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International Symposium on Neural Networks

ISNN 2006: Advances in Neural Networks - ISNN 2006 pp 844–851Cite as

A Levinson Predictor Based Compensatory Fuzzy Neural Network and Its Application in Crude Oil Distillation Process Modeling

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

Abstract

Levinson predictor based Compensatory fuzzy neural networks (LPCFNN), which can be trained by a back-propagation learning algorithm, is proposed as a modeling technique for crude oil distillation processes. This approach adds feedback to the input by using Levinson predictor. Simulation experiments are made by applying proposed LPCFNN on modeling for crude oil distillation process to confirm its effectiveness.

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Author information

Authors and Affiliations

  1. Department of Automation, Tsinghua University, Beijing, 100084, China

    Yongfeng He & Quanyi Fan

Authors
  1. Yongfeng He
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  2. Quanyi Fan
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

He, Y., Fan, Q. (2006). A Levinson Predictor Based Compensatory Fuzzy Neural Network and Its Application in Crude Oil Distillation Process Modeling. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_123

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  • DOI: https://doi.org/10.1007/11760191_123

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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