MEMSDUKEPRATT School of engineering

Education

  • PhD, Princeton University, Princeton, NJ, 2002
  • MA, Princeton University, Princeton, NJ, 1999
  • BS, Embry-Riddle Aeronautical University, Daytona Beach, FL, 1997
Silvia Ferrari
  • Office Phone: (919) 660-5310
  • Email Address: silvia.ferrari@duke.edu
  • Web Page: http://fred.mems.duke.edu/

    • Professor Ferrari's research aims at providing intelligent control systems with a higher degree of mathematical structure to guide their application and improve reliability. Decision-making processes are automated based on concepts drawn from control theory and the life sciences. Recent efforts have focused on the development of reconfigurable controllers implementing neural networks with procedural long-term memories. Full-scale simulations show that these controllers are capable of learning from new and unmodeled aircraft dynamics in real time, improving performance and even preventing loss of control in the event of control failures, nonlinear and near-stall dynamics, and parameter variations. New optimal control problems and methods based on computational geometry are being investigated to improve the effectiveness of integrated surveillance systems by networks of autonomous vehicles, such as, underwater gliders and ground robots.

    Specialties
    neural networks
    Bayesian networks
    Controls
    Smart Technology

    TEACHING (Fall 2009)

    ME 125L.001, CONTROL SYSTEMS, MF 01:15 PM-02:30 PM
    ME 125L.01L, CONTROL SYSTEMS, Tu 04:25 PM-06:50 PM
    ME 125L.02L, CONTROL SYSTEMS, Th 03:05 PM-05:30 PM
    ME 125L.03L, CONTROL SYSTEMS, W 03:05 PM-05:30 PM

    TEACHING (Spring 2010)

    ME 233.01, INTELLIGENT SYSTEMS,

    Recent Publications More Publications

    1. C. Cai and S. Ferrari, Information-Driven Sensor Path Planning by Approximate Cell Decomposition, IEEE Transaction on Systems, Man, and Cybernetics - Part B, (2008)
    2. S. Ferrari, Multi-Objective Algebraic Synthesis of Neural Control Systems by Implicit Model Following, IEEE Transactions on Neural Networks, (2008)
    3. Ferrari S., Steck J.E. and Chandramohan R., Adaptive Feedback Control by Constrained Approximate Dynamic Programming, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 38 no. 4 (2008), ppt. 982-987
    4. K. Baumgartner and S. Ferrari, A geometric transversal approach to analyzing track coverage in sensor networks, Ieee Transactions On Computers, vol. 57 no. 8 (2008), ppt. 1113 -- 1128
    5. Baumgartner, K.C., and Ferrari S., Constructing Bayesian Networks for Criminal Profiling from Limited Data, Knowledge-Based Systems, (2008)
    6. Ferrari, S., and Jensenius M., A Constrained Optimization Approach to Preserving Prior Knowledge During Incremental Training, IEEE Transactions on Neural Networks, vol. 19 no. 6 (2008)
    7. Ferrari, Silvia and Vaghi, Alberto, Demining sensor modeling and feature-level fusion by bayesian networks, IEEE Sensors Journal, vol. 6 no. 2 (2006), ppt. 471 - 483 , [JSEN.2006.870162] [abs]
    8. Ferrari, Silvia and Stengel, Robert F., Smooth function approximation using neural networks, IEEE Transactions on Neural Networks, vol. 16 no. 1 (2005), ppt. 24 - 38 , [TNN.2004.836233] [abs]
    9. S. Ferrari and R. F. Stengel, Online adaptive critic flight control, Journal Of Guidance Control And Dynamics, vol. 27 no. 5 (2004), ppt. 777 -- 786
    10. Ferrari, Silvia and Stengel, Robert F., Classical/neural synthesis of nonlinear control systems, Journal of Guidance, Control, and Dynamics, vol. 25 no. 3 (2002), ppt. 442 - 448 [abs]

    Research Interests

      Design and analysis of methods and algorithms for learning and computational intelligence. Theory and approximation properties of network models, such as neural and probabilistic networks, for the purpose of enhancing their learning abilities and improving reliability. Approximate dynamic programming and optimal control techniques, with applications in adaptive flight control and mobile sensor networks. Application of expert systems and systems theory to psychological and cognitive modeling from data.

    The mission of Duke's Mechanical Engineering and Materials Science educational programs is to provide the knowledge, skills, and credentials needed to be successful in the practice of engineering; the preparation necessary to undertake professional registration; an educational preparation for graduate or professional study; and an education background that is the basis for professional growth and leadership throughout a career that may encompass a broad range of endeavors, both technical and non-technical.