NDLI: Using wavelet neural network model product quality of continuous casting furnace and hot rolling mill

Content Provider	IEEE Xplore Digital Library
Author	Huanqin Li Baiwu Wan
Copyright Year	2004
Description	Author affiliation: Fac. of Sci., Xi'an Jiaotong Univ., China (Huanqin Li)
Abstract	The industrial process of continuous casting furnace and hot rolling mill is very complicated, and the number of parameters, which determine the final product quality, is more than 30, so there is no mathematical model available. This paper models the product quality using wavelet neural network based on large number of data acquired from production. In accordance with the industrial process consisting of several procedures, this paper proposes a new architecture of the high-dimension input wavelet neural network. In the presented neural network, some input variables are connected directly to the second hidden layer or more later hidden layers according them to make action being early or late in work procedures; the key input connected to not only all nodes in the subsequent layer but also the output nodes directly. To avoiding fall in local minimum, this paper present a new global optimization algorithm based on a filled function. Experimental results indicate that the developed methodology is efficient and has a high accuracy in application to establishing production quality model.
Sponsorship	Zhejian Univ. Nat. Lab. of Ind. Control Technol. IEEE Robotics and Automation Soc. Nat. Natural Sci. Found. of China Chinese Assoc. of Automation
Starting Page	3381
Ending Page	3384
File Size	246000
Page Count	4
File Format	PDF
ISBN	0780382730
DOI	10.1109/WCICA.2004.1343169
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Continuous wavelet transforms Neural networks Casting Furnaces Milling machines Production Mathematical model Input variables
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Product quality model based on high dimension input wavelet neural network for hot rolling mill

Design of Hot Rolling Mill Plate Quality Control Model Based on WNN

Multi-input-layer wavelet neural network and its application

Phosphorus endpoint prediction of AOD furnace ferroalloy melting based on wavelet neural network

Power consumption prediction of submerged arc furnace based on multi-input layer wavelet neural network

The research on an adaptive rolling load prediction model based on neural networks

The research on integrated neural networks in rolling load prediction system for temper mill

High Precision Prediction of Rolling Load of Finishing Stands by Fuzzy Identification Method

Steel scheduling optimization for IPSCO's rolling mill and reheat furnace

Using wavelet neural network model product quality of continuous casting furnace and hot rolling mill

Similar Documents

Product quality model based on high dimension input wavelet neural network for hot rolling mill

Design of Hot Rolling Mill Plate Quality Control Model Based on WNN

Multi-input-layer wavelet neural network and its application

Phosphorus endpoint prediction of AOD furnace ferroalloy melting based on wavelet neural network

Power consumption prediction of submerged arc furnace based on multi-input layer wavelet neural network

The research on an adaptive rolling load prediction model based on neural networks

The research on integrated neural networks in rolling load prediction system for temper mill

High Precision Prediction of Rolling Load of Finishing Stands by Fuzzy Identification Method

Steel scheduling optimization for IPSCO's rolling mill and reheat furnace

Using wavelet neural network model product quality of continuous casting furnace and hot rolling mill