Combinative neural network and its applications

  • Authors:

  • Yaqiu Chen;Shangxu Hu;Dezhao Chen

  • Affiliations:

  • Department of Chemical Engineering, National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, People's Republic of China;Department of Chemical Engineering, National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, People's Republic of China;Department of Chemical Engineering, National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, People's Republic of China

  • Venue:
  • Computational Biology and Chemistry
  • Year:
  • 2003

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Abstract

A new approach named combinative neural network (CN) using partial least squares (PLS) analysis to modify the hidden layer in the multi-layered feed forward (MLFF) neural networks (NN) was proposed in this paper. The significant contributions of PLS in the proposed CN are to reorganize the outputs of hidden nodes such that the correlation of variables could be circumvented, to make the network meet the non-linear relationship best between the input and output data of the NN, and to eliminate the risk of over-fitting problem at the same time. The performance of the proposed approach was demonstrated through two examples, a well defined nonlinear approximation problem, and a practical nonlinear pattern classification problem with unknown relationship between the input and output data. The results were compared with those by conventional MLFF NNs. Good performance and time-saving implementation make the proposed method an attractive approach for non-linear mapping and classification.