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| 1 | AI驱动智能优化与控制文献计量分析显示文摘AI技术为突破传统的优化与控制理论、方法,推动智能优化与控制提供了重要途径和基础支撑。为探析AI驱动智能优化与控制的研究脉络、热点和趋势,对近10年中国知网(CNKI)和Web of Science中收录的AI驱动智能优化与控制相关文献进行筛选,得到中文文献数据共5814条,英文文献数据共5208条;使用CiteSpaee6.1.R6软件以及VOSviewer1.8.18软件进行可视化分析,并绘制关键词共现图谱、时间线演化图谱、作者聚类图谱等知识图谱,进行文献计量分析。结果表明:有关AI驱动智能优化与控制的文献数量整体处于上升趋势,在发文国家中以中国和美国文献数量最多。国际上对该方向的研究主要是围绕系统进行的,并尝试通过机器学习解决系统难题;国内研究热点聚集在深度学习的方向。根据研究现状和热点,对该领域进行了发展趋势预测,为AI驱动智能优化与控制领域研究提供了参考。 | 郭洪飞 韦雨佳 任亚平 陈文辉 | 2023 | 机电工程技术2023，52，4： | 1 |
| 2 | Continuous-Time Channel Prediction Based on Tensor Neural Ordinary Differential Equation显示文摘Channel prediction is critical to address the channel aging issue in mobile scenarios.Existing channel prediction techniques are mainly designed for discrete channel prediction,which can only predict the future channel in a fixed time slot per frame,while the other intra-frame channels are usually recovered by interpolation.However,these approaches suffer from a serious interpolation loss,especially for mobile millimeter-wave communications.To solve this challenging problem,we propose a tensor neural ordinary differential equation(TN-ODE)based continuous-time channel prediction scheme to realize the direct prediction of intra-frame channels.Specifically,inspired by the recently developed continuous mapping model named neural ODE in the field of machine learning,we first utilize the neural ODE model to predict future continuous-time channels.To improve the channel prediction accuracy and reduce computational complexity,we then propose the TN-ODE scheme to learn the structural characteristics of the high-dimensional channel by low-dimensional learnable transform.Simulation results show that the proposed scheme is able to achieve higher intra-frame channel prediction accuracy than existing schemes. | Mingyao Cui Hao Jiang Yuhao Chen Yang Du Linglong Dai | 2024 | China Communications2024，21，1： | 0 |
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