SYSC 576 / EE 456/556: Neural Networks II: Spring 2011
Course Syllabus (pdf)
Assignments
- Reading Assignment for Week 3
- Problem Assignment 1: Fuzzy logic
- Problem Assignment 2: Build NN model via given data set
Readings
(Alphabetical by first author)
- Barto, A.G. (1991), "Connectionist Learning for Control: An Overview" (Chapter 1) and Werbos, P.J., "Overview of Designs and Capabilities" (Chapter 2) from Neural Networks for Control, Miller, Sutton & Werbos, Eds, MIT Press, pp.5-66.
- Cotter, N.E., and Conwell, P. (1991), “Learning Algorithms and Fixed Dynamics,” IEEE International Joint Conference on Neural Networks, Seattle 1991, vol. I, 799-804.
- Jaeger, H. (2002): Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the "echo state network" approach. GMD Report 159, German National Research Center for Information Technology (revised 2005).
- Lendaris, G.G. (2009), “A Retrospective on Adaptive Dynamic Programming for Control”, IJCNN'2009 (shorter w/similar content 2008 IEEE paper).
- Lendaris, G.G. (1988), "Neural Networks, Potential Assistants To Knowledge Engineers," HEURISTICS, Journal of the International Association of Knowledge Engineers, Kensington, MD 20895, Vol. 1, No. 2, pp. 7 - 18, December. [Also, presented as Invited Paper at Third International Symposium on Knowledge Engineering, Oct 17-29, Madrid, Spain.]
- Lendaris, G.G., I. Harb (1990), "Improved Generalization in ANNs via Use of Conceptual Graphs: A Character Recognition Task as an Example Case," Proceedings of International Joint Conference on Neural Networks'R90 (IJCNN'90), San Diego, IEEE, July.
- Lendaris, G.G. (1992), "A Neural-Network Approach to Implementing Conceptual Graphs," (invited) Chapter 8 in Conceptual Structures, Nagle, et al, Editors, Ellis Horwood.
- Lendaris, G.G, K. Mathia, R. Saeks (1999), "Linear Hopfield Networks and Constrained Optimization," Transactions of Systems, Man Cybernetics, IEEE, Vol. 29, No. 1, pp. 114 - 118, February.
- Lendaris, G., Mathia, K. (1996), "Efficient Numerical Inversion Using Multilayer Feedforward Neural Networks," World Congress on Neural Networks 1996, San Diego, California.
- Lendaris, G.G., Shannon, T.T. (1998), "Designing (Approximate) Optimal Controllers via DHP Adaptive Critics & Neural Networks", Draft of Invited Chapter for The Handbook of Applied Computational Intelligence, CRC Press, Padget, Karayianis, & Zadeh, Eds. [book not published]
- Lendaris, G.G., Neidhoefer, J.S. (2004), "Guidance in the Use of Adaptive Critics for Control" Ch.4 in Handbook of Learning and Approximate Dynamic Programming, Si, et al, Eds., IEEE Press & Wiley Interscience, pp. 97-124, 2004.
- Lewis, F.L. (2006), "Neural Networks in Feedback Control Systems" in Mechanical Engineer’s Handbook, John Wiley, New York.
- Kong, S.-G. and B. Kosko (1990) , "Comparison of fuzzy and neural truck backer-upper control systems ," 1990 IJCNN International Joint Conference on Neural Networks,, vol.3, 17-21, pp.349-358.
- Nguyen, D. and B. Widrow (1990). “Neural Networks for Self-Learning Control Systems,” IEEE Control Systems Magazine, 10(3):18-23, April 1990.
- Nguyen, D. and B. Widrow (1990). “The Truck Backer-Upper: An example of Self-Learning in Neural Networks,” Chapter 12 in Neural Networks for Control , Miller, W.T., R. Sutton, P. Werbos (eds.), pp 287-299, MIT Press, Cambridge, MA.
- Narendra, K.S. (1990), "Adaptive Control using Neural Networks," Ch. 5 in Neural Networks for Control (1990), Miller, W.T., R. Sutton, P. Werbos (eds.), MIT Press, Cambridge, MA.
Slides
- Lendaris, George G. (2009), "A Retrospective on Adaptive Dynamic Programming for ControlAdaptive Dynamic Programming for Control," Proceedings of the International Joint Conference on Neural Networks 2009 (IJCNN'09), Atlanta, Georgia, July.
- Lendaris, G., Mathia, K. (1996), "Efficient Numerical Inversion Using Multilayer Feedforward Neural Networks," World Congress on Neural Networks 1996, San Diego, California.
Faculty
Office Hours: Monday and Wednesday 3:30pm to 4:30pm
TA
Joshua Hughes (hughesjg@pdx.edu), Harder House, Room 207
Office Hours: Monday and Wednesday 1:00pm to 4:00pm (also available via e-mail and by appointment)
