the future of telecommunications and satellite communication technologies.