找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Estimating Ore Grade Using Evolutionary Machine Learning Models; Mohammad Ehteram,Zohreh Sheikh Khozani,Maliheh Abb Book 2023 The Editor(s

[复制链接]
楼主: EXTRA
发表于 2025-3-26 23:06:30 | 显示全部楼层
发表于 2025-3-27 04:23:38 | 显示全部楼层
Neeta Sharma,Swati Sharma,Basant Prabhages and disadvantages of different models are described. This chapter presents the solutions for improving the accuracy of soft computing models. This chapter explains the details for quantifying uncertainty modeling. The chapter indicated that the artificial neural network models (ANN) had high cap
发表于 2025-3-27 08:57:26 | 显示全部楼层
发表于 2025-3-27 10:44:41 | 显示全部楼层
发表于 2025-3-27 16:54:59 | 显示全部楼层
发表于 2025-3-27 18:08:24 | 显示全部楼层
Estimating Ore Grade Using Evolutionary Machine Learning Models
发表于 2025-3-27 22:37:55 | 显示全部楼层
Estimating Ore Grade Using Evolutionary Machine Learning Models978-981-19-8106-7
发表于 2025-3-28 03:42:46 | 显示全部楼层
The Necessity of Grade Estimation,elers need robust models for estimating ore grade since it is a nonlinear and complex process. We investigate the potential of different models for estimating ore grade. We explain the advantages and disadvantages of models. The purpose of this chapter is to assist modelers in choosing the best mode
发表于 2025-3-28 07:46:15 | 显示全部楼层
A Review of Modeling Approaches,rent models. The chapter also discusses the benefits of different soft computing models. This chapter aims to assess the potential of artificial neural networks for estimating ore grades. In addition, this chapter examines the research gaps for estimating ore grade in previous studies. Additionally,
发表于 2025-3-28 13:29:52 | 显示全部楼层
Structure of Different Kinds of ANN Models, advanced operators. The advantages of each ANN model are discussed in this chapter. There are different layers in ANN models. Layers perform different tasks. The performance of ANN models depends on the parameters of ANNs. Different ANN models are compared for estimating ore grade in this chapter.
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-6-11 06:44
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表