Generalization ability of universal learning network by using second order derivatives

M. Han, K. Hirasawa, J. Hu, J. Murata

Research output: Contribution to journalConference article

4 Citations (Scopus)

Abstract

In this paper, it is studied how the generalization ability of modeling of the dynamic systems can be improved by taking advantages of the second order derivatives of the criterion function with respect to the external inputs. The proposed method is based on the regularization theory proposed by Poggio, but a main distinctive point in this paper is that extension to dynamic systems from static systems has been taken into account and actual second order derivatives of the Universal Learning Network have been used to train the parameters of the networks. The second order derivatives term of the criterion function may minimize the deviation caused by the external input changes. Simulation results show that the method is useful for improving the generalization ability of identifying nonlinear dynamic systems using neural networks.

Original languageEnglish
Pages (from-to)1818-1823
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume2
Publication statusPublished - 1998
EventProceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 2 (of 5) - San Diego, CA, USA
Duration: 1998 Oct 111998 Oct 14

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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