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Analysis of ANFIS Model for Polymerization Process

  • Hideyuki Matsumoto
  • Cheng Lin
  • Chiaki Kuroda
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Root Mean Square Error Membership Function Predictive Performance Fuzzy Inference System Polyvinyl Acetate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hideyuki Matsumoto
    • 1
  • Cheng Lin
    • 1
  • Chiaki Kuroda
    • 1
  1. 1.Department of Chemical EngineeringTokyo Institute of TechnologyTokyoJapan

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