Integrated neural-network-based method for predicting synthetic permeability in lead-zinc sintering process

Xu Chen-Hua; Wu Min; She Jin-Hua; Yokoyama Ryuichi

doi:10.1109/UKRICIS.2008.4798973

Integrated neural-network-based method for predicting synthetic permeability in lead-zinc sintering process

Xu Chen-Hua, Wu Min, She Jin-Hua, Yokoyama Ryuichi

Waseda University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In order to deal with the time variance and strong nonlinearity of lead-zinc sinteringprocess, an integrated method of predicting synthetic permeability based on neural networks (NNs) and a particle swarm algorithm has been developed. In this paper, the concept of the exponent of synthetic permeability, which reflects the state of the permeability of the process, is first explained. Next, NNs are used to establish timesequence- based and technological-parameter-based models for predicting the permeability. Then, an integrated structure based on a fuzzy classifier is described and used to construct an intelligent integrated model for predicting the permeability that combines the two models just mentioned. Finally, the results of actual runs show the method to be both effective and practical.

Original language	English
Title of host publication	2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008
DOIs	https://doi.org/10.1109/UKRICIS.2008.4798973
Publication status	Published - 2008
Event	2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008 - London Duration: 2008 Sep 9 → 2008 Sep 10

Other

Other	2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008
City	London
Period	08/9/9 → 08/9/10

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications

Access to Document

10.1109/UKRICIS.2008.4798973

Fingerprint Dive into the research topics of 'Integrated neural-network-based method for predicting synthetic permeability in lead-zinc sintering process'. Together they form a unique fingerprint.

Cite this

Chen-Hua, X., Min, W., Jin-Hua, S., & Ryuichi, Y. (2008). Integrated neural-network-based method for predicting synthetic permeability in lead-zinc sintering process. In 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008 [4798973] https://doi.org/10.1109/UKRICIS.2008.4798973

Chen-Hua, X, Min, W, Jin-Hua, S & Ryuichi, Y 2008, Integrated neural-network-based method for predicting synthetic permeability in lead-zinc sintering process. in 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008., 4798973, 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008, London, 08/9/9. https://doi.org/10.1109/UKRICIS.2008.4798973

@inproceedings{666fcf33641c41a49191b15bfc0e1b8f,

title = "Integrated neural-network-based method for predicting synthetic permeability in lead-zinc sintering process",

abstract = "In order to deal with the time variance and strong nonlinearity of lead-zinc sinteringprocess, an integrated method of predicting synthetic permeability based on neural networks (NNs) and a particle swarm algorithm has been developed. In this paper, the concept of the exponent of synthetic permeability, which reflects the state of the permeability of the process, is first explained. Next, NNs are used to establish timesequence- based and technological-parameter-based models for predicting the permeability. Then, an integrated structure based on a fuzzy classifier is described and used to construct an intelligent integrated model for predicting the permeability that combines the two models just mentioned. Finally, the results of actual runs show the method to be both effective and practical.",

author = "Xu Chen-Hua and Wu Min and She Jin-Hua and Yokoyama Ryuichi",

year = "2008",

doi = "10.1109/UKRICIS.2008.4798973",

language = "English",

isbn = "9781424429141",

booktitle = "2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008",

note = "2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008 ; Conference date: 09-09-2008 Through 10-09-2008",

}

TY - GEN

T1 - Integrated neural-network-based method for predicting synthetic permeability in lead-zinc sintering process

AU - Chen-Hua, Xu

AU - Min, Wu

AU - Jin-Hua, She

AU - Ryuichi, Yokoyama

PY - 2008

Y1 - 2008

N2 - In order to deal with the time variance and strong nonlinearity of lead-zinc sinteringprocess, an integrated method of predicting synthetic permeability based on neural networks (NNs) and a particle swarm algorithm has been developed. In this paper, the concept of the exponent of synthetic permeability, which reflects the state of the permeability of the process, is first explained. Next, NNs are used to establish timesequence- based and technological-parameter-based models for predicting the permeability. Then, an integrated structure based on a fuzzy classifier is described and used to construct an intelligent integrated model for predicting the permeability that combines the two models just mentioned. Finally, the results of actual runs show the method to be both effective and practical.

AB - In order to deal with the time variance and strong nonlinearity of lead-zinc sinteringprocess, an integrated method of predicting synthetic permeability based on neural networks (NNs) and a particle swarm algorithm has been developed. In this paper, the concept of the exponent of synthetic permeability, which reflects the state of the permeability of the process, is first explained. Next, NNs are used to establish timesequence- based and technological-parameter-based models for predicting the permeability. Then, an integrated structure based on a fuzzy classifier is described and used to construct an intelligent integrated model for predicting the permeability that combines the two models just mentioned. Finally, the results of actual runs show the method to be both effective and practical.

UR - http://www.scopus.com/inward/record.url?scp=64949112067&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=64949112067&partnerID=8YFLogxK

U2 - 10.1109/UKRICIS.2008.4798973

DO - 10.1109/UKRICIS.2008.4798973

M3 - Conference contribution

AN - SCOPUS:64949112067

SN - 9781424429141

BT - 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008

T2 - 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008

Y2 - 9 September 2008 through 10 September 2008

ER -