predrag stanimirovi¿ (5 Ergebnisse)

Taschenbuch. Zustand: Neu. Hybrid Methods for Modeling and Optimizing Complex Systems: Advances in Interdisciplinary Approaches for Complex Problem Solving | Proceedings of the IV International Workshop on Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-IV 2025), Volume 1 | Predrag S. Stanimirovi¿ (u. a.) | Taschenbuch | Lecture Notes in Networks and Systems | xi | Englisch | 2026 | Springer | EAN 9783032244017 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Hybrid Methods for Modeling and Optimizing Complex Systems | Advances in Interdisciplinary Approaches for Complex Problem Solving | Predrag S. Stanimirovi¿ (u. a.) | Taschenbuch | Lecture Notes in Networks and Systems | xii | Englisch | 2025 | Springer | EAN 9783031956485 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. 6th EAI International Conference on Robotic Sensor Networks | Predrag S. Stanimirovi¿ (u. a.) | Taschenbuch | EAI/Springer Innovations in Communication and Computing | xvi | Englisch | 2024 | Springer | EAN 9783031338281 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 Approachis an essential resource for researchers and practitioners seeking advanced methods at the intersection of machine learning, optimization, and matrix computation.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Delivering innovative methods for addressing complex systems, this book presents the latest advances in hybrid modeling, machine learning, and digital technologies. Based on selected papers from the III International Workshop Hybrid Methods of Modeling and Optimization in Complex Systems held December 2 4, 2024, in Krasnoyarsk, Russia, the book covers hybrid modeling and optimization, intelligent data analysis, financial forecasting, industrial and educational digitalization, AI-guided decision support, and digital system security. Readers will find such interdisciplinary applications as climate project modeling, agricultural digital services, and the digital platform economy; e-learning analysis and digital competence development; digital twins and production optimization; as well as research on network systems. It is essential for researchers, practitioners, and educators seeking practical solutions and advanced hybrid methods for diverse scientific and engineering challenges.