Digital Library of Zielona Góra contains 63 052 digital objects
Object
Title: Analog neural networks for solving in real-time linear inverse and total least squares problems
Contributor:
Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.
Group publication title:
Abstract:
A class of simplified low-cost artificial neural networks with on-line adaptive learning algorithms are discussed for solving large system of algebraic equations and related problems in real-time. The proposed learning algorithms for Least Squares (LS), Total Least Squares (TLS) and Data Least Squares (DLS) problems can be considered as an extension and generalization of the well known Least Mean Squares (LMS) and Kaczmarz algorithms. ; A generalized maximum entropy and minimum "p-norm" criteria are used as a principle for reconstructing images "and/or" signals from noisy and incomplete projection data. The inverse problem is reformulated as a suitable optimization problem and solved by a unified neural network.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
Pages:
Source:
AMCS, volume 5, number 1 (1995) ; click here to follow the link
Language:
License CC BY 4.0:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Digital Library of Zielona Góra > Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Digital Library of Zielona Góra > Repository > Types of work > Articles
- Digital Library of Zielona Góra > Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (1995)
Last modified:
Jul 14, 2025
In our library since:
Aug 17, 2020
Number of object content hits:
143
All available object's versions:
https://www.zbc.uz.zgora.pl/publication/64035
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Show description in OAI-PMH format:
| Edition name | Date |
|---|---|
| Analog neural networks for solving in real-time linear inverse and total least squares problems | Jul 14, 2025 |
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