A shunting multilayer perceptron network for confusing/composite pattern recognition

Wu Chung-Hsien Wu, Jhing Fa Wang, Wen Horng Wu

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


In this paper, a shunting multilayer perceptron (SMLP) network trained with a modified backpropagation algorithm is proposed. In the SMLP, an MLP network shunts another MLP network in order to learn the relationship between any two training patterns. The SMLP network is endowed with two abilities in relation to patterns in complicated circumstances. Firstly, the SMLP network can perform confusing pattern recognition by weighting the node responses which are important for distinguishing two confusing patterns. Secondly, the SMLP network can perform composite pattern recognition by paying selective attention to the node responses which are distinctive for each individual pattern. The determination of the hidden node number for constructing a suitably sized SMLP network is also discussed here. For the experimental evaluations, ten spoken and handprinted digits and nine spoken and handprinted English alphabets were used. Comparisons to MLP, dynamic time warping algorithm, and nearest neighbor classifier showed satisfactory improvements in recognition accuracy for confusing/composite patterns.

Original languageEnglish
Pages (from-to)1093-1103
Number of pages11
JournalPattern Recognition
Issue number11
Publication statusPublished - 1991

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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