Generalized Matrix Inversion: A Machine Learning Approach
- Publisher's listprice EUR 213.99
-
88 752 Ft (84 526 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 12% (cc. 10 650 Ft off)
- Discounted price 78 102 Ft (74 383 Ft + 5% VAT)
Subcribe now and take benefit of a favourable price.
Subscribe
88 752 Ft
Availability
Not yet published.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Springer Nature Switzerland
- Date of Publication 3 January 2026
- Number of Volumes 1 pieces, Book
- ISBN 9783032014924
- Binding Hardback
- No. of pages333 pages
- Size 235x155 mm
- Language English
- Illustrations XXXVII, 333 p. 135 illus., 74 illus. in color. 700
Categories
Long description:
"
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.
" MoreTable of Contents:
1. Background Information.- 2 Gradient Neural Network (GNN) and their Modifications.- 3 Zeroing Neural Network (ZNN).- 4 From iterations to ZNNs and vice versa, 5 Modified ZNN dynamical systems.
More
