RBF神经网络在低压导线截面选择中的应用  

Application of RBF Neural Network in Selection of Low Voltage Conductor Cross Section

作　　者：陈滨掖[1] CHEN Bin-ye(Jiangsu Urban and Rural Construction College,Changzhou Jiangsu 213147)

机构地区：[1]江苏城乡建设职业学院,江苏常州213147

出　　处：《数字技术与应用》2018年第11期115-117,121,共4页Digital Technology & Application

摘　　要：传统低压导线截面选择过程中存在数据繁琐复杂,计算过程冗长,计算准确率及效率较低等问题。本文提出在MATLAB环境之下,通过建立RBF神经网络应用模型,对导线截面选择数据进行测试分析。这种新的计算方法与常规的计算方法相比,极大提高了计算效率,为低压导线截面积的计算提供了新的思路和方向,为优化线路截面的选择提供了可靠、高效的理论数据。There are some problems in the traditional low-voltage conductor section selection process, such as complicated data, long calculation process, low calculation accuracy and efficiency. in this paper, under the environment of MATLAB, the application model of RBF neural network is established to test and analyze the data of conductor section selection. compared with the conventional calculation method, this new calculation method greatly improves the calculation efficiency, provides a new idea and direction for the calculation of low-voltage conductor cross-section area, and provides reliable and efficient theoretical data for optimizing the selection of line cross-section.

关 键 词：导线截面选择 径向基网络函数 MATLAB 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]