Combining Particle Swarm Optimization and Neural Network for Diagnosis of Unexplained Syncope

  • Liang Gao
  • Chi Zhou
  • Hai-Bing Gao
  • Yong-Ren Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)


Given the relative limitations of BP and GA based leaning algorithms, Particle Swarm Optimization (PSO) is proposed to train Artificial Neural Networks (ANN) for the diagnosis of unexplained syncope. Compared with BP and GA based training techniques, PSO based learning method improves the diagnosis accuracy and speeds up the convergence process. Experimental results show that PSO is a robust training algorithm and should be extended to other real-world pattern classification applications.


Artificial Neural Network Training Algorithm Swarm Intelligence Inertial Weight Computational Expense 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Liang Gao
    • 1
  • Chi Zhou
    • 1
  • Hai-Bing Gao
    • 1
  • Yong-Ren Shi
    • 1
  1. 1.Department of Industrial & Manufacturing System EngineeringHuazhong Univ.of Sci. & Tech.WuhanChina

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