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Nonlinear Process Modelling and Control Using Neurofuzzy Networks

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Abstract

This chapter presents neurofuzzy networks for nonlinear process modeling and control. The neurofuzzy network uses local linear models to model a nonlinear process and the local linear models are combined using center of gravity (COG) defuzzification. In order to be able to provide accurate long-range predictions, a recurrent neurofuzzy network structure is developed. An advantage of neurofuzzy network models is that they are easy to interpret. Insight about the process characteristics at different operating regions can be easily obtained from the neurofuzzy network parameters. Based on the neurofuzzy network model, nonlinear model predictive controllers can be developed as a nonlinear combination of several local linear model predictive controllers that have analytical solutions. Applications to the modeling and control of a neutralization process and a fed-batch process demonstrate that the proposed recurrent neurofuzzy network is very effective in the modeling and control of nonlinear processes.

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Author information

Authors and Affiliations

  1. School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, UK

    Jie Zhang

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  1. Jie Zhang
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Editor information

Editors and Affiliations

  1. LIACS, Leiden University, Leiden, The Netherlands

    Grzegorz Rozenberg

  2. Computer Science Department, University of Colorado, Boulder, USA

    Grzegorz Rozenberg

  3. LIACS, Leiden University, Leiden, The Netherlands

    Thomas Bäck

  4. LIACS, Leiden University, Leiden, The Netherlands

    Joost N. Kok

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Zhang, J. (2012). Nonlinear Process Modelling and Control Using Neurofuzzy Networks. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_12

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