A new modified hybrid learning algorithm for feedforward neural networks

Fei Han*, Deshuang Huang, Yiu Ming CHEUNG, Guangbin Huang

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)

Abstract

In this paper, a new modified hybrid learning algorithm for feedforward neural networks is proposed to obtain better generalization performance. For the sake of penalizing both the input-to-output mapping sensitivity and the high frequency components in training data, the first additional cost term and the second one are selected based on the first-order derivatives of the neural activation at the hidden layers and the second-order derivatives of the neural activation at the output layer, respectively. Finally, theoretical justifications and simulation results are given to verify the efficiency and effectiveness of our proposed learning algorithm.

Original languageEnglish
Pages (from-to)572-577
Number of pages6
JournalLecture Notes in Computer Science
Volume3496
Issue numberI
DOIs
Publication statusPublished - 2005
EventSecond International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China
Duration: 30 May 20051 Jun 2005

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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