Fast Pattern Classification with the Multi-layer Cellular Nonlinear Networks (CNN)

다층 셀룰라 비선형 회로망(CNN)을 이용한 고속 패턴 분류

  • 오태완 (전북대 공대 제어계측공학과) ;
  • 이혜정 (전북대 공대 제어계측공학과) ;
  • 손홍락 (전북대 공대 전기정보공학부) ;
  • 김형석 (전북대 공대 전기정보공학부)
  • Published : 2003.09.01


A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.


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