基于人工神经网络的三维分形插值方法

Nanoscience and Nanotechnology Letters Pub Date : 2020-10-01 DOI:10.1166/NNL.2020.3226

Rashad Al-Jawfi

{"title":"基于人工神经网络的三维分形插值方法","authors":"Rashad Al-Jawfi","doi":"10.1166/NNL.2020.3226","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce a new method for fractal interpolation, herein called Neural Network Algorithm (NNA), which is based on Iterated Functions Systems (IFS); proposed to self-affine signals interpolation with error of expected interpolation. Experiments on theoretical data show\n that the proposed interpolation schemes can obtain the expected point value and work with great precision in rebuilding the specified data profile, which leads to a significant advantage over other interpolation methods.","PeriodicalId":18871,"journal":{"name":"Nanoscience and Nanotechnology Letters","volume":"12 1","pages":"1221-1225"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Using Artificial Neural Networks for Fractal Interpolation Approach in 3D\",\"authors\":\"Rashad Al-Jawfi\",\"doi\":\"10.1166/NNL.2020.3226\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we introduce a new method for fractal interpolation, herein called Neural Network Algorithm (NNA), which is based on Iterated Functions Systems (IFS); proposed to self-affine signals interpolation with error of expected interpolation. Experiments on theoretical data show\\n that the proposed interpolation schemes can obtain the expected point value and work with great precision in rebuilding the specified data profile, which leads to a significant advantage over other interpolation methods.\",\"PeriodicalId\":18871,\"journal\":{\"name\":\"Nanoscience and Nanotechnology Letters\",\"volume\":\"12 1\",\"pages\":\"1221-1225\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nanoscience and Nanotechnology Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1166/NNL.2020.3226\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nanoscience and Nanotechnology Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1166/NNL.2020.3226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

本文介绍了一种新的分形插值方法，即基于迭代函数系统(IFS)的神经网络算法(NNA);提出了对自仿射信号进行误差预期插值的方法。理论数据实验表明，所提出的插值方法能够获得期望的点值，并能以较高的精度重建指定的数据轮廓，与其他插值方法相比具有明显的优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Using Artificial Neural Networks for Fractal Interpolation Approach in 3D

In this paper, we introduce a new method for fractal interpolation, herein called Neural Network Algorithm (NNA), which is based on Iterated Functions Systems (IFS); proposed to self-affine signals interpolation with error of expected interpolation. Experiments on theoretical data show that the proposed interpolation schemes can obtain the expected point value and work with great precision in rebuilding the specified data profile, which leads to a significant advantage over other interpolation methods.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Nanoscience and Nanotechnology Letters Physical, Chemical & Earth Sciences-MATERIALS SCIENCE, MULTIDISCIPLINARY

自引率

0.00%

发文量

审稿时长

2.6 months