Publications

Click on the following links or my Google Scholar and Research Gate pages.

2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, 1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986, 1985, 1983, 1982, 1979, Unpublished Reports, Advisee Dissertations and Theses

Copyright Notice: The PDF articles provided herein are for your personal, scholarly, noncommercial use. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. These works may not be reposted without the explicit permission of the copyright holder. If you notice that any links are not working, please let me know at chuck.anderson@colostate.edu. Thank you.

2017

  • Can a Reinforcement Learning Agent Practice Before It Starts Learning? [DOI] M. Lee and C. Anderson Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN’17), May 2017.
  • Word Clustering as a Feature for Arabic Sentiment Classification [DOI] S. Alotaibi and C. Anderson International Journal of Education and Management Engineering, vol. 1, pp. 1-13, 2017.

2016

  • Detecting P300 ERPs with Convolutional Networks [DOI] E. Forney, C. Anderson, P. Davies, W. Gavin, M. Roll Proceedings of the Sixth International Brain-Computer Interface Meeting: BCI Past, Present, and Future, May 30 – June 3 2016, Asilomar Conference Center, Pacific Grove, California, USA, page 206, 2016.
  • CEBL3: A New Software Platform for EEG Analysis and Rapid Prototyping of BCI Technologies [DOI] E. Forney, C. Anderson, W. Gavin, P. Davies, M. Roll, I. Ryzhkov, and F. Vafaei Proceedings of the Sixth International Brain-Computer Interface Meeting: BCI Past, Present, and Future, May 30June 3 2016, Asilomar Conference Center, Pacific Grove, California, USA, page 145, 2016.
  • Relevance Vector Sampling for Reinforcement Learning in Continuous Action Space [DOI] M. Lee and C. Anderson Proceedings of the 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA’16), December 2016.
  • Robust Reinforcement Learning with Relevance Vector Machines [PDF] M. Lee and C. Anderson Proceedings of the 1st International Workshop on Robot Learning and Planning (RLP 2016) in conjunction with 2016 Robotics: Science and Systems, Ann Arbor, Michigan, USA. June 18, 2016.
  • Extending the Knowledge of the Arabic Sentiment Classification Using a Foreign External Lexical Source [DOI] S. Alotaibi and C. Anderson International Journal on Natural Language Computing (IJNLC), Vol. 5, No.3, June 2016.
  • To Fear or Not to Fear That is the Question: Code Characteristics of a Vulnerable Function with an Existing Exploit [DOI] A. Younis, Y. Malaiya, C. Anderson, and I. Ray Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, (CODASPY 2016), ACM, New York, pp. 97-104, 2016.


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2015

  • Discovering Spatial and Temporal Patterns in Climate Data Using Deep Learning. [PDF] (Poster [PDF]) C. Anderson, I. Ebert-Uphoff, Y. Deng, and M. Ryan. Proceedings of the Fifth International Workshop on Climate Informatics: CI 2015, edited by J. G. Dy, J. Emile-Geay, V. Lakshmanan, and Y. Liu, September 2015.
  • Faster Reinforcement Learning After Pretraining Deep Networks to Predict State Dynamics. [PDF] [DOI] C. Anderson, M. Lee, and D. Elliott. Proceedings of the 2015 International Joint Conference on Neural Networks, Killarney, Ireland, 2015. Best Overall Paper Award.
  • Capturing Negation Scope Using Base Phrase Chunk in Arabic Sentiment Classification. S. Alotaibi and C. Anderson. Proceedings of the International Conference on Collaboration Technologies and Systems (CTS 2015), pp. 376-382, 2015.
  • LearnLoc: Mobile Learning for Smart Indoor Localization. [DOI] V. Ugave, S. Pasricha, C. Anderson, and Q. Han. Proceedings of CODES ’15 Proceedings of the 10th International Conference on Hardware/Software Codesign and System Synthesis, (CODES-2015), pp. 37-44, IEEE Press Piscataway, NJ, 2015.


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2014

  • Failure-Resilient Real-Time Processing of Health Streams. [DOI] K. Ericson, S. Pallickara, and C. Anderson. Concurrency and Computation: Practice and Experience, 2014.
  • Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future. [DOI] J.E. Huggins C. Guger, B. Allison, C. Anderson, A. Batista, A-M. Brouwer, C. Brunner, R. Chavarriaga, M. Fried-Oken, A. Gunduz, D. Gupta, A. Kübler, R. Leeb, F. Lotte, L.E. Miller, G. Müller-Putz, T. Rutkowski, M. Tangermann, and D.E. Thompson. Brain-Computer Interface Journal, 1(1):27-49, 2014.
  • Context-Aware Energy Enhancements for Smart Mobile Devices. [DOI] B. Donohoo, C. Ohlsen, S. Pasricha, Y. Xiang, and C. Anderson. IEEE Transactions on Mobile Computing, 13(8):1720-1732, August 2014.
  • EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces. [DOI] R. Ashari and C. Anderson. In Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Brain-Computer Interfaces (CIBCI), pp. 57-63, 2014.
  • Convergent Reinforcement Learning Control with Neural Networks and Continuous Action Search. [DOI] M. Lee and C. Anderson. In Proceedings of 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), pp. 1-8, 2014.
  • Using Supervised Training Signals of Observable State Dynamics to Speed-up and Improve Reinforcement Learning. [DOI] D. Elliott and C. Anderson. In 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), pp. 1-8, 2014.
  • Brain Computer Interface Classifier Parameters Are Influenced by Practice: Results from a P300 Speller. K. The, B. Taylor, J. Crasta, M.-H. Lin, E. Forney, C. Anderson, P. Davies, and W. Gavin. In Proceedings of the Society of Psychophsiological Research (SPR) 2014 Meeting, 2014.


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2013

  • A Bioinformatics Method for Identifying Q/N-Rich Prion-Like Domains in Proteins. [DOI] E. Ross, K. MacLea, C. Anderson, and A. Ben-Hur. In Tandem Repeats in Genes, Proteins, and Disease: Methods and Protocols, Methods in Molecular Biology, ed. by D. Hatters, and A. Hannan, vol. 1017, Chapter 16, pp. 219-228, Humana Press, 2013.
  • Stable Adaptive Neural Control of Partially Observable Dynamic Systems. [DOI] J. Knight and C. Anderson. In Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, ed. by F. Lewis and D. Liu, John Wiley and Sons, Inc., Chapter 2, pp. 31-51, 2013.
  • A Comparison of EEG Systems for Use with Brain-Computer Interfaces in Home Environments. C. Anderson, W. Gavin, E. Forney, B. Taylor, and P. Davies. In Proceedings of the Society of Psychophsiological Research (SPR) 2013 Meeting, presented at the Symposium on Translational Research on Brain Computer Interfaces (BCI): From the Lab to the Home at in Florence, Italy, October 2-6, 2013.
  • A Stimulus-Free Brain-Computer Interface Using Mental Tasks and Echo State Networks. [DOI] E. Forney, C. Anderson, W. Gavin, and P. Davies. In Proceedings of the Fifth International Brain-Computer Interface Meeting: Defining the Future, Graz University of Technology Publishing House, June 3-7, 2013. Best Overall Poster Award
  • The N100 of Averaged ERPs Predicts LDA Classifier Success on an Individual Basis. [DOI] B. Taylor, E. Forney, W. Gavin, C. Anderson, and P. Davies. In Proceedings of the Fifth International Brain-Computer Interface Meeting: Defining the Future, Graz University of Technology Publishing House, June 3-7, 2013.
  • A Comparison of EEG Systems for Use in P300 Spellers by Users With Motor Impairments in Real-World Environments. [DOI] E. Forney, C. Anderson, P. Davies, W. Gavin, B. Taylor, and M. Roll. In Proceedings of the Fifth International Brain-Computer Interface Meeting: Defining the Future, SGraz University of Technology Publishing House, June 3-7, 2013.
  • Cloud-Based Analysis of EEG Signals for BCI Applications. [DOI] K. Ericson, S. Pallickara, and C. Anderson. In Proceedings of the Fifth International Brain-Computer Interface Meeting: Defining the Future, Graz University of Technology Publishing House, June 3-7, 2013.


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2012

  • Using Steady State Predictions to Improve the Transient Response of a Water to Air Heat Exchanger. [Link] D. Hodgson, P. Young, C. Anderson, D. Hittle, W. Duff, and D. Olsen. ASHRAE Transactions, vol. 118, July 1. 2012.
  • EEG Character Identification Using Stimulus Sequences Designed to Maximize Minimal Hamming Distance. [DOI] T. Fukami, T. Shimada, E. Forney, and C. Anderson. In Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), San Diego, California USA, pp. 1782-1785, 2012.
  • Smoothly Combining Steady State Predictions with PI Control. [DOI] D. Hodgson, P. Young, C. Anderson, W. Duff, D. Hittle, and D. Olsen. In Proceedings of the ASME Dynamic Systems and Control Division Conference, Fort Lauderdale, FL. ASME, Oct 17-19, 2012.
  • Exploiting Spatiotemporal and Device Contexts for Energy-Efficient Mobile Embedded Systems. [DOI] B. Donohoo, C. Ohlsen, S. Pasricha, and C. Anderson. In Proceedings of the 49th Annual Design Automation Conference (DAC ’12), ACM, New York, NY, pp. 1278-1283, 2012. [PDF]


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2011

  • Critical Issues in State-of-the-Art Brain-Computer Interface Signal Processing. [DOI] D. Krusienski, M. Grosse-Wentrup, F. Galan, D. Coyle, K. Miller, E. Forney, and C. Anderson. Journal of Neural Engineering, 8(2), 2011.
  • Reliable Identification of Mental Tasks Using Time-Embedded EEG and Sequential Evidence Accumulation. [DOI] C. Anderson, E. Forney, D. Haines, M. Natarajan. Journal of Neural Engineering, 8(2), 2011.
  • Stable Reinforcement Learning with Recurrent Neural Networks. [DOI] J. Knight and C. Anderson. Journal of Control Theory and Applications, 9(3):410-420, 2011.
  • Discount and speed/execution tradeoffs in MDP Games. [DOI] R. Uribe, F. Lozano, K. Shibata, and C. Anderson. In Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games, pp. 79-86, Seoul, South Korea, Aug. 31-Sept. 3, 2011.
  • Comparison of EEG Blind Source Separation Techniques to Improve the Classification of P300 Trials. [DOI] Z. Cashero and C. Anderson. In Proceedings of 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC ’11), Boston, MA, pp. 7183-7186, 2011.
  • Classification of EEG During Imagined Mental Tasks by Forecasting with Elman Recurrent Neural Networks. [DOI] E. Forney and C. Anderson. In Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2749-2755, July 31-Aug. 5, 2011.
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    2010

  • Neural Network Approach to Stream-Aquifer Modeling for Improved River Basin Management. [DOI] E.Triana, J. Labadie, T. Gates, and C. Anderson. Journal of Hydrology, 391(3-4):235-247, 2010.

  • Dissociating the Contributions of Independent Corticostriatal Systems to Visual Categorization Learning Through the Use of Reinforcement Learning Modeling and Granger Causality Modeling. [DOI] C. Seger, E. Peterson, C. Cincotta, D. Lopez-Paniaqua, and C. Anderson. NeuroImage, 50:644-656. Editors Choice Award, Systems Neuroscience Section, 2010.

  • Analyzing Electroencephalograms Using Cloud Computing Techniques. [PDF] [DOI] S. Pallickara, K. Ericson, and C. Anderson. In Proceedings of the IEEE Second International Conference on Cloud Computing Technology and Science, Indianapolis, Nov. 30-Dec. 3, 2010. Winner of Best Student Paper Award.

  • Handwriting Recognition Using a Cloud Runtime. [PDF] K. Ericson, S. Pallickara, and C. Anderson. In Proceedings of the Colorado Celebration of Women in Computing, 2010. One of 8 showcased presentations at the conference.
  • A Comparison of Elman and Echo State Networks. [PDF] E. Forney and C. Anderson. In Proceedings of the Colorado Celebration of Women in Computing, 2010.
  • Automatic Creation of Tile Size Selection Models. [DOI] T. Yuki, L. Renganarayanan, S. Rajopadhye, C. Anderson, A. Eichenberger, and K. O’Brien. In Proceedings of the International Symposium on Code Generation and Optimization (CGO), Toronto, Canada, April, 2010.
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    2009

  • Peer commentary for Freeing the Mind: Brain Communication that Bypasses the Body. C. Anderson. In Pioneering Studies in Cognitive Neuroscience, edited by R. Roche and S. Commins. McGraw-Hill, Open University Press, pp. 76-77, 2009.

  • Nonlinear Dimensionality Reduction of Electroencephalogram (EEG) for Brain Computer Interfaces. [PDF] [DOI] M. Teli and C. Anderson. In Proceedings of the 31st Annual International IEEE EMBS Conference, vol. 1, pp. 2486-2489, Minneapolis, MN, 2009.
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    2008

  • Restricted Gradient-Descent Algorithm for Value-Function Approximation in Reinforcement Learning. [DOI] A. Barreto and C. Anderson. Artificial Intelligence, 172(4-5):454-482, 2008.

  • MIMO Robust Control for HVAC Systems. [DOI] M. Anderson, M. Buehner, P. Young, D. Hittle, C. Anderson, J. Tu, and D. Hodgson. IEEE Transactions on Control Systems Technology, 16(3):475-483, 2008.

  • Translating Thoughts Into Actions by Finding Patterns in Brainwaves. [PDF] C. Anderson and J. Bratman. In Proceedings of The Fourteenth Yale Workshop on Adaptive and Learning Syste ms, Yale University, June 2-4, 2008.
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    2007

  • Robust Reinforcement Learning Control using Integral Quadratic Constraints for Recurrent Neural Networks. [DOI] C. Anderson, P. Young, M. Buehner, K. Bush, and D. Hittle. IEEE Transactions on Neural Networks, 18(4):993-1002, July, 2007.

  • An Experimental System for Advanced Heating, Ventilating, and Air Conditioning (HVAC) Control. [DOI] M. Anderson, P. Young, D. Hittle, C. Anderson, J. Tu, and D. Hodgson. Energy and Buildings, 39(2):113-119, February 2, 2007

  • Classification of Time-Embedded EEG Using Short-Time Principal Component Analysis. [Link] C. Anderson, M. Kirby, D. Hundley, and J. Knight. In Toward Brain-Computer Interfacing, edited by G. Dornhege, J. del R. Millan, T. Hinterberger, D. McFarland, and K.-R. Müller, pp. 261-278, The MIT Press, 2007.

  • Improving Performance using Robust Recurrent Reinforcement Learning Control. M. Buehner, C. Anderson, P. Young, K. Bush, and D. Hittle. In Proceedings of the European Control Conference 2007, Kos, Greece, pp. 1676-1681, July 2-5, 2007.
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    2006


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  • BCI Meeting 2005—Workshop on BCI Signal Processing: Feature Extraction and Translation. D. McFarland, C. Anderson, K.-R. Müller, A. Schl\”{o}gl, and D. Krusienski. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2):135-138, June 2006.

  • Geometric Subspace Methods and Time-Delay Embedding for EEG Artifact Removal and Classification. [DOI] C. Anderson, J. Knight, T. O’Connor, M. Kirby,, and A. Sokolov. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2):142-146, June 2006.

  • Robust and Interpretable Statistical Models for Predicting the Intensification of Tropical Cyclones. [PDF] K. Chatzidimitriou, C. Anderson, and M. DeMaria. In Proceedings of the AMS 27th Conference on Hurricanes and Tropical Meteorology , Monterey, California, April 24-28, 2006.
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    2005

  • Optimizing Conductance Parameters of Cortical Neural Models via Electrotonic Partitions. [DOI] K. Bush, J. Knight, C. Anderson. Neural Networks, 18(5-6):488-496, 2005.

  • Feature selection and blind source separation in an EEG-based brain-computer interface. [DOI] D. Peterson, J. Knight, M. Kirby, C. Anderson, M. Thaut. EURASIP Journal on Applied Signal Processing, 2005(19):3128-3140, 2005.

  • Modeling Reward Functions for Incomplete State Representations via Echo State Networks. [DOI] K. Bush and C. Anderson. In Proceedings of the International Joint Conference on Neural Networks, Montreal, Quebec, vol. 5, pp. 2995-3000, July 2005.

  • Optimizing Neural Model Templates using Covariance Matrix Adaptation and Fourier Analysis. [DOI] K. Bush, J. Knight, and C. Anderson. In Proceedings of the International Joint Conference on Neural Networks, Montreal, Quebec, vol. 5, pp. 2162-2166, July 2005.
  • An Inexpensive Brain-Computer Interface Based on Spatial and Temporal Analysis of EEG. [PDF]) C. Anderson, J. Knight, and M. Kirby. In Proceedings of HCI International, (HCI-I), Las Vegas, NV, (CD-ROM), 2005.
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    2004

  • Exact Solution of the Governing PDE of a Hot Water to Air Finned Tub Cross Flow Heat Exchanger. [PDF] C. Delnero, D. Dreisigmeyer, D. Hittle, P. Young, C. Anderson, and M. Anderson. International Journal of Heating, Ventilating, Air-Conditioning and Refrigerating Research, 10(1), 2004.

  • Robust Reinforcement Learning Using Integral-Quadratic Constraints. [DOI] C. Anderson, R. Kretchmar, P. Young, and D. Hittle. In Learning and Approximate Dynamic Programming, edited by J. Si, A. Barto, and P. Werbos, IEEE Press, Chapter 13, pp. 337-358, 2004.

  • Robust Reinforcement Learning for Heating, Ventilation, and Air Conditioning Control of Buildings. [DOI] C. Anderson, D. Hittle, R. Kretchmar, and P. Young. In Learning and Approximate Dynamic Programming, edited by J. Si, A. Barto, and P. Werbos, IEEE Press, Chapter 20, pp. 517-534, 2004.

  • A New Product for Estimating the Probability of Tropical Cyclone Formation. [PDF] M. DeMaria, C. Anderson, J. Knaff, and B. Connell. In Preprints, American Meteorological Society 26th Conference on Hurricanes and Tropical Meteorology, Miami, FL, pp. 52-53, May 2004.
  • Feature Selection as a Precursor to Modeling in High-Dimensional Scientific Discovery. D. Peterson, C. Anderson, M. Kirby, and M. Thaut. In Abstracts of Papers Presented to the American Mathematical Society, 25(1), pp. 163, 2004.
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2003

  • Comparison of Linear and Nonlinear Methods for EEG Signal Classification. D. Garrett, D. Peterson, C. Anderson, and M. Thaut. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2):141-144, 2003.
  • Linear and Non-linear Methods in Brain-Computer Interfaces. [PDF] [DOI] K.-R. Müller, C. Anderson, and G. Birch. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2):162-165, 2003.
  • Geometric Analysis for the Characterization of Nonstationary Time-Series. M. Kirby and C. Anderson. In Springer Applied Mathematical Sciences Series Celebratory Volume for the Occasion of the 70th Birthday of Larry Sirovich, edited by E. Kaplan, J. Marsden, and K.R. Sreenivasan, Springer-Verlag, Chapter 8, pp. 263-292, 2003.
  • EEG Subspace Representations and Feature Selection for Brain-Computer Interfaces. C. Anderson and M. Kirby. In Proceedings of the 1st IEEE Workshop on Computer Vision and Pattern Recognition for Human Computer Interaction (CVPRHCI), Madison, Wisconsin. (CD-ROM), June 17, 2003.
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2002

  • Behavioral Model Verification. T. Chen, A. Bai, A. Hajjar, A. Andrews, and C. Anderson. Journal of Electronic Testing: Theory and Applications (JETTA), 18(6):583-594, Dec. 2002.
  • Custom Frequency Band Features Improve Single Trial EEG Classification in Early Finger Movement Precision. D. Peterson, C. Anderson, and M. Thaut. In Proceedings of the Society of Neuroscience Meeting, Poster 506.11, 2002.
  • MIMO Robust Control for Heating, Ventilating and Air Conditioning (HVAC) Systems. M. Anderson, P. Young, D. Hittle, C. Anderson, J. Tu, and D. Hodgson. In Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, Dec. 10-13, pp. 167-172, 2002.
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2001

  • Robust Reinforcement Learning Control with Static and Dynamic Stability. R. Kretchmar, P. Young, C. Anderson, D. Hittle, M. Anderson, C. Delnero, and J. Tu. International Journal of Robust and Nonlinear Control, 11:1469-1500, 2001.
  • Recent Advances in EEG Signal Analysis and Classification. C. Anderson and D. Peterson. In Clinical Applications of Artificial Neural Networks, edited by R. Dybowski and V. Gant, Cambridge University Press, UK, Chapter 8, pp. 175-191, 2001.
  • Robust Reinforcement Learning Control. R. Kretchmar, P. Young, C. Anderson, D. Hittle, M. Anderson, J. Tu, and C. Delnero. In Proceedings of the American Control Conference, Arlington, VA, pp. 902-907, June 2001.
  • Neural Networks and PI Control using Steady State Prediction Applied to a Heating Coil. C. Delnero, D. Hittle, C. Anderson, P. Young, and M. Anderson. In Proceedings of CLIMA 2000, World Conference on Indoor Climate and Comfort Science, Naples, Italy, pp. 58-71, Sept. 2001.
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2000

  • Learning Expert Delineations in Biomedical Image Segmentation. S. Crawford-Hines and C. Anderson. In Proceedings of the Conference on Artificial Neural Networks In Engineering, ANNIE-2000, St. Louis, Missouri, pp. 657-662, Nov. 5-8, 2000.
  • Behavioral Cloning of Student Pilots with Modular Neural Networks. C. Anderson, B. Draper, and D. Peterson. In Proceedings of the Seventeenth International Conference on Machine Learning, edited by P. Langley, Stanford University, pp. 25-32, June, 2000.
  • On Choosing Test Criteria for Behavioral Hardware Design Verification. A. von Mayrhauser, T. Chen, J. Kok, C. Anderson, A. Read, and A. Hajjar. In IEEE International High Level Design Validation and Test Workshop (HLDVT’00), San Francisco, CA, pp. 124-132, 2000.
  • Achieving the Quality of Verification for Behavioral Models With Minmum Effort. T. Chen, A.Hajjar, A. von Mayrhauser, M. Sahinoglu, and C. Anderson. In Proceedings of the International Symposium on Quality in Electronic Design, San Jose, CA, pp\ 234, March, 2000.
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1999

  • Fast Generation of NURBS Surfaces from Polygonal Mesh Models Using Artificial Neural Networks. C. Anderson. In Proceedings of the Applications Symposium of the 12th Australian Joint Conference on Artificial Intelligence, AI’99, Coogee, Australia, pp. 38-41, 1999.
  • How Much Testing is Enough? Applying Stopping Rules to Behavioral Model Testing. T. Chen, M. Sahinoglu, A. von Mayrhauser, A. Hajjar, and C. Anderson. In Proceedings of the High Assurance Systems Engineering Symposium, pp. 249-256, November, 1999.
  • On the Efficiency of a Compound Poisson Stopping Rule for Mixed Strategy Testing. M. Sahinoglu, A. von Mayrhauser, A. Hajjar, T. Chen, and C. Anderson. In Proceedings of the IEEE Aerospace Conference, Track 7, vol. 5, pp. 93-98, March 1999.
  • Identifying Mental Tasks From Spontaneous EEG: Signal Representation and Spatial Analysis. C. Anderson. In Proceedings of Engineering Applications of Bio-Inspired Artificial Neural Networks: International Work-Conference on Artificial and Natural Neural Networks, IWANN’99, Alicante, Spain, vol. II, Springer-Verlag: Lecture Notes in Computer Science, edited by J. Mira and J. Sanches-Andres, pp. 228-237, June 1999.
  • EEG-Based Cognitive Task Classification with ICA and Neural Networks. D. Peterson and C. Anderson. In Proceedings of Engineering Applications of Bio-Inspired Artificial Neural Networks: International Work-Conference on Artificial and Natural Neural Networks, IWANN’99, Alicante, Spain, vol. II, Springer-Verlag: Lecture Notes in Computer Science, edited by J. Mira and J. Sanches-Andres, pp. 265-272, June 1999.
  • Using Temporal Neighborhoods to Adapt Function Approximators in Reinforcement Learning. R. Kretchmar and C. Anderson. In Foundations and Tools for Neural Modeling: International Work-Conference on Artificial and Natural Neural Networks, IWANN’99, Alicante, Spain, vol. I, Springer-Verlag: Lecture Notes in Computer Science, edited by J. Mira and J. Sanches-Andres, pp. 488-496, June 1999.
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1998

  • Multivariate Autoregressive Models for Classification of Spontaneous Electroencephalogram During Mental Tasks. C. Anderson, E. Stolz, S. Shamsunder. IEEE Transactions on Biomedical Engineering, 45(3):277-286, 1998.
  • On the Promise of Neural Networks to Support Software Testing. A. von Mayrhauser, C. Anderson, T. Chen, R. Mraz, and C. Gideon. In Computational Intelligence in Software Engineering, edited by W. Pedrycz and J.F. Peters, World Scientific, pp. 3-32, 1998.
  • Selected bibliography on connectionism. O. Selfridge, R. Sutton, and C. Anderson. In Evolution, Learning, and Cognition, edited by Y.C. Lee, World Scientific Publishing, pp. 391-404, 1998.
  • Fast Antirandom (FAR) Test Generation. A. Bai, T. Chen, A. Hajjar, A. von Mayrhauser, and C. Anderson. In Proceedings of the 3rd IEEE International High Assurance Systems Engineering Symposium, Washington, D.C, pp. 262-269, Nov. 1998.
  • Fast Antirandom (FAR) Test Generation to Improve Code Coverage. A. von Mayrhauser, A. Bai, T. Chen, A. Hajjar, and C. Anderson. In Proceedings of the 11th International Software Quality Week, San Francisco, California, May, 1998.
  • Machine Learned Contours to Assist Boundary Tracing. S. Crawford-Hines and C. Anderson. In Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, Tucson, AZ, pp. 229-231, April, 1998.
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1997

  • Synthesis of Reinforcement Learning, Neural Networks, and PI Control Applied to a Simulated Heating Coil. C. Anderson, D. Hittle, A. Katz, R. Kretchmar. Journal of Artificial Intelligence in Engineering, 11(4):423-431, 1997.
  • Effects of Variations in Neural Network Topology and Output Averaging on the Discrimination of Mental Tasks from Spontaneous Electroencephalogram. C. Anderson. Journal of Intelligent Systems, 7(1-2):165-190, 1997.
  • Neural Nets in Boundary Tracing Tasks. S. Crawford-Hines and C. Anderson. In Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Proce ssing, VII, edited by J. Principe, L. Giles, N. Morgan, and E. Wilson, pp. 207-215, 1997.
  • Efficient Indexing for Object Recognition Using Large Neural Networks. M. Stevens, C. Anderson, and J. Beveridge. In Proceedings of the International Conference on Neural Networks, ICNN’97, Houston, TX, vol. 3, pp. 1454-1458, June, 1997.
  • Comparison of CMACs and Radial Basis Functions for Local Function Approximators in Reinforcement Learning. R. Kretchmar and C. Anderson. In Proceedings of the International Conference on Neural Networks, ICNN’97, Houston, TX, vol. 2, pp. 834-837, June, 1997.
  • Test Coverage Prediction of VHDL Models using Neural Networks. C. Anderson, A. von Mayrhauser, C. Gideon, T. Chen, and J. Kok. In Proceedings of the Annual Oregon Workshop on Software Metrics, Coeur d’Alene, Idaho, May, 1997.
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1996

  • EEG Signal Compression With ADPCM Subband Coding. Z. Sijercic, G. Agarwal, and C. Anderson. In Proceedings of the 39th Midwest Symposium on Circuits and Systems, pp. 695-698, August, 1996.
  • Reinforcement Learning, Neural Networks and PI Control Applied to a Heating Coil. C. Anderson, D. Hittle, A. Katz, and R. Kretchmar. In Solving Engineering Problems with Neural Networks: Proceedings of the International Conference on Engineering Applications of Neural Networks (EANN’96), edited by A. Bulsari, S. Kallio, and D. Tsaptsinos, Systems Engineering Association, PL 34, FIN-20111 Turku 11, Finland, pp. 135-142, 1996.
  • Classification of EEG Signals from Four Subjects During Five Mental Tasks. [PDF] C. Anderson and Z. Sijercic. In Solving Engineering Problems with Neural Networks: Proceedings of the International Conference on Engineering Applications of Neural Networks (EANN’96), edited by A. Bulsari, S. Kallio, and D. Tsaptsinos, Systems Engineering Association, PL 34, FIN-20111 Turku 11, Finland, pp. 407-414, 1996.
  • Assessing Neural Networks as Guides for Testing Activities. C. Anderson, A. von Mayrhauser, and T.Chen. In Proceedings of the 3rd International Software Metrics Symposium, Berlin, pp. 155-165, 1996.
  • Neural Networks for Predicting Chilled Water Demand in Buildings. D. Hittle, P. Flocken, and C. Anderson. In Proceedings of the ASME International Solar Energy Conference, San Antonio, TX, pp. 387-394, March/April, 1996.
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1995

  • Determining Mental State from EEG Signals Using Neural Networks. C. Anderson, S. Devulapalli, and E. Stolz. Scientific Programming, Special Issue on Applications Analysis, 4(3):171-183, 1995.
  • EEG Signal Classification with Different Signal Representations. C. Anderson, S. Devulapalli, and E. Stolz. In Neural Networks for Signal Processing V, edited by F. Girosi, J. Makhoul, E. Manolakos, and E. Wilson, IEEE Service Center, Piscataway, NJ, pp. 475-483, 1995.
  • On the Use of Neural Networks to Guide Software Testing Activities. C. Anderson, A. von Mayrhauser, and R. Mraz. In Proceedings of ITC’95, the International Test Conference, Washington, D.C, pp. 720-729, October 21-26, 1995.
  • Using a Neural Network to Predict Test Case Effectiveness. A. von Mayrhauser, C. Anderson, and R. Mraz. In Proceedings of the 1995 IEEE Aerospace Applications Conference, pp. 77-91, Feb. 1995.
  • Discriminating Mental Tasks Using EEG Represented by AR Models. C. Anderson, E. Stolz, and S. Shamsunder. In Proceedings of the 1995 IEEE Engineering in Medicine and Biology Annual Conference, Montreal, Canada, CD-ROM, Sept 20-23, 1995.
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1994

  • EEG as a Means of Communication: Preliminary Experiments in EEG Analysis Using Neural Networks. C. Anderson, S. Devulapalli, and E. Stolz. In Proceedings of the First International ACM/SIGCAPH Conference on Assistive Technologies (ASSETS’94), pp. 141-147, 1994. [DOI]
  • Reinforcement Learning with Modular Neural Networks for Control. C. Anderson and Z. Hong, Z. In Proceedings of the IEEE International Workshop on Neural Networks Applied to Control and Image Processing (NNACIP’94), pp. 90-93, 1994. [PDF]
  • Interactive Region Bounding with Neural Networks. S. Crawford-Hines and C. Anderson. In Proceedings of the IEEE International Workshop on Neural Networks Applied to Control and Image Processing (NNACIP’94), pp. 58-61, 1994.
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1993

  • Genetic reinforcement learning for neurocontrol problems. [PDF] [DOI] D. Whitley, S. Dominic, R. Das, and C. Anderson. Machine Learning, 13:259-284, 1993.
  • Q-Learning with Hidden-Unit Restarting. [PDF] C. Anderson. In Advances in Neural Information Processing Systems, vol. 5, edited by S. Hanson, J. Cowan, and C. Giles, Morgan Kaufmann Publishers, San Mateo, CA, pp. 81-88, 1993.
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1992

  • Estimating ignition timing from engine cylinder pressure with neural networks. [PDF] [DOI] B. Willson, J. Whitham, and C. Anderson. In Proceedings of Intelligent Vehicles 92, Detroit, MI, pp. 108-113, July, 1992.
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1991

  • Genetic reinforcement learning for neural networks. S. Dominic, R. Das, D. Whitley, and C. Anderson. In Proceedings of the International Joint Conference on Neural Networks, Seattle, vol. II, pp. 71-76, July 8-12, 1991. [DOI]
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1990

  • A set of challenging control problems. C. Anderson, and W.T. Miller. In Neural Networks for Control, edited by W.T. Miller, R. Sutton, and P. Werbos, MIT Press, pp. 475-510, 1990.
  • Learning a nonlinear model of a manufacturing process using multilayer connectionist networks. C. Anderson, J. Franklin, and R. Sutton. In Proceedings of the 5th IEEE International Symposium on Intelligent Control, Philadelphia, PA, pp. 404-409, Sept. 1990. [PDF] [DOI]
  • Knowledge representation for learning control. M. Kokar, C. Anderson, T. Dean, K. Valavanis, and W. Zadrony. In Proceedings of the 5th IEEE International Symposium on Intelligent Control, Philadelphia, PA, pp. 389-399, Sept. 1990. [DOI]
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1989

  • Learning to control an inverted pendulum with neural networks. C. Anderson. IEEE Control Systems Magazine, 9(3):31-36, April, 1989. [DOI] [PDF]
  • Tower of hanoi with connectionist networks: learning new features. C. Anderson. In Proceedings of the Sixth International Workshop on Machine Learning, Cornell University, pp. 345-349, June, 1989. [PDF]
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1988

  • Learning to control an inverted pendulum with connectionist networks. C. Anderson. In Proceedings of the 1988 American Control Conference, Atlanta, GA, pp. 2294-2298, 1988.
  • Application of connectionist learning methods to manufacturing process monitoring. J. Franklin, R. Sutton, and C. Anderson. In Proceedings of the Third IEEE International Symposium on Intelligent Control, Arlington, VA, pp. 709-712, 1988. [PDF] [DOI]
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1987

  • Strategy learning with multilayer connectionist representations. C. Anderson. In Proceedings of the Fourth International Workshop on Machine Learning, Irvine, CA, pp. 103-114, 1987. [PDF] [C]
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1986

  • Learning and problem solving with connectionist representations. C. Anderson. Ph.D. Dissertation, Computer and Information Science Department, University of Massachusetts, Amherst, MA, 1986. [PDF]
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1985

  • Cooperativity in networks of pattern recognizing stochastic learning automata. A. Barto, P. Anandan, and C. Anderson. In Proceedings of the Fourth Yale Workshop on Applications of Adaptive Systems Theory, New Haven, CT, pp. 85-90, 1985. [DOI]
  • Structural learning in connectionist systems. A. Barto and C. Anderson. In Proceedings of the Seventh Annual Conference of the Cognitive Science Society, Irvine, CA, 1985.
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1983

  • Neuron-like adaptive elements that can solve difficult learning control problems. A. Barto, R. Sutton, and C. Anderson. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13(5):834-846, 1983. (Reprinted in Neurocomputing: Foundations of Research, ed. by J. Anderson and E. Rosenfeld, Cambridge, MA: The MIT Press, 1988, pp. 537-549.) [PDF] [DOI]
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1982

  • Feature generation and selection by a layered network of reinforcement learning elements: Some initial experiments. C. Anderson. M.S. Thesis, Computer and Information Science Department, Technical Report 82-12, University of Massachusetts, Amherst, MA, 1982. [PDF]
  • Synthesis of nonlinear control surfaces by a layered associative network. A. Barto, C. Anderson, and R. Sutton. Biological Cybernetics, 43:175-185, 1982. [PDF] [DOI]
  • Spatial learning simulation systems. A. Barto, R. Sutton, and C. Anderson. In Proceedings of the 10th IMACS World Congress on Systems Simulation and Scientific Computation, pp. 204-206, 1982.
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1979

  • HOUSE, an energy utilization management tool. G. Bodman, T. Thompson, C. Anderson, and A. Hutchins. In Proceedings of the Joint Meeting of the American Society of Agricultural Engineers and the Canadian Society of Agricultural Engineering, 1979.
  • Clustering methods and their application to multispectral satellite data. C. Anderson. B.S. Thesis, Department of Computer Science, University of Nebraska, Lincoln, NE, 1979.
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Unpublished Reports

  • Echo State Networks for Modeling and Classification of EEG Signals in Mental-Task Brain Computer Interfaces. E. Forney, C. Anderson, W. Gavin, P. Davies, M. Roll, B. Taylor. Technical Report CS-15-102, Department of Computer Science, Colorado State University, Fort Collins, CO, 2015. [PDF]
  • Introduction to Computational Neural Modeling for Computer Scientists and Mathematicians. K. Bush and C. Anderson. Technical Report CS-04-01, Department of Computer Science, Colorado State University, Fort Collins, CO, 2004. [PDF]
  • Approximating a Policy Can be Easier Than Approximating a Value Function. C. Anderson. Technical Report CS-00-101, Department of Computer Science, Colorado State University, Fort Collins, CO, 2000. [PDF]
  • Fast Generation of NURBS Surfaces from Polygonal Mesh Models of Human Anatomy. C. Anderson and S. Crawford-Hines. Technical Report CS-99-101, Department of Computer Science, Colorado State University, Fort Collins, CO, 1999. [PDF]
  • A Multigrid Form of Value Iteration Applied to a Markov Decision Problem. R. Heckendorn and C. Anderson. Technical Report CS-98-113, Department of Computer Science, Colorado State University, Fort Collins, CO, 1989. [PDF]
  • Multigrid Q-Learning. C. Anderson and S. Crawford-Hines. Technical Report CS-94-121, Department of Computer Science, Colorado State University, Fort Collins, CO, 1994. [PDF]
  • Controlling a Dynamic System in Real Time. E. Furrow and C. Anderson. Technical Report CS-94-119, Department of Computer Science, Colorado State University, Fort Collins, CO, 1994. [PDF]
  • Classification of EEG Signals Using a Sparse Polynomial Builder. E. Orosz and C. Anderson. Technical Report CS-94-111, Department of Computer Science, Colorado State University, Fort Collins, CO, 1994. [PDF]
  • Project Report for Robotics Seminar. C. Anderson and F. Cervantes. Report for graduate course at University of Massachusetts, Amherst, 1982. Shows implementation of Associative Search Network (early reinforcement learning algorithm) in the Forth programming language, applied to an IBM cartesian robot. [PDF]
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Students’ Dissertations and Theses

  • Sparse Bayesian Reinforcement Learning. Minwoo Lee. Ph.D. Dissertation, Department of Computer Science, Colorado State University, 2017. [PDF]
  • EEG Subspace Analysis and Classification Using Principal Angles For Brain-Computer Interfaces. Rehab Bahaaddin Ashari. Ph.D. Dissertation, Department of Computer Science, Colorado State University, 2015. [PDF]
  • Sentiment Analysis in the Arabic Language Using Machine Learning. Saud Saleh Alotaibi. Ph.D. Dissertation, Department of Computer Science, Colorado State University, 2015. [PDF]
  • Automated Tropical Cyclone Eye Detection Using Discriminant Analysis. Robert DeMaria. M.S. Thesis, Department of Computer Science, Colorado State University, 2014. [PDF]
  • Generative Topographic Mapping of Electroencephalography (EEG) Data. Navini Dantanarayana. M.S. Thesis, Department of Computer Science, Colorado State University, 2014. [PDF]
  • Detecting Error Related Negativity using EEG Potentials Generated during Simulated Brain Computer Interaction. Prathamesh Verlekar. M.S. Thesis, Department of Computer Science, Colorado State University, 2014. [PDF]
  • P300 Classification Using Deep Belief Nets. Amin Sobhani. M.S. Thesis, Department of Computer Science, Colorado State University, 2014. [PDF]
  • Localized Anomaly Detection via Hierarchical Integrated Activity Discovery. Thiyagarajan Chockalingam. M.S. Thesis, Department of Electrical and Computer Engineering, Colorado State University, 2014. [PDF]
  • Single-Trial P300 Classification Using PCA with LDA and Neural Networks. Nand Sharma. M.S. Thesis, Department of Electrical and Computer Engineering, Colorado State University, 2014. [PDF]
  • Electroencephalogram Classification by Forecasting with Recurrent Neural Networks. Elliott M. Forney. M.S. Thesis, Department of Electrical and Computer Engineering, Colorado State University, 2011. [PDF]
  • Comparison of EEG Preprocessing Methods to Improve the Performance of the P300 Speller. Zachary Cashero. M.S. Thesis, Department of Computer Science, Colorado State University, 2011. [PDF]
  • Estimating Sparse Inverse Covariance Matrix for Brain Computer Interface Applications. Annamalai Natarajan. M.S. Thesis, Department of Computer Science, Colorado State University, 2009. [PDF]
  • An Echo State Model of Non-Markovian Reinforcement Learning. Keith Bush. Ph.D. Dissertation, Department of Computer Science, Colorado State University, 2008. [PDF]
  • Stability Analysis of Recurrent Neural Networks with Applications. James N. Knight. Ph.D. Dissertation, Department of Computer Science, Colorado State University, 2008. [PDF]
  • Plasticity in EEG Oscillations Associated with Auditory Verbal Learning. David A. Peterson. Ph.D. Dissertation, Department of Computer Science, Colorado State University, 2007. [PDF]
  • Analysis of Temporal Structure and Normality in EEG Data. Artem Sokolov. M.S. Thesis, Department of Computer Science, Colorado State University, 2007. [PDF]
  • Dimensionality Reduction and Classification of Time Embedded EEG Signals. Mohammad Nayeem Teli. M.S. Thesis, Department of Computer Science, Colorado State University, 2007. [PDF]
  • Signal Fraction Analysis and Artifact Removal in EEG. James N. Knight. M.S. Thesis, Department of Computer Science, Colorado State University, 2003. [PDF]
  • Machine Learned Boundary Definitions for an Expert’s Tracing Assistant in Image Processing. Stewart Crawford-Hines. Ph.D. Dissertation, Department of Computer Science, Colorado State University, 2003. [PDF]
  • Modeling Observed Developmental Changes Influencing Hippocampal CA1 and CA3 Epileptiform Burst Characteristics. Keith Bush. M.S. Thesis, Department of Computer Science, Colorado State University, 2003. [PDF]
  • A Synthesis of Reinforcement Learning and Robust Control Theory. R. Matthew Kretchmar. Ph.D. Dissertation, Department of Computer Science, Colorado State University, 2000. [PDF]
  • Vehicle Traffic Light Control Using SARSA. T. Thorpe. M.S. Thesis, Department of Computer Science, Colorado State University, 1997. [PDF]
  • A Physically-Realistic Simulation of Vehicle Traffic Flow. T. Thorpe. Technical Report 97-104, Department of Computer Science, Colorado State University, 1997. [PDF]
  • Analysis of LVQ in the Context of Spontaneous EEG Signal Classification. Daniel Ford. M.S. Thesis, Department of Computer Science, Colorado State University, 1996. [PDF]
  • Non-Linear Principal Component Analysis and Classification EEG During Mental Tasks. Saikumar Devulapalla. M.S. Thesis, Department of Computer Science, Colorado State University, 1996. [PDF]
  • Using Neural Networks for Approximate Radiosity Form Factor Computation. Charles Martin. M.S. Thesis, Department of Computer Science, Colorado State University, 1996. [PDF]
  • Generation of Terrain Textures Using Neural Networks. Saniago Alvarez. M.S. Thesis, Department of Computer Science, Colorado State University, 1995. [PDF]
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