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Bücher Englische Bücher Wissen & Bildung Naturwissenschaft, Medizin, Informatik, Technik Informatik & EDV Informatik Generalized Matrix Inversion: A Machine Learning Approach
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Generalized Matrix Inversion: A Machine Learning Approach
Predrag S. Stanimirović, Yimin Wei, Shuai Li, Dimitrios Gerontitis, Xinwei Cao (Gebundene Ausgabe, Englisch)
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Beschreibung
This book presents a comprehensive exploration of the dynamical system approach in numerical linear algebra, with a special focus on computing generalized inverses, solving systems of linear equations, and addressing linear matrix equations. Bridging four major scientific domains—numerical linear algebra, recurrent neural networks (RNNs), dynamical systems, and unconstrained nonlinear… optimization—this book offers a unique, interdisciplinary perspective. Generalized Matrix Inversion: A Machine Learning Approach explores the theory and application of recurrent neural networks, particularly continuous-time recurrent neural networks (CTRNNs), which use systems of ordinary differential equations to model the influence of inputs on neurons. Special attention is given to CTRNNs designed for finding zeros of equations or minimizing nonlinear functions, with detailed coverage of two important classes: Gradient Neural Networks (GNN) and Zhang (Zeroing) Neural Networks (ZNN). Both time-varying and time-invariant models are examined across scalar, vector, and matrix cases. Based on the authors’ research that has been published in leading scientific journals, the book spans a variety of disciplines, including linear and multilinear algebra, generalized inverses, recurrent neural networks, dynamical systems, time-varying problem solving, and unconstrained nonlinear optimization. Readers will find a global overview of activation functions, rigorous convergence analysis, and innovative improvements in the definition of error functions for GNN and ZNN dynamic systems. Generalized Matrix Inversion: A Machine Learning Approach is an essential resource for researchers and practitioners seeking advanced methods at the intersection of machine learning, optimization, and matrix computation. Dieses Produkt haben wir gerade leider nicht auf Lager.
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Technische Daten
Erscheinungsdatum
03.01.2026
Sprache
Englisch
EAN
9783032014924
Herausgeber
Springer International Publishing
Sonderedition
Nein
Autor
Predrag S. Stanimirović, Yimin Wei, Shuai Li, Dimitrios Gerontitis, Xinwei Cao
Seitenanzahl
333
Einbandart
Gebundene Ausgabe
Schlagwörter
Theory of matrix inversion and generalized inverses, Applications of generalized inverses, Matrix theory and rank, Numerical methods for matrix equations, Linear and multilinear algebra, system of linear equations, recurrent neural networks, Dynamical systems, Learning and adaptive systems in artificial intelligence, Time-varying problems, Activation function, Unconstrained nonlinear optimization
Thema-Inhalt
UYQM - Maschinelles Lernen
TBJ - Mathematik für Ingenieure
GPFC - Kybernetik und Systemtheorie
PBF - Algebra
UYA - Theoretische Informatik
PBKS - Numerische Mathematik
Höhe
235 mm
Breite
15.5 cm
Hersteller: Springer Nature Customer Service Center GmbH, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com
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