Rapid backpropagation learning algorithms

Sung Bae Cho, Jin H. Kim

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


One of the major drawbacks of the backpropagation algorithm is its slow rate of convergence. Researchers have tried several different approaches to speed up the convergence of backpropagation learning. In this paper, we present those rapid learning methods as three categories, and implement the representative methods of each category: (1) for the numerical method based approach, the Aitken's Δ2 process, (2) for the heuristics based approach, the dynamic adaptation of learning rate, and (3) for the learning strategy based approach, the selective presentation of learning samples. Based on these implementations, the performance is evaluated with experiments and the merits and demerits are briefly discussed.

Original languageEnglish
Pages (from-to)155-175
Number of pages21
JournalCircuits, Systems, and Signal Processing
Issue number2
Publication statusPublished - 1993 Jun

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Applied Mathematics


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