Adaptive learning using higher-order statistics

Michail K. Tsatsanis, Georgios B. Giannakis

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


The classification of random and deterministic signals is considered. A cumulant-based classifier is derived, which is insensitive to additive Gaussian noise and can classify non-minimum phase signals. A computationally efficient implementation structure is proposed, which avoids the explicit computation of cumulants. Its performance analysis is discussed. Adaptive training algorithms for the classifier are also derived, using gradient methods to minimize cumulant-based criteria. Gaussian noise insensitivity is preserved. The authors prove global convergence, irrespective of the initial conditions, and illustrate the tracking capabilities with simulations.

Original languageEnglish (US)
Pages (from-to)1473-1478
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
StatePublished - 1991
EventConference Proceedings of the 1991 IEEE International Conference on Systems, Man, and Cybernetics - Charlottesville, VA, USA
Duration: Oct 13 1991Oct 16 1991


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