Application of likelihood search method to neural networks learning

Masaru Koga, Kotaro Hirasawa, Junichi Murata, Masanao Ohbayashi

Research output: Contribution to journalArticlepeer-review


This article mainly compares learning performances between the Likelihood Search Method (L.S.M.) and the Back Propagation Method (B.P.). The performances are evaluated by the simulations which include both static and dynamic Neural Networks (N.N.) learning problems. In the simulations, N.N. is trained to realize nonlinear functions or control a nonlinear crane system by using the L.S.M. and B.P.. Simulation results show that the L.S.M. is superior to the B.P. because of the ability of intensification and diversification of the search.

Original languageEnglish
Pages (from-to)83-97
Number of pages15
JournalMemoirs of the Kyushu University, Faculty of Engineering
Issue number2
Publication statusPublished - Jun 1 1996

All Science Journal Classification (ASJC) codes

  • Energy(all)
  • Atmospheric Science
  • Earth and Planetary Sciences(all)
  • Management of Technology and Innovation


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