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 | |
| 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 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
Cite this
Integrated neural-network-based method for predicting synthetic permeability in lead-zinc sintering process. / Chen-Hua, Xu; Min, Wu; Jin-Hua, She; Ryuichi, Yokoyama.
2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008. 2008. 4798973.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
ER -