Contrast enhancement for backpropagation

Taek M Kwon, Hui Cheng

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


This paper analyzes the effect of data-contrast to a backpropagation (BP) network and introduces a data preprocessing algorithm that can improve the efficiency of the standard BP learning. The basic idea is to transform input data to a range that associates the high-slope region of the sigmoid function where a relatively large modification of weights occurs. A simple uniform transformation to such a desired range, however, can lead to a slow and unbalanced learning if the data distribution is heavily skewed. To facilitate data processing on such distribution, we propose a modified histogram equalization technique which enhances the spacing between the data points in the heavily concentrated regions of the distribution.

Original languageEnglish (US)
Pages (from-to)515-524
Number of pages10
JournalIEEE Transactions on Neural Networks
Issue number2
StatePublished - 1996


Dive into the research topics of 'Contrast enhancement for backpropagation'. Together they form a unique fingerprint.

Cite this