Optimization of wireless power transfer using artificial neural network: A review

机译：利用人工神经网络优化无线电力传递：综述

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Abstract >Wireless power transfer (WPT) is widely explored and applied nowadays because of its simplicity in transferring power without using wire, easy maintenance, and equipment mobility. Due to mobility and compatibility attributes, WPT is utilized in powering biomedical devices, small electronic equipment, wireless sensor, mobile phones, and high voltage applications (eg, electric vehicles). The implementation of artificial neural network (ANN) in WPT has emerged as a powerful/prominent tool for estimating the performance parameters due to its learning and significant features. Such implementation can minimize design complexity and time‐consuming calculations. An early application of ANN employs the information derived from the collectively measured processes for training the ANN algorithm. After a suitable training process, the network output can be considered in place of computationally thorough representations to speed up the result search. To obtain precise result and optimize the parameters in WPT, several popular ANN algorithms have been used by researchers. This review paper highlighted the latest research specifically regarding the implementation of ANN in WPT, which included the types of ANN implemented in WPT, current WPT problem investigation that used ANN, and a comparison between the techniques. Moreover, the challenges and constraints of ANN techniques were elucidated at the end of this paper. </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> <P>无线电力传输（WPT）被广泛探索和当今应用，因为它在不使用电线，维修方便，设备的移动性传递动力简单。由于移动性和相容性的属性，WPT被用在生物医学装置，小型电子设备，无线传感器，移动电话，和高电压应用（例如，电动车辆）供电。在WPT人工神经网络（ANN）的实施，已成为估计性能参数，由于其学习和显著的功能强大的/显着的工具。这样的实施方式可以最大限度地减少设计复杂和耗时的计算。 ANN的早期应用采用从集体测量的过程导出用于训练ANN算法的信息。合适的训练过程后，网络输出可代替计算彻底交涉，以加快结果搜索时考虑。为了获得精确的结果，并优化WPT参数，几种流行ANN算法已被用于研究。该评论文章强调特别是关于人工神经网络在WPT的实施，其中包括人工神经网络在WPT，目前WPT问题调查所用ANN实现的类型和技术之间的比较的最新研究成果。此外，ANN技术挑战和限制在本文的端分别鉴定</ 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2期</span><b style="margin: 0 2px;">|</b><span>共9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=ANN&option=203" rel="nofollow">ANN;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=WPT&option=203" rel="nofollow">WPT;</a> </p> <div class="translation"> 机译：安;WPT.; 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