A global transformation approach to RBF neural network learning

Kar Ann Toh, K. Z. Mao

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In this paper, we propose to train the RBF neural network using a global descent method. Essentially, the method imposes a monotonic transformation on the training objective to improve numerical sensitivity without altering the relative orders of all local extrema. A gradient descent search which inherits the global descent property is derived to locate the global solution of an error objective. Numerical examples comparing the global descent algorithm with a gradient-based line-search algorithm shows superiority of the proposed global descent algorithm in terms of speed of convergence and quality of solution achieved.

Original languageEnglish
Pages (from-to)96-99
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Issue number2
Publication statusPublished - 2002

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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