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[Author] Jong-Seok LEE(2hit)

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  • Self-Organizing Neural Networks by Construction and Pruning

    Jong-Seok LEE  Hajoon LEE  Jae-Young KIM  Dongkyung NAM  Cheol Hoon PARK  

     
    PAPER-Biocybernetics, Neurocomputing

      Vol:
    E87-D No:11
      Page(s):
    2489-2498

    Feedforward neural networks have been successfully developed and applied in many areas because of their universal approximation capability. However, there still remains the problem of determining a suitable network structure for the given task. In this paper, we propose a novel self-organizing neural network which automatically adjusts its structure according to the task. Utilizing both the constructive and the pruning procedures, the proposed algorithm finds a near-optimal network which is compact and shows good generalization performance. One of its important features is reliability, which means the randomness of neural networks is effectively reduced. The resultant networks can have suitable numbers of hidden neurons and hidden layers according to the complexity of the given task. The simulation results for the well-known function regression problems show that our method successfully organizes near-optimal networks.

  • n-Dimensional Cauchy Neighbor Generation for the Fast Simulated Annealing

    Dongkyung NAM  Jong-Seok LEE  Cheol Hoon PARK  

     
    LETTER-Algorithm Theory

      Vol:
    E87-D No:11
      Page(s):
    2499-2502

    Many simulated annealing algorithms use the Cauchy neighbors for fast convergence, and the conventional method uses the product of n one-dimensional Cauchy distributions as an approximation. However, this method slows down the search severely as the dimension gets high because of the dimension-wise neighbor generation. In this paper, we analyze the orthogonal neighbor characteristics of the conventional method and propose a method of generating symmetric neighbors from the n-dimensional Cauchy distribution. The simulation results show that the proposed method is very effective for the search in the simulated annealing and can be applied to many other stochastic optimization algorithms.