Home page of Danil Prokhorov

I work on system modeling, control and optimization using machine learning methods. I am especially known in the areas of recurrent neural networks and neurocontrol. My work has recently been cited in Simon Haykin's latest edition of very popular textbook Neural Networks and Learning Machines, Prentice Hall, 2008.

I am familiar with all main topics of computational intelligence.

# I am currently with Toyota Research Institute NA, Ann Arbor, Michigan
# I got my Masters in Russia (Robotics) in 1992 and my Ph.D. (EE) in 1997 as a member of ACIL)
# I was with Scientific Research Laboratory of Ford Motor Co. (Metropolitan Detroit area) from November 1997 to August 2005
# I am a recipient of the 1999 Young Investigator Award of the International Neural Network Society (INNS)
# I am the INNS VP for Conferences, and have extensive experience as conference/workshop/session organizer and Program Committee member
# I have been helping the U.S. National Science Foundation (NSF) to award grants by reviewing proposals from academia and industry since 1995
# I am Associate Editor of Neural Networks, IEEE Trans. on Neural Networks and IEEE Trans. on Autonomous Mental Development
# I am Senior Member of both IEEE and INNS
# I am glad to help you with your neural network training problem. I also provide tutorial services.

Selected publications (see below for the yet-to-be-updated full list):

**- D. Prokhorov (Ed.), Computational Intelligence in Automotive Applications. Springer, 2008.**

**- S. Singh et al., Dynamic Multiple Fault Diagnosis: Mathematical Formulations and Solution Techniques, IEEE Trans. on Systems, Man and Cybernetics (Part A), January 2009.**

**- I. Tyukin, D. Prokhorov, and C. van Leeuwen, Adaptive Classification of Temporal Signals in Fixed-Weight Recurrent Neural Networks: An Existence Proof, Neural Computation, October 2008.**

**- D. Prokhorov, Prius HEV neurocontrol and diagnostics, Neural Networks, 21 (2008), pp. 458-465. (An early version of this paper received IJCNN 2007 Best Paper Award, Orlando, FL, August 2007.)**

**- P. Jurica et al., Unsupervised adaptive optimization of motion-sensitive systems guided by measurement uncertainty, Proc. of ISSNIP, Melbourne, Australia, December 2007.**

**- S. Kalik and D. Prokhorov, Automotive Turing Test, Prof. of Performance Metrics in Intelligent Systems (PerMIS), Washington DC, August 2007.**

**- D. Prokhorov, Training neurocontrollers for robustness with derivative-free Kalman filter, IEEE Trans. on Neural Networks, November 2006.**

**- D. Prokhorov, Echo State Networks: Appeal and Challenges, Proc. of International Joint Conference on Neural Networks (IJCNN), Montreal, Canada, August 2005.**

**- D. Prokhorov, Backpropagation Through Time and Derivative Adaptive Critics: A Common Framework for Comparison. In J. Si et al. (Eds.), Learning and Approximate Dynamic Programming, Wiley, 2004.**

- Prokhorov, D., Feldkamp, L., and I. Tyukin, "Adaptive Behavior with Fixed Weights in Recurrent Neural Networks: An Overview," Proc. of International Joint Conference on Neural Networks (IJCNN), WCCI'02, Honolulu, Hawaii, May 2002.

**- N. Barabanov and D. Prokhorov, "Stability Analysis of Discrete-Time Recurrent Neural Networks," IEEE Trans. on Neural Networks, March 2002.**

**- Prokhorov, D., Feldkamp, L., and T. Feldkamp, "A New Approach to Cluster Weighted Modeling," Proc. of International Joint Conference on Neural Networks (IJCNN), Washington DC, July 2001.**

- A. Petrosian, D. Prokhorov, W. Lajara-Nanson, and B. Schiffer, "Recurrent Neural Network Based Approach for Early Recognition of Alzheimer's disease in EEG," Clinical Neurophysiology, Vol.112/8, pp. 1378-1387, 2001.

- D. Prokhorov, G. Puskorius, and L. Feldkamp, **Dynamical Neural Networks for Control. In J. Kolen and S. Kremer (Eds.) A Field Guide to Dynamical Recurrent Networks, IEEE Press, 2001.**

**- L. Feldkamp, T. Feldkamp, and D. Prokhorov, "An Approach to Adaptive Classification," in S. Haykin (Ed.) Intelligent Signal Processing, IEEE Press, 2001.**

- Eaton, P., Prokhorov, D., and D. Wunsch, "Neurocontroller Alternatives for "Fuzzy" Ball-and-Beam Systems with Nonlinear, Nonuniform Friction," IEEE Trans. on Neural Networks, March 2000, pp. 423-435.

- L. Feldkamp, D. Prokhorov, C. Eagen, and F. Yuan, Enhanced Multi-Stream Kalman Filter Training for Recurrent Networks. In J. Suykens and J. Vandewalle (Eds.) Nonlinear Modeling: Advanced Black-Box Techniques, pp. 29-53, Kluwer Academic Publishers, 1998.

**- Prokhorov, D., and D. Wunsch, "Adaptive Critic Designs," IEEE Trans. on Neural Networks, Vol. 8, No. 5, September 1997, pp. 997-1007.**

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Last update January 2009