基于深度学习的船用大功率放大器特性数学建模  

Mathematical modeling of marine high-power amplifier based on deep learning

作　　者：王春鸽[1] WANG Chun-ge(College of Arts and Sciences,Yangtze University,Jingzhou 434000,China)

机构地区：[1]长江大学文理学院,湖北荆州434000

出　　处：《舰船科学技术》2023年第4期113-116,共4页Ship Science and Technology

基　　金：2021长江大学文理学院教学质量工程一流课程建设资助项目。

摘　　要：设计基于深度学习的船用大功率放大器特性数学建模方法,合理分析船用大功率放大器特性及特性影响因素间的相关性,为船用大功率放大器实际应用与改进提供可靠依据。通过模拟实验采集船用大功率放大器特性影响因素数据,并对其实施有效的归一化处理,将归一化后的船用大功率放大器特性影响因素数据、放大器输出功率当作深度学习算法的有效输入以及输出,利用深度学习算法合理实施训练,合理构造、拟合船用大功率放大器特性关于影响因素的数学模型,并实施合理验证。实验结果表明:该方法构建的船用大功率放大器特性数学模型,可有效分析负载电阻、环境温度等影响因素与船用大功率放大器间的相关性,助力设计出更适合应用于水上领域的船用大功率放大器。This paper studies the mathematical modeling method of the characteristics of marine high-power amplifier based on deep learning, reasonably analyzes the correlation between the characteristics of marine high-power amplifier and its influencing factors, and provides a reliable basis for the practical application and improvement of marine high-power amplifier. The data of influencing factors of marine high-power amplifier characteristics are collected through simulation experiments and effectively normalized. The normalized data of influencing factors of marine high-power amplifier characteristics and the output power of the amplifier are regarded as the effective input and output of the depth learning algorithm. The depth learning algorithm is used to implement training reasonably, and the mathematical model of influencing factors of marine high-power amplifier characteristics is reasonably constructed and fitted, and implement reasonable verification. The experimental results show that the mathematical model of the characteristics of the marine high-power amplifier constructed by this method can effectively analyze the correlation between the load resistance, ambient temperature and other influencing factors and the marine high-power amplifier, and help to design a marine high-power amplifier more suitable for the water field.

关 键 词：深度学习 船用放大器 数学建模 大功率 归一化 深度置信网络 

分 类 号：TN247[电子电信—物理电子学]