A new learning method using local and global information for neural networks

Baiquan Lu, Junichi Murata, Kotaro Hirasawa, Hong Gu

Research output: Contribution to journalArticlepeer-review


A new learning method is proposed, which can be free from local minima of error function by using prior information. Because prior information can describe some features of teach function, neural networks also must have the features after learning. For this, learning using the prior information must attain two targets: learning of the features of teach function and a good approximation accuracy. The proposed method is very promising for solving the generalization ability problem of neural networks and avoiding the convergence to local minima. A bound on learning rate is also given for stability of the proposed method. The simulation results indicate usefulness of the proposed method.

Original languageEnglish
Pages (from-to)55-60
Number of pages6
JournalResearch Reports on Information Science and Electrical Engineering of Kyushu University
Issue number2
Publication statusPublished - Sep 1 2004

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering


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