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Analysis of ANFIS Model for Polymerization Process

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4252))

Abstract

Adaptive-network-based Fuzzy Inference System (ANFIS), proposed by Jang, is applied to estimating characteristics of end products for a semibatch process of polyvinyl acetate. In modeling the process, it is found that an ANFIS model restructured in a way of cascade mode enhances predictive performance. And membership functions for temperature, solvent fraction, initiator concentration and monomer conversion, which are changed by training, are analyzed. Consequently, it is considered that the analysis of parameter adjustment in the membership functions can clarify effect of adding the conversion to an input variable of fuzzy sets on enhancement of robustness and improvement of local prediction accuracy in restructuring ANFIS model.

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References

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Author information

Authors and Affiliations

  1. Department of Chemical Engineering, Tokyo Institute of Technology, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan

    Hideyuki Matsumoto, Cheng Lin & Chiaki Kuroda

Authors
  1. Hideyuki Matsumoto
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  2. Cheng Lin
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  3. Chiaki Kuroda
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Editor information

Editors and Affiliations

  1. School of Design, Engineering and Computing, Bournemouth University, UK

    Bogdan Gabrys

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, SA, 5095, Mawson Lakes, Australia

    Lakhmi C. Jain

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© 2006 Springer-Verlag Berlin Heidelberg

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Matsumoto, H., Lin, C., Kuroda, C. (2006). Analysis of ANFIS Model for Polymerization Process. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_73

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  • DOI: https://doi.org/10.1007/11893004_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46537-9

  • Online ISBN: 978-3-540-46539-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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