World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
13530
World Ranking
7773
National Ranking
3359

Overview

Charles W. Anderson is affiliated with Colorado State University in the United States. Their research primarily spans the field of Computer Science, with a focus on several subfields including Artificial Intelligence, Atmospheric Science, Cognitive Neuroscience, Ecology, and Electrical and Electronic Engineering.

Their scholarly work covers a range of topics such as Neural Networks and Applications, Reinforcement Learning in Robotics, Meteorological Phenomena and Simulations, Neural Networks and Reservoir Computing, Anomaly Detection Techniques and Applications, Wildlife Ecology and Conservation, and Species Distribution and Climate Change.

Charles W. Anderson has authored multiple papers published across several recognized venues. Selected recent publications include:

  • Looking Back on the Actor-Critic Architecture, 2020, IEEE Transactions on Systems Man and Cybernetics Systems
  • Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks, 2022, Journal of Advances in Modeling Earth Systems
  • The Wisdom of the Crowd: Reliable Deep Reinforcement Learning Through Ensembles of Q-Functions, 2021, IEEE Transactions on Neural Networks and Learning Systems
  • Workshops of the eighth international brain-computer interface meeting: BCIs: the next frontier, 2022, Brain-Computer Interfaces
  • Gradient boosting in crowd ensembles for Q-learning using weight sharing, 2020, International Journal of Machine Learning and Cybernetics

Their frequent coauthors include Jason Stock, Imme Ebert-Uphoff, Mariela G. Gantchoff, Jerrold L. Belant, and Indrakshi Ray, reflecting collaborative efforts in their research projects.

Publications by Anderson appear recurrently in venues such as arXiv (Cornell University), IEEE Transactions on Systems Man and Cybernetics Systems, Journal of Advances in Modeling Earth Systems, IEEE Transactions on Neural Networks and Learning Systems, and Brain-Computer Interfaces. This range indicates an engagement with both open-access preprint archives and specialized peer-reviewed journals.

Best Publications

  • Neuronlike adaptive elements that can solve difficult learning control problems

    Andrew G. Barto;Richard S. Sutton;Charles W. Anderson

  • Comparison of linear, nonlinear, and feature selection methods for EEG signal classification

    D. Garrett;D.A. Peterson;C.W. Anderson;M.H. Thaut

  • Learning to control an inverted pendulum using neural networks

    C.W. Anderson

  • Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks

    C.W. Anderson;E.A. Stolz;S. Shamsunder

  • Linear and nonlinear methods for brain-computer interfaces

    K.-R. Muller;C.W. Anderson;G.E. Birch

  • Docker [Software engineering]

    Unknown

  • BCI meeting 2005-workshop on BCI signal processing: feature extraction and translation

    D.J. McFarland;C.W. Anderson;K.-R. Muller;A. Schlogl

  • Classification of EEG Signals from Four Subjects During Five Mental Tasks

    Charles W. Anderson

  • Genetic Reinforcement Learning for Neurocontrol Problems

    Darrell Whitley;Stephen Dominic;Rajarshi Das;Charles W. Anderson

  • Strategy Learning with Multilayer Connectionist Representations

    Charles W. Anderson

  • Critical issues in state-of-the-art brain–computer interface signal processing

    Dean J Krusienski;Moritz Grosse-Wentrup;Ferran Galán;Damien Coyle

  • Learning and Problem Solving with Multilayer Connectionist Systems

    Charles W. Anderson

  • Learning and problem-solving with multilayer connectionist systems (adaptive, strategy learning, neural networks, reinforcement learning)

    Charles William Anderson

  • Viewing Forced Climate Patterns Through an AI Lens

    Elizabeth A. Barnes;James W. Hurrell;Imme Ebert-Uphoff;Imme Ebert-Uphoff;Chuck Anderson

  • Determining mental state from EEG signals using parallel implementations of neural networks

    Charles W. Anderson;Saikumar V. Devulapalli;Erik A. Stolz

  • Indicator patterns of forced change learned by an artificial neural network

    Elizabeth A. Barnes;Benjamin Toms;James W. Hurrell;Imme Ebert-Uphoff;Imme Ebert-Uphoff

  • Dissociating the contributions of independent corticostriatal systems to visual categorization learning through the use of reinforcement learning modeling and Granger causality modeling.

    Carol A. Seger;Erik J. Peterson;Corinna M. Cincotta;Dan Lopez-Paniagua

  • Comparison of CMACs and radial basis functions for local function approximators in reinforcement learning

    R.M. Kretchmar;C.W. Anderson

  • MIMO Robust Control for HVAC Systems

    M. Anderson;M. Buehner;P. Young;D. Hittle

  • A challenging set of control problems

    Charles W. Anderson;W. Thomas Miller

  • Discriminating mental tasks using EEG represented by AR models

    C.W. Anderson;E.A. Stolz;S. Shamsunder

  • Feature selection and blind source separation in an EEG-based brain-computer interface

    David A. Peterson;James N. Knight;Michael J. Kirby;Charles W. Anderson

Frequent Co-Authors

Thomas M. Chen
Thomas M. Chen City, University of London
Andrew G. Barto
Andrew G. Barto University of Massachusetts Amherst
Sudeep Pasricha
Sudeep Pasricha Colorado State University
Elizabeth A. Barnes
Elizabeth A. Barnes Colorado State University
Christoph Guger
Christoph Guger Graz University of Technology
Ricardo Chavarriaga
Ricardo Chavarriaga École Polytechnique Fédérale de Lausanne
Brendan Z. Allison
Brendan Z. Allison University of California, San Diego
Michael H. Thaut
Michael H. Thaut University of Toronto
Gernot R. Müller-Putz
Gernot R. Müller-Putz Graz University of Technology
Indrakshi Ray
Indrakshi Ray Colorado State University

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