Simplified Neural Networks for Solving Linear Least Squares and Total Least Squares Problems in Real Time

Andrzej Cichocki, Rolf Unbehauen

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

In this paper a new class of simplified low-cost analog artificial neural networks with on chip adaptive learning algorithms are proposed for solving linear systems of algebraic equations in real time. The proposed learning algorithms for linear least squares (LS), total least squares (TLS) and data least squares (DLS) problems can be considered as modifications and extensions of well known algorithms: the row-action projection-Kaczmarz algorithm [25] and/or the LMS (Adaline) Widrow-Hoff algorithms [21]. The algorithms can be applied to any problem which can be formulated as a linear regression problem. The correctness and high performance of the proposed neural networks are illustrated by extensive computer simulation results.

Original languageEnglish
Pages (from-to)910-923
Number of pages14
JournalIEEE Transactions on Neural Networks
Volume5
Issue number6
DOIs
Publication statusPublished - Nov 1994
Externally publishedYes

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