Cover Image

Controlling Chaotic Maps using Next-Generation Reservoir Computing

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kent, Robert M., Barbosa, Wendson A. S., Gauthier, Daniel J.
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Neural and Evolutionary Computing Systems and Control Chaotic Dynamics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Attractor-merging Crises and Intermittency in Reservoir Computing
by: Kabayama, Tempei, et al.
Published: (2025)

Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime
by: Abdel-Ghani, Hend, et al.
Published: (2025)

Designing Chaotic Attractors: A Semi-supervised Approach
by: Kabayama, Tempei, et al.
Published: (2024)

Learning Continuous Chaotic Attractors with a Reservoir Computer
by: Smith, Lindsay M., et al.
Published: (2021)

Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing
by: Sedehi, Omid, et al.
Published: (2025)

Controlling Chaos Using Edge Computing Hardware
by: Kent, Robert M., et al.
Published: (2024)

Reservoir computing with logistic map
by: Arun, R., et al.
Published: (2024)

Reconstruction of dynamic systems using genetic algorithms with dynamic search limits
by: Rodríguez-Abreo, Omar, et al.
Published: (2024)

The boundary of neural network trainability is fractal
by: Sohl-Dickstein, Jascha
Published: (2024)

Panda: A pretrained forecast model for chaotic dynamics
by: Lai, Jeffrey, et al.
Published: (2025)

Chaotic Oscillator Networks for Classification Tasks
by: Ivas, Toni, et al.
Published: (2026)

Enhancing Computational Efficiency in Multiscale Systems Using Deep Learning of Coordinates and Flow Maps
by: Hamid, Asif, et al.
Published: (2024)

Identification of fixations and saccades in eye-tracking data using adaptive threshold-based method
by: Orioma, Charles, et al.
Published: (2025)

Machine Learning of Nonlinear Dynamical Systems with Control Parameters Using Feedforward Neural Networks
by: Sakaguchi, Hidetsugu
Published: (2024)

Optimizing the Network Topology of a Linear Reservoir Computer
by: Tangerami, Sahand, et al.
Published: (2025)

Jump-diffusion models of parametric volume-price distributions
by: Budhathoki, Anup, et al.
Published: (2025)

Integer-State Dynamics of Quantized Spiking Neural Networks for Efficient Hardware Acceleration
by: Zhang, Lei
Published: (2026)

Asymmetrically connected reservoir networks learn better
by: Rathor, Shailendra K., et al.
Published: (2024)

Predicting Chaotic System Behavior using Machine Learning Techniques
by: Rao, Huaiyuan, et al.
Published: (2024)

Prediction performance of random reservoirs with different topology for nonlinear dynamical systems with different number of degrees of freedom
by: Rathor, Shailendra K., et al.
Published: (2025)

Freezing chaos without synaptic plasticity
by: Huang, Weizhong, et al.
Published: (2025)

Revisiting time-variant complex conjugate matrix equations with their corresponding real field time-variant large-scale linear equations, neural hypercomplex numbers space compressive approximation approach
by: He, Jiakuang, et al.
Published: (2024)

Reconfigurable Nonlinear Photonic Networks for In-Situ Learning and Memory Formation via Driven-Dissipative Dynamics
by: Yorke, Isaac
Published: (2026)

Reservoir Predictive Path Integral Control for Unknown Nonlinear Dynamics
by: Inoue, Daisuke, et al.
Published: (2025)

Stochastic Reservoir Computers
by: Ehlers, Peter J., et al.
Published: (2024)

Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data
by: Maksymov, Ivan S.
Published: (2024)

Designing Robust Software Sensors for Nonlinear Systems via Neural Networks and Adaptive Sliding Mode Control
by: Farkane, Ayoub, et al.
Published: (2025)

Proposed modified computational model for the amoeba-inspired combinatorial optimization machine
by: Miyajima, Yusuke, et al.
Published: (2024)

Analysis of chaotic dynamical systems with autoencoders
by: Almazova, N., et al.
Published: (2021)

Harnessing and modulating chaos to sample from neural generative models
by: Chaudhuri, Rishidev, et al.
Published: (2024)

Adjusting Dynamics of Hopfield Neural Network via Time-variant Stimulus
by: Peng, Xuenan, et al.
Published: (2024)

Controlling Dynamical Systems into Unseen Target States Using Machine Learning
by: Köglmayr, Daniel, et al.
Published: (2024)

Hierarchy of chaotic dynamics in random modular networks
by: Kuśmierz, Łukasz, et al.
Published: (2024)

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective
by: Abbas, A. H., et al.
Published: (2024)

Locality Blended Next Generation Reservoir Computing For Attention Accuracy
by: Gauthier, Daniel J., et al.
Published: (2025)

Universal Sharpness Dynamics in Neural Network Training: Fixed Point Analysis, Edge of Stability, and Route to Chaos
by: Kalra, Dayal Singh, et al.
Published: (2023)

Neural Ising Machines via Unrolling and Zeroth-Order Training
by: Reifenstein, Sam, et al.
Published: (2026)

Physics-Informed Echo State Networks for Modeling Controllable Dynamical Systems
by: Mochiutti, Eric, et al.
Published: (2024)

Accurate Data-Based State Estimation from Power Loads Inference in Electric Power Grids
by: Jacquod, Philippe, et al.
Published: (2026)

Machine-Precision Prediction of Low-Dimensional Chaotic Systems
by: Schötz, Christof, et al.
Published: (2025)