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Modeling pH Neutralization Process Via Support Vector Machines

  • Dongwon Kim
  • Gwi-Tae Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)

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.

Keywords

Support Vector Machine Mean Square Error Kernel Function Continuously Stir Tank Reactor Radial Basis Function Kernel 
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

  • Dongwon Kim
    • 1
  • Gwi-Tae Park
    • 1
  1. 1.Department of Electrical EngineeringKorea UniversitySeoulKorea

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