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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8440462 | PMC |
| http://dx.doi.org/10.1111/jce.15169 | DOI Listing |
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Orbits of families of discrete dynamical systems evolving in the natural numbers.
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January 2025
Division of Control and Dynamical Systems, Instituto Potosino de Investigación Científica y Tecnológica, Camino a la Presa San José 2055, Col. Lomas 4ta. Sección, 78216 San Luis Potosí, SLP, México.
In this paper, we give a class of one-dimensional discrete dynamical systems with state space N+. This class of systems is defined by two parameters: one of them sets the number of nearest neighbors that determine the rule of evolution, and the other parameter determines a segment of natural numbers Λ={1,2,…,b}. In particular, we investigate the behavior of a class of one-dimensional maps where an integer moves to an other integer given by the sum of the nearest neighbors minus a multiple of b∈N+.
View Article and Find Full Text PDFIn this paper, we study the problem of predicting optical chaos for semiconductor lasers, where data uncertainty can severely degrade the performance of chaos prediction. We hereby propose a multi-stage extreme learning machine (MSELM) based approach for the continuous prediction of optical chaos, which handles data uncertainty effectively. Rather than relying on pilot signals for conventional reservoir learning, the proposed approach enables the use of predicted optical intensity as virtual training samples for the MSELM model learning, which leads to enhanced prediction performance and low overhead.
View Article and Find Full Text PDFModel-free distributed state estimation with local measurements.
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November 2024
College of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
In this paper, the state estimation problem of physical plants with unknown system dynamic is revisited from the perspective of limited output information measurement, which corresponds to those with characteristics of high-dimensional, wide-area coverage and scatter. Given this fact, a network of sensors are used to carry out the measurement with each one accessing only partial outputs of the targeted systems and a novel model-free state estimation approach, named distributed stochastic variational inference state estimation, is proposed. The key idea of this method is to compensate for the impacts of local output measurements by adding nearest-neighbor rule-based information interaction among estimators to complete the state estimation.
View Article and Find Full Text PDFScore-driven exponential random graphs: A new class of time-varying parameter models for temporal networks.
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Department of Mathematics, University of Bologna, Piazza di Porta San Donato 5, 40126 Bologna, Italy and Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy.
Motivated by the increasing abundance of data describing real-world networks that exhibit dynamical features, we propose an extension of the exponential random graph models (ERGMs) that accommodates the time variation of its parameters. Inspired by the fast-growing literature on dynamic conditional score models, each parameter evolves according to an updating rule driven by the score of the ERGM distribution. We demonstrate the flexibility of score-driven ERGMs (SD-ERGMs) as data-generating processes and filters and show the advantages of the dynamic version over the static one.
View Article and Find Full Text PDFEquilibrium seeking of higher-order networks under facet cover constraints.
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October 2024
School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.
Article Synopsis
- Agent intelligence relies on social attributes where decisions are influenced by group dynamics, aiming to benefit both individuals and their groups.
- The paper explores an anticoordination game on a complex network to ensure each subgroup achieves positive outcomes, focusing on the facet cover problem and introducing a facet update rule for group interactions.
- Findings indicate that this new update rule helps nodes converge to a strict Nash equilibrium efficiently, while competing asynchronous updates tend to involve more nodes in their equilibrium states.
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