• DocumentCode
    3390342
  • Title

    GA -RBF model and its application in evaluation of water quality

  • Author

    Zhu, Changjun ; Li, Sha ; Wu, Liping

  • Author_Institution
    Coll. of Urban Constr., Hebei Univ. of Eng., Handan, China
  • Volume
    3
  • fYear
    2009
  • fDate
    19-20 Dec. 2009
  • Firstpage
    25
  • Lastpage
    28
  • Abstract
    By combining GA (Genetic Algorithm), which has the advantage of global optimization, and RBF, which has the advantage of local optimization, the calculation accuracy and convergence rate of the traditional RBF neural network are improved. So a combinational evaluation model is presented based on GA and RBF neural network. And it is applied to comprehensive analysis and evaluation of water quality, not only retains the original merits of the neural network, but also overcomes these shortcomings, and establishes water quality evaluation model. The experimental results show that the hybrid algorithm model has evaluation of high precision, and can be applied to water quality evaluation. The simulation result shows this method has high convergent speed and easily oriented global optimization and is therefore of great practical value.
  • Keywords
    genetic algorithms; radial basis function networks; water resources; RBF neural network; combinational evaluation model; genetic algorithm; radial basis function network; water quality evaluation; Artificial neural networks; Biological neural networks; Convergence; Genetic algorithms; Genetic mutations; Intelligent networks; Intelligent transportation systems; Neural networks; Optimization methods; Signal processing algorithms; RBF neural network; genetic algorithm; water quality;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Electronics and Intelligent Transportation System (PEITS), 2009 2nd International Conference on
  • Conference_Location
    Shenzhen
  • Print_ISBN
    978-1-4244-4544-8
  • Type

    conf

  • DOI
    10.1109/PEITS.2009.5406848
  • Filename
    5406848