Professor

School of Electrical and Computer Engineering

Oklahoma State University

Stillwater, OK 74078

(405) 744-7340

mhagan (at) okstate.edu

Courses | Brief Biography | Professional |

Academic Background | Selected Publications | Areas of Interest |

New Edition of Neural Network Design Textbook |

Courses

Spring 2017

Design of Engineering Systems ECEN 4013 (See the Online
Classroom)

Academic Background

Ph.D., Electrical Engineering, University of Kansas, 1977.

M.S., Information and Computer Science, Georgia Institute of Technology,
1973.

B.S., Electrical Engineering, University of Notre Dame, 1972.

Major Areas of Interest

Machine Learning, System Identification, Time Series Analysis, Control Systems, Signal Processing

Professional Experience

- Professor, School of Electrical and Computer Engineering, Oklahoma State University, August 1998-present.
- Visiting Scholar, Laboratoire d'Analyse et d'Architecture des Systèms, Centre National de la Recherche Scientifique, Toulouse, France, September 2005-July 2006.
- Director, Control Systems Engineering Program, College of Engineering, Architecture and Technology, Oklahoma State University, March 1999-September 2010.
- Visiting Scholar, Department of Electrical and Electronic Engineering, University of Canterbury, Christchurch, New Zealand, January 1994-December 1994.
- Associate Professor, School of Electrical and Computer Engineering, Oklahoma State University, June 1986-July 1998.
- Associate Professor and Graduate Program Coordinator, Electrical Engineering Department, University of Tulsa, September 1984-May 1986.
- Assistant Professor, Electrical Engineering Department, University of Tulsa, September 1978-August 1984.
- Member of Technical Staff, The Analytical Sciences Corporation (TASC), Reading, Massachusetts, November 1977-August 1978.

Brief Biography

Dr. Hagan has taught and conducted research in the areas of statistical modeling and control systems for the last thirty years. His research has encompassed a variety of application areas: seismic signal processing, genetic pathway modeling, optimal portfolio management, electric load prediction, flight simulators, precision pointing systems, diesel engines, adaptive flight control and friction compensation. He has received grants from Boeing, Texas Instruments, Halliburton Energy Services, Cummins Engine Company, National Science Foundation, Air Force Office of Scientific Research, California Public Employees Retirement System (CalPERS), Amgen, and FlightSafety International. For the last twenty years his research has focused on the use of neural networks for nonlinear filtering, prediction and control. He is the principal author, with Howard Demuth and Mark Beale, of the textbook Neural Network Design. He is also a co-author of the Neural Network Toolbox for MATLAB. He regularly teaches courses in stochastic processes, estimation theory, neural networks, system identification and control systems. He was awarded the Oklahoma State University Regents Distinguished Teaching Award in 2000 and the Lockheed Martin Aeronautics Teaching Excellence Award in 2005 and 2010.

Selected Publications

D. Hagan and M. Hagan, “Virtual Drug Screening Using Neural Networks,” Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vancouver, CA, July, 2016.

M. Alam, P. Patil, M. Hagan, D. Chandler, “A computational model for predicting local distortion visibility via convolutional neural network trained on natural scenes,” IEEE International Conference on Image Processing (ICIP), 2015, pp. 3967 - 3971.

A. Jafari, and M. Hagan. "Enhanced recurrent network training," Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 1-8., July, 2015.

R. Jafari, A. Kable and M. Hagan, “Forward and Converse Lyapunov Theorems
for Discrete Dynamical Systems,” * IEEE Transactions on Automatic
Control*, Vol. 59, No. 9, pp. 2496 - 2501, September, 2014.

M. Phan and M. Hagan, “Error Surface of Recurrent Networks,” * IEEE
Transactions on Neural Networks and Learning Systems*, Vol. 24, No.
11, pp. 1709 - 1721, October, 2013.

M. Phan and M. Hagan, "A Procedure for Training Recurrent Networks," *International
Joint
Conference on Neural Networks*, August 2013, Dallas, TX, Paper No.
1420.

L. M. Raff, R. Komanduri, M. T. Hagan, and S. T. Bukkapatnam, * Neural
Networks in Chemical Reaction Dynamics*, Oxford University Press,
2011.

A. Pukrittayakamee, M. Hagan, L. Raff, S. Bukkapatnam, R. Komanduri,
“Practical training framework for fitting functions and their
derivatives,” * IEEE Transactions on Neural Networks*, Vol. 22, No.
6, pp. 936 - 947, June 2011.

M. Malshe, L. M. Raff, M. Hagan, S. Bukkapatnam, and R. Komanduri, “Input
vector optimization of feed-forward neural networks for fitting ab initio
potential-energy databases,” *The Journal of Chemical Physics*, 132,
204103, 2010.

M. Malshe, A. Pukrittayakamee, L. M. Raff, M. Hagan, S. Bukkapatnam, and
R. Komanduri, “Accurate prediction of higher-level electronic structure
energies for large databases using neural networks, Hartree-Fock energies,
and small subsets of the database,” *The Journal of Chemical Physics*,
131, 124127, 2009.

M. Malshe, R. Narulkar, L. Raff, M. Hagan, S. Bukkapatnam, P. Agrawal,
and R. Komanduri, “Development of generalized potential-energy surfaces
(GPES) using many-body expansions, neural networks (NN), and moiety energy
(ME) approximations,” *The Journal of Chemical Physics*, 130,
184102, 2009.

A. Pukrittayakamee, M. Malshe,
M. Hagan, L. M. Raff, R. Narulkar, S. Bukkapatnum and R. Komanduri,
“Simultaneous fitting of a potential-energy surface and its corresponding
force fields using feedforward neural networks,” *The Journal of
Chemical Physics*, 130, 134101, 2009.

J. Horn, O. De Jesús and
M. Hagan, “Spurious Valleys in the Error Surface of Recurrent
Networks - Analysis and Avoidance,” *IEEE Transactions on Neural
Networks*, Vol. 20, No. 4, pp. 686-700, April 2009.

P.M. Agrawal, M. Malshe , R. Narulkar , L.M. Raff, M. Hagan, S.
Bukkapatnum and R. Komanduri , “A Self-Starting Method for Obtaining
Analytic Potential-Energy Surfaces from ab Initio Electronic Structure
Calculations,” * The Journal of Physical Chemistry*, A, Vol. 113,
No. 5, pp. 869-877, Feb., 2009.

M. Malshe, R. Narulkar, L. M. Raff, M. Hagan, S. Bukkapatnam and R.
Komanduri, “Parametrization of analytic interatomic potential functions
using neural networks,” *The Journal of Chemical Physics*, 129,
044111, 2008.

L. Hamm, B. W. Brorsen and M. T. Hagan, "Comparison of Stochastic Global
Optimization Methods to Estimate Neural Network Weights," *Neural
Processing Letters*, Vol. 26, No. 3, December 2007.

O. De Jesus and M. Hagan,
"Backpropagation Algorithms for a Broad Class of Dynamic Networks," *IEEE
Transactions on Neural Networks*, Vol. 18, No. 1, January 2007, pp.
14 -27.

P.M. Agrawal, L.M. Raff, M. Hagan, and R. Komanduri, "Molecular dynamics
investigations of the dissociation of SiO{_2} on ab initio potential
energy surface obtained using neural network methods," *The Journal of
Chemical Physics*, 124, 134306 (2006).

Agrawal, P.M., A.N.A. Samadh, L.M. Raff, M. Hagan, S. T. Bukkapatnam, and
R. Komanduri, "Prediction of molecular-dynamics simulation results using
feedforward neural networks: Reaction of a C2 dimer with an activated
diamond (100) surface," *The Journal of Chemical Physics*, 123,
224711 (2005).

Raff, L.M., M. Malshe, M. Hagan, D.I. Doughan, M.G. Rockley, and R.
Komanduri, "Ab initio potential-energy surfaces for complex, multi-channel
systems using modified novelty sampling and feedforward neural networks,"
*The Journal of Chemical Physics*, 122, 084104 (2005).

M. Hagan, H. Demuth, O. De Jesus, "An
Introduction to the Use of Neural Networks in Control Systems," *International
Journal of Robust and Nonlinear Control*, Vol. 12, No. 11, September,
2002, pp. 959-985. Related Software Demos

Torii, M. and M. Hagan , "Stability of Steepest Descent with Momentum for
Quadratic Functions," *IEEE Transactions on Neural Networks*, Vol.
13, No. 3, May 2002, pp. 752 -756.

F. Farbiz, M. Menhaj, S. Motamedi, and M. Hagan, "A New Fuzzy Logic
Filter for Image Enhancement," *IEEE Transactions on Systems, Man and
Cybernetics*, Vol. 30, No. 1, February 2000.

M. Hagan, O. De Jesus, and R. Schultz,
"Training Recurrent Networks for Filtering and Control," Chapter 11 of *Recurrent
Neural Networks:Design and Applications*, L.R. Medsker and L.C. Jain,
Eds., CRC Press, 1999, pp. 325-354.

M. Fun and M. Hagan, "Recursive Orhogonal
Least Squares Learning with Automatic Weight Selection for Gaussian Neural
Networks," *International Joint Conference on Neural Networks*, July
1999, Washington, Paper No. 322.

R. Schultz and M. Hagan, "Training
Multiloop Networks," *International Joint Conference on Neural Networks*,
July 1999, Washington, Paper No. 514.

R. Schultz and M. Hagan, "On-Line Least
Squares Learning for the Underdetermined Case," *International Joint
Conference on Neural Networks*, July 1999, Washington, Paper No. 515.

D. Chen and M. Hagan, "Optimal Use of
Regularization and Cross-Validation in Neural Network Modeling," *International
Joint Conference on Neural Networks*, July 1999, Washington, Paper
No. 323.

M. Hagan and H. Demuth, "Neural
Networks for Control," Invited Tutorial, 1999 *American Control
Conference*, June, 1999, San Diego, pp. 1642-1656.

C. James, M. Hagan, R. Jones, P. Bones, and G. Carrol, "Neural Network
Based Spatio-Temporal Filtering of the EEG via Multireference Adaptive
Noise Canceling," *IEEE Transactions on Biomedical Engineering*.
Vol. 44, No. 8, pp. 775-780, 1997.

D. Foresee and M. Hagan, "Gauss-Newton
Approximation to Bayesian Learning," *Proceedings of the 1997
International Joint Conference on Neural Networks*.

M. Fun and M. Hagan, "Levenberg-Marquardt Training for Modular Networks,
" *Proceedings of the 1996 International Conference on Neural Networks*,
pp. 468-473, 1996.

M. Hagan, H. Demuth, and M. Beale, *Neural
Network Design*, Boston, MA: PWS Publishing, 1996.

M. Hagan and C. Latino, "A Modular Control
Systems Laboratory, " *Computer Applications in Engineering Education*
Vol. 3, No. 2, 1995, pp. 89-96.

M. Hagan and M. Menhaj, "Training Feedforward Networks with the Marquardt
Algorithm, " *IEEE Transactions on Neural Networks*, Vol. 5, No. 6,
November 1994, pp. 989-993.

Y. Zhang and M. Hagan, "A Reduced Parameter Bilinear Time Series Model, "
*IEEE Transactions on Signal Processing*, Vol. 42, No. 7, July 1994,
pp. 1867-1870.

M. Menhaj and M. Hagan, "Analysis of Delays in Networked Flight
Simulators, " *IEEE Transactions on Systems, Man and Cybernetics*,
Vol. 24, No. 6, June 1994, pp. 875-881.

M. Hagan, N. Sammur, G. Guimaraes, C. Latino and J. Hamalainen,
"Distributed Control Loading and Motion for Flight Simulation," *Progress
in Simulation*, Vol. 2, Ablex Publishing, 1994, pp. 52-90.

N. Sammur and M. Hagan, "Mapping Signal Processing Algorithms on Parallel
Architectures," *Journal of Parallel and Distributed Computing*,
Vol. 8, No. 2, February 1990.

M. Hagan and S. Behr, "The Time Series Approach to Short Term Load
Forecasting," *IEEE Transactions on Power Apparatus and Systems*,
Vol. PWRS-2, No. 3, pp. 785-791, August 1987.

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