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A study on path loss prediction based on Tikhonov regularization using gradient adaptive step‐size in mountain areas

机译：基于山区梯度自适应阶梯大小的Tikhonov规范化的路径损耗预测研究

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Abstract >This paper presents a method of applying a regularization concept for more accurate path loss prediction in mountainous areas. Tikhonov regularization with gradient adaptive step‐size is used to adjust the path loss model. Measurements were conducted in two mountainous areas to compare the path loss prediction performances. Prediction accuracies were improved by up to 3.42 and 0.95?dB in the average error and the SD of errors by the proposed method when compared to the nontuned log‐distance path loss model and Edwards‐Durkin model with diffraction models. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><abstract type =“main”xml：lang =“en”> <标题类型=“main”>抽象</ title> >本文提出了一种对山区更准确的路径损耗预测应用正则化概念的方法。使用梯度自适应梯形大小的Tikhonov常规用于调整路径损耗模型。测量在两个山区进行，以比较路径损耗预测性能。通过使用衍射模型的非吞注日志路径损耗模型和Edwards-Durkin模型相比，预测精度高达3.42和0.95？DB在平均误差和误差的SD中。</ p> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-24977/'>《Microwave and optical technology letters》</a> <b style="margin: 0 2px;">|</b><span>2020年第7期</span><b style="margin: 0 2px;">|</b><span>共6页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/1165.html" title="光学">光学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=adaptive step‐size&option=203" rel="nofollow">adaptive step‐size;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=mountain areas&option=203" rel="nofollow">mountain areas;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=path loss prediction&option=203" rel="nofollow">path loss prediction;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=regression&option=203" rel="nofollow">regression;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=regularization&option=203" rel="nofollow">regularization;</a> </p> <div class="translation"> 机译：自适应阶梯大小;山区;路径损失预测;回归;正规化; 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