在中性环境中使用模糊逻辑控制器进入加油站的最佳方式

Muhammad Naveed Jafar, M. Saqlain, Aasia Mansoob, Asma Riffat
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引用次数: 3

摘要

如今,谷歌地图被用来寻找任何位置和/或定义到任何给定地点的路线。它的精度高达30米,但如果使用中性数字,它会提供更高的精度。为了检验嗜中性数在谷歌地图中的实现情况,开发了一个基于模糊控制器(FLC)的系统,利用嗜中性数来寻找距离最近、停车单位少、路上交通信号少的加油站。这样,到达可用的加油站所花的时间就更少了。这个系统使司机能够更准确地找到加油站。我们取距离、可用汽油量、停车数量、汽油量和交通信号数量五个语言输入,得到一个输出,即时间。我们为每个语言输入分配了不同的中性软集。FLC推理采用基于if-then语句的108条规则来选择到达加油站的时间。通过MATLAB模糊逻辑工具箱对结果进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Best Way to Access Gas Stations using Fuzzy Logic Controller in a Neutrosophic Environment
These days, Google Map is used to find any location and/or to define the route to any given place. Its accuracy is up to 30 meters but if neutrosophic numbers are used, it gives more accuracy. To check the implementation of neutrosophic numbers in Google Map, a system is developed based on Fuzzy Logic Controller (FLC) using neutrosophic numbers to find the gas station which is nearest, less parking car units and with few traffic signals on the way. In this way, it takes less time to reach the available gas station. This system enables the driver to find a fuel station with more accuracy. We took five linguistic inputs including distance, gas availability, parking car unit, amount of gas, and the number of traffic signals to get one output, that is, time. We assigned different neutrosophic soft sets to each linguistic input. FLC inference was designed using 108 rules based on if-then statements to select time to reach the gas station. The results were verified by MATLAB’s Fuzzy Logic Toolbox.
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