• DocumentCode
    2992158
  • Title

    Research on the Prediction of Breath Period Signal Based on RFN Network of Self-Adaptive Genetic Algorithm

  • Author

    Junjie, Su ; Qiuhai, Zhong

  • Author_Institution
    Coll. of Autom. Control, Beijing Inst. of Technol., Beijing, China
  • fYear
    2010
  • fDate
    25-27 June 2010
  • Firstpage
    1798
  • Lastpage
    1801
  • Abstract
    A hybrid algorithm -RFN network of self-adaptive genetic algorithm was introduced, which combined the excellences of BP network, RFN network and genetic algorithm. The hybrid algorithm adopts the learning rule of RFN network and combines self-adaptive genetic algorithm and gradient descent method. The capability of prediction can be optimized using the hybrid algorithm and the shortcoming of the learning rule of RFN network was overcomed. At the same time, the problem that Global Optimal Solution always cann´t be found only with genetic algorithm was solved. Simulation results show that hybrid algorithm can obtain better forecasting precision.
  • Keywords
    backpropagation; genetic algorithms; gradient methods; prediction theory; recurrent neural nets; BP network; RFN network; breath period signal prediction; global optimal solution; gradient descent method; hybrid algorithm; learning rule; self-adaptive genetic algorithm; Companies; Educational institutions; Gallium; Home appliances; Medical services; Prediction algorithms; Simulation; Breath period signal; RFN network; Self-adaptive genetic algorithm; prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Control Engineering (ICECE), 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-6880-5
  • Type

    conf

  • DOI
    10.1109/iCECE.2010.442
  • Filename
    5630480