• DocumentCode
    1752855
  • Title

    Designing Functional Networks Through Evolutionary Programming

  • Author

    Zhou, Yongquan ; Wang, Dongdong ; Zhang, Ming

  • Author_Institution
    Coll. of Comput. & Inf. Sci., Guangxi Univ. for Nationalities, Nanning
  • Volume
    1
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    3250
  • Lastpage
    3254
  • Abstract
    Functional network is a recently introduced extension of neural networks. Like neural networks, nowadays, there is no system designing method for designing approximation functional networks structure. A new genetic programming designing modeling method, combining genetic programming and genetic algorithm, was proposed for hybrid identification of model structure and functional parameters by performing global optimal search in the complex solution space where the structures and parameters coexist and interact. These results also show that the proposed method in this paper can produce very compact network structure and the functional networks convergent precision are improved greatly
  • Keywords
    evolutionary computation; neural nets; search problems; Lagrange multipliers; evolutionary programming; functional network; genetic algorithm; genetic programming designing modeling; global optimal search; hybrid identification; learning algorithm; neural network; neuron function; Algorithm design and analysis; Computer networks; Design methodology; Educational institutions; Electronic mail; Functional programming; Genetic algorithms; Genetic programming; Information science; Neural networks; Functional networks; Genetic programming; Lagrange multipliers technique; Learning algorithm; Neuron function;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
  • Conference_Location
    Dalian
  • Print_ISBN
    1-4244-0332-4
  • Type

    conf

  • DOI
    10.1109/WCICA.2006.1712968
  • Filename
    1712968