Modeling pH Neutralization Process Via Support Vector Machines (original) (raw)

Abstract

This paper discusses the use of support vector machines for modeling and identification of pH neutralization process. Support vector machines (SVM) and kernel method have become very popular as methods for learning from examples. We apply SVM to model pH process which has strong nonlinearities. The experimental results show that the SVM based on the kernel substitution including linear and radial basis function kernel provides a promising alternative to model strong nonlinearities of the pH neutralization but also to control the system. Comparisons with other modeling methods show that the SVM method offers encouraging advantages and has better performance.

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Authors and Affiliations

  1. Department of Electrical Engineering, Korea University, 1, 5-ka, Anam-dong, Seongbuk-ku, Seoul, 136-701, Korea
    Dongwon Kim & Gwi-Tae Park

Authors

  1. Dongwon Kim
  2. Gwi-Tae Park

Editor information

Editors and Affiliations

  1. Department of Computer Science, Texas State University-San Marcos, Nueces 247, 601 University Drive, 78666-4616, San Marcos, TX, USA
    Moonis Ali
  2. ESIA Laboratoire d’Informatique, Sytèmes, Traitement de l’Information et de la Connaissance, Université de Savoie, B.P. 806, F-74016, ANNECY Cedex, France
    Richard Dapoigny

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© 2006 Springer-Verlag Berlin Heidelberg

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Kim, D., Park, GT. (2006). Modeling pH Neutralization Process Via Support Vector Machines. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568\_89

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