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

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008
DOIs
Publication statusPublished - 2008
Event2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008 - London
Duration: 2008 Sep 92008 Sep 10

Other

Other2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008
CityLondon
Period08/9/908/9/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications

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    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