Simplified neural networks for solving linear least squares and total least squares problems in real time
作者： A. Cichocki , R. Unbehauen
DOI: 10.1109/72.329687
关键词: Computer science 、 Artificial neural network 、 Adaptive algorithm 、 Non-linear iterative partial least squares 、 Iteratively reweighted least squares 、 Algebraic equation 、 Linear least squares 、 Linear regression 、 Recursive least squares filter 、 Total least squares 、 Algorithm 、 Least squares support vector machine 、 Non-linear least squares 、 Linear system 、 Least squares
摘要: 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 algebraic equations in real time. The least squares (LS), total (TLS) and data (DLS) problems can be considered as modifications extensions well known algorithms: the row-action projection-Kaczmarz algorithm and/or LMS (Adaline) Widrow-Hoff algorithms. applied to any problem which formulated regression problem. correctness high performance illustrated by extensive computer simulation results. >
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