Cross-Correlation Neural Network Models

Konstantinos I. Diamantaras, Sun Yuan Kung

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


In this paper we provide theoretical foundations for a new neural model for singular value decomposition based on an extension of the Hebbian learning rule called the cross-coupled Hebbian rule. The model is extracting the SVD of the cross-correlation matrix of two stochastic signals and is an extension on previous work on neural-network-related principal component analysis (PCA). We prove the asymptotic convergence of the network to the principal (normalized) singular vectors of the cross-correlation and we provide simulation results which suggest that the convergence is exponential. The new model may have useful applications in the problems of filtering for signal processing and signal detection.

Original languageEnglish (US)
Pages (from-to)3218-3223
Number of pages6
JournalIEEE Transactions on Signal Processing
Issue number11
StatePublished - Nov 1994

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


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