Improvement of Learning Capability with Combination of the Generalized Cascade Correlation and Generalized Recurrent Cascade Correlation Algorithms

일반화된 캐스케이드 코릴레이션 알고리즘과 일반화된 순환 캐스케이드 코릴레이션 알고리즘의 결합을 통한 학습 능력 향상

  • 이상화 (서원대학교 컴퓨터정보통신공학부) ;
  • 송해상 (서원대학교 컴퓨터정보통신공학부)
  • Published : 2009.02.28


This paper presents a combination of the generalized Cascade Correlation and generalized Recurrent Cascade Correlation learning algorithms. The new network will be able to grow with vertical or horizontal direction and with recurrent or without recurrent units for the quick solution of the pattern classification problem. The proposed algorithm was tested learning capability with the sigmoidal activation function and hyperbolic tangent activation function on the contact lens and balance scale standard benchmark problems. And results are compared with those obtained with Cascade Correlation and Recurrent Cascade Correlation algorithms. By the learning the new network was composed with the minimal number of the created hidden units and shows quick learning speed. Consequently it will be able to improve a learning capability.


(Recurrent) Cascade Correlation Algorithm;Activation Function;Sigmoid Function;Hyperbolic Tangent Function


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