Optimized Network for Predicting Total Iron Content and Alkalinity of Sinter With Input and Middle Layers Nodes Reducing

DEStech Transactions on Computer Science and Engineering
doi 10.12783/dtcse/cnai2018/24150
Full Text
Abstract

Available in full text

Date
Authors
Publisher

DEStech Publications

Related search

Network Kriging - Predicting the Attributes of Nodes in a Network

English

A Global Monthly Climatology of Total Alkalinity: A Neural Network Approach

2019English

Mineral Forming and Its Composition Model for Iron Ore Sinter

Tetsu-To-Hagane/Journal of the Iron and Steel Institute of Japan
AlloysCondensed Matter PhysicsMetalsTheoretical ChemistryMaterials ChemistryPhysical
1987English

Reducing Memory Fragmentation With Performance-Optimized Dynamic Memory Allocators in Network Applications

Lecture Notes in Computer Science
Computer ScienceTheoretical Computer Science
2005English

Antioxidant Activity, Reducing Power and Total Phenolic Content of Iranian Olive Cultivar

Journal of Biological Sciences
Molecular MedicineCell Biology
2008English

Research on Reducing Alkalinity of an Ecological Porous Concrete Mixure With Carbonization

Materiali in Tehnologije
PolymersAlloysPlasticsMetals
2019English

The Dam With Content-Dependent Input and Output

Applicationes Mathematicae
1987English

Iron, Zinc and Total Antioxidant Capacity in Different Layers of Rice Grain Among Different Varieties

International Journal of Agriculture and Biology
AgriculturalBiological Sciences
2016English

Effect of Coarse-Grain and Low-Grade Iron Ores on Sinter Properties

ISIJ International
Mechanics of MaterialsAlloysMaterials ChemistryMetalsMechanical Engineering
2017English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy