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GA-Based RBF Neural Network for Nonlinear SISO System

In: DNA Computing Based Genetic Algorithm

Author

Listed:
  • Jili Tao

    (NingboTech University, School of Information Science and Engineering)

  • Ridong Zhang

    (Hangzhou Dianzi University, The Belt and Road Information Research Institute)

  • Yong Zhu

    (NingboTech University, School of Information Science and Engineering)

Abstract

Radial basis function (RBF) neural network is efficient to model nonlinear systems with its simpler network structure and faster learning capability. The temperature and pressure modeling of the coke furnace in an industrial coke equipment is not very easy due to disturbances, nonlinearity, and switches of coke towers. To construct the temperature and pressure models in a coke furnace, RBF neural network is utilized to improve the modeling precision. Moreover, the shortcoming of RBF neural network, such as over-fitting is overcome.

Suggested Citation

  • Jili Tao & Ridong Zhang & Yong Zhu, 2020. "GA-Based RBF Neural Network for Nonlinear SISO System," Springer Books, in: DNA Computing Based Genetic Algorithm, chapter 0, pages 119-166, Springer.
    • Handle: RePEc:spr:sprchp:978-981-15-5403-2_6
    DOI: 10.1007/978-981-15-5403-2_6
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