Silvia Ferrari

Publications

  • Rudd, K; Albertson, JD; Ferrari, S,
    Optimal root profiles in water-limited ecosystems
    , Advances in Water Resources, vol 71 (2014), pp. 16-22 [10.1016/j.advwatres.2014.04.021] [abs].
  • Rudd, K; Di Muro, G; Ferrari, S, A constrained backpropagation approach for the adaptive solution of partial differential equations., IEEE Transactions on Neural Networks and Learning Systems, vol 25 no. 3 (2014), pp. 571-584 [10.1109/tnnls.2013.2277601] [abs].
  • Lu, W; Zhang, G; Ferrari, S; Anderson, M; Fierro, R,
    A particle-filter information potential method for tracking and monitoring maneuvering targets using a mobile sensor agent
    , Journal of Defense Modeling and Simulation, vol 11 no. 1 (2014), pp. 47-58 [10.1177/1548512912445406] [abs].
  • Foderaro, G; Ferrari, S; Wettergren, TA, Distributed optimal control for multi-agent trajectory optimization, Automatica, vol 50 no. 1 (2014), pp. 149-154 [abs].
  • Foderaro, G; Ferrari, S; Wettergren, TA, Distributed optimal control for multi-agent trajectory optimization, Automatica (2013) [abs].
  • Zielinski, DJ; Kopper, R; McMahan, RP; Lu, W; Ferrari, S, Intercept tags: Enhancing intercept-based systems, Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST (2013), pp. 263-266 [abs].
  • Lu, W; Ferrari, S, An approximate dynamic programming approach for model-free control of switched systems, Proceedings of the IEEE Conference on Decision and Control (2013), pp. 3837-3844 [abs].
  • Rudd, K; Foderaro, G; Ferrari, S, A generalized reduced gradient method for the optimal control of multiscale dynamical systems, Proceedings of the IEEE Conference on Decision and Control (2013), pp. 3857-3863 [abs].
  • Swingler, A; Ferrari, S, On the duality of robot and sensor path planning, Proceedings of the IEEE Conference on Decision and Control (2013), pp. 984-989 [abs].
  • Wei, H; Ross, W; Varisco, S; Krief, P; Ferrari, S, Modeling of human driver behavior via receding horizon and artificial neural network controllers, Proceedings of the IEEE Conference on Decision and Control (2013), pp. 6778-6785 [abs].
  • Zielinski, DJ; McMahan, RP; Lu, W; Ferrari, S, ML2VR: Providing MATLAB users an easy transition to virtual reality and immersive interactivity, Proceedings - IEEE Virtual Reality (2013), pp. 83-84 [10.1109/VR.2013.6549374] [abs].
  • Ferrari, S; Rudd, K; Di Muro, G, A Constrained Backpropagation Approach to Function Approximation and Approximate Dynamic Programming, Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (2013), pp. 162-181 [abs].
  • Zhang, G; Ferrari, S; Cai, C, A comparison of information functions and search strategies for sensor planning in target classification., IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, vol 42 no. 1 (2012), pp. 2-16 [10.1109/TSMCB.2011.2165336] [abs].
  • Maheswaranathan, N; Ferrari, S; Vandongen, AM; Henriquez, CS, Emergent bursting and synchrony in computer simulations of neuronal cultures., Frontiers in Computational Neuroscience, vol 6 (2012) [10.3389/fncom.2012.00015] [abs].
  • Ferrari, S; Daugherty, G, A Q-learning approach to automated unmanned air vehicle demining, Journal of Defense Modeling and Simulation, vol 9 no. 1 (2012), pp. 83-92 [10.1177/1548512911414599] [abs].
  • Lu, W; Ferrari, S; Fierro, R; Wettergren, TA, Approximate Dynamic Programming Recurrence Relations for a Hybrid Optimal Control Problem, Proceedings of SPIE - The International Society for Optical Engineering, vol 8387 (2012) [10.1117/12.919286] [abs].
  • Lu, W; Zhang, G; Ferrari, S; IEEE, , A COMPARISON OF INFORMATION THEORETIC FUNCTIONS FOR TRACKING MANEUVERING TARGETS, 2012 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP) (2012), pp. 149-152 [abs].
  • Foderaro, G; Swingler, A; Ferrari, S, A model-based cell decomposition approach to on-line pursuit-evasion path planning and the video game Ms. Pac-Man, 2012 IEEE Conference on Computational Intelligence and Games, CIG 2012 (2012), pp. 281-287 [10.1109/CIG.2012.6374167] [abs].
  • Tolic, D; Fierro, R; Ferrari, S, Optimal self-triggering for nonlinear systems via Approximate Dynamic Programming, Proceedings of the IEEE International Conference on Control Applications (2012), pp. 879-884 [10.1109/CCA.2012.6402727] [abs].
  • Ferrari, S; Sarangapani, J; Lewis, FL, Special issue on approximate dynamic programming and reinforcement learning, Journal of Control Theory and Applications, vol 9 no. 3 (2011) [10.1007/s11768-011-1104-1] [abs].
  • Ferrari, S; Anderson, M; Fierro, R; Lu, W, Cooperative navigation for heterogeneous autonomous vehicles via approximate dynamic programming, Proceedings of the IEEE Conference on Decision and Control (2011), pp. 121-127 [10.1109/CDC.2011.6161127] [abs].
  • Foderaro, G; Raju, V; Ferrari, S, A model-based approximate λ-policy iteration approach to online evasive path planning and the video game Ms. Pac-Man, Journal of Control Theory and Applications, vol 9 no. 3 (2011), pp. 391-399 [10.1007/s11768-011-0272-3] [abs].
  • Foderaro, G; Raju, V; Ferrari, S, A cell decomposition approach to online evasive path planning and the video game Ms. Pac-Man, IEEE International Symposium on Intelligent Control - Proceedings (2011), pp. 191-197 [10.1109/ISIC.2011.6045414] [abs].
  • Bezzo, N; Fierro, R; Swingler, A; Ferrari, S, A disjunctive programming approach for motion planning of mobile router networks, International Journal of Robotics and Automation, vol 26 no. 1 (2011), pp. 13-25 [10.2316/Journal.206.2011.1.206-3405] [abs].
  • Lu, W; Zhang, G; Ferrari, S; Fierro, R; Palunko, I, An information potential approach for tracking and surveilling multiple moving targets using mobile sensor agents, Proceedings of SPIE - The International Society for Optical Engineering, vol 8045 (2011) [10.1117/12.884116] [abs].
  • Ferrari, S; Zhang, G; Wettergren, TA, Probabilistic track coverage in cooperative sensor networks., IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, vol 40 no. 6 (2010), pp. 1492-1504 [10.1109/TSMCB.2010.2041449] [abs].
  • Lu, W; Zhang, G; Ferrari, S, A randomized hybrid system approach to coordinated robotic sensor planning, Proceedings of the IEEE Conference on Decision and Control (2010), pp. 3857-3864 [10.1109/CDC.2010.5717351] [abs].
  • Foderaro, G; Ferrari, S, Necessary conditions for optimality for a distributed optimal control problem, Proceedings of the IEEE Conference on Decision and Control (2010), pp. 4831-4838 [10.1109/CDC.2010.5718021] [abs].
  • Bernard, B; Ferrari, S, A geometric transversals approach to analyzing track coverage of omnidirectional sensor networks for maneuvering targets, Proceedings of the IEEE Conference on Decision and Control (2010), pp. 1243-1249 [10.1109/CDC.2010.5717198] [abs].
  • Swingler, A; Ferrari, S, A cell decomposition approach to cooperative path planning and collision avoidance via disjunctive programming, Proceedings of the IEEE Conference on Decision and Control (2010), pp. 6329-6336 [10.1109/CDC.2010.5717137] [abs].
  • Foderaro, G; Henriquez, C; Ferrari, S, Indirect training of a spiking neural network for flight control via spike-timing-dependent synaptic plasticity, Proceedings of the IEEE Conference on Decision and Control (2010), pp. 911-917 [10.1109/CDC.2010.5717260] [abs].
  • Ferrari, S; Foderaro, G; Tremblay, A, A probability density function approach to distributed sensors' path planning, Proceedings - IEEE International Conference on Robotics and Automation (2010), pp. 432-439 [10.1109/ROBOT.2010.5509184] [abs].
  • Ferrari, S; Daugherty, G, Q-learning approach to automated Unmanned Air Vehicle (UAV) demining, Proceedings of SPIE - The International Society for Optical Engineering, vol 7692 (2010) [10.1117/12.850135] [abs].
  • Ferrari, S; Foderaro, G, A potential field approach to finding minimum-exposure paths in wireless sensor networks, Proceedings - IEEE International Conference on Robotics and Automation (2010), pp. 335-341 [10.1109/ROBOT.2010.5509193] [abs].
  • Cai, C; Ferrari, S, Information-driven sensor path planning by approximate cell decomposition., IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, vol 39 no. 3 (2009), pp. 672-689 [10.1109/TSMCB.2008.2008561] [abs].
  • Ferrari, S; Cai, C, Information-driven search strategies in the board game of CLUE., IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, vol 39 no. 3 (2009), pp. 607-625 [10.1109/TSMCB.2008.2007629] [abs].
  • Ferrari, S, Multiobjective algebraic synthesis of neural control systems by implicit model following., IEEE Transactions on Neural Networks, vol 20 no. 3 (2009), pp. 406-419 [10.1109/TNN.2008.2008332] [abs].
  • Zhang, G; Ferrari, S; Qian, M, An information roadmap method for robotic sensor path planning, Journal of Intelligent and Robotic Systems: theory and applications, vol 56 no. 1-2 (2009), pp. 69-98 [10.1007/s10846-009-9318-x] [abs].
  • Baumgartner, KAC; Ferrari, S; Wettergren, TA, Robust deployment of dynamic sensor networks for cooperative track detection, IEEE Sensors Journal, vol 9 no. 9 (2009), pp. 1029-1048 [10.1109/JSEN.2009.2025836] [abs].
  • Ferrari, S; Fierro, R; Tolic, D, A geometric optimization approach to tracking maneuvering targets using a heterogeneous mobile sensor network, Proceedings of the IEEE Conference on Decision and Control (2009), pp. 1080-1087 [10.1109/CDC.2009.5400166] [abs].
  • Zhang, G; Ferrari, S, An adaptive artificial potential function approach for geometric sensing, Proceedings of the IEEE Conference on Decision and Control (2009), pp. 7903-7910 [10.1109/CDC.2009.5399490] [abs].
  • Muro, GD; Ferrari, S, A constrained backpropagation approach to solving Partial Differential Equations in non-stationary environments, Proceedings of the International Joint Conference on Neural Networks (2009), pp. 685-689 [10.1109/IJCNN.2009.5179018] [abs].
  • Baumgartner, KAC; Ferrari, S; Rao, AV, Optimal control of an underwater sensor network for cooperative target tracking, IEEE Journal of Oceanic Engineering, vol 34 no. 4 (2009), pp. 678-697 [10.1109/JOE.2009.2025643] [abs].
  • Ferrari, S; Fierro, R; Perteet, B; Cai, C; Baumgartner, K, A geometric optimization approach to detecting and intercepting dynamic targets using a mobile sensor network, SIAM Journal on Control and Optimization, vol 48 no. 1 (2009), pp. 292-320 [10.1137/070679144] [abs].
  • Ferrari, S; Steck, JE; 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), pp. 982-987 [10.1109/TSMCB.2008.924140] [abs].
  • Ferrari, S; Jensenius, M, A constrained optimization approach to preserving prior knowledge during incremental training., IEEE Transactions on Neural Networks, vol 19 no. 6 (2008), pp. 996-1009 [10.1109/TNN.2007.915108] [abs].
  • Baumgartner, K; Ferrari, S, A geometric transversal approach to analyzing track coverage in sensor networks, IEEE Transactions on Computers, vol 57 no. 8 (2008), pp. 1113-1128 [10.1109/TC.2008.56] [abs].
  • Ferrari, S; Baumgartner, KC; Palermo, GB; Bruzzone, R; Strano, M, Network models of criminal behavior, IEEE Control Systems Magazine, vol 28 no. 4 (2008), pp. 65-77 [10.1109/MCS.2008.924037] [abs].
  • Zhang, G; Ferrari, S; Qian, M, Information Roadmap Method for Robotic Sensor Path Planning, Journal of Intelligent and Robotic Systems (2008) [abs].
  • Ferrari, S; Fierro, R; Perteet, B; Cai, C; Baumgartner, KC, A Multi-Objective Optimization Approach to Detecting and Intercepting Dynamic Targets Using Mobile Sensors, SIAM Journal on Control and Optimization (2008) [abs].
  • Baumgartner, K; Ferrari, S; Palermo, G, Constructing Bayesian networks for criminal profiling from limited data, Knowledge-Based Systems, vol 21 no. 7 (2008), pp. 563-572 [10.1016/j.knosys.2008.03.019] [abs].
  • Fierro, R; Ferrari, S; Cai, C, An information-driven framework for motion planning in robotic sensor networks: Complexity and experiments, Proceedings of the IEEE Conference on Decision and Control (2008), pp. 483-489 [10.1109/CDC.2008.4739437] [abs].
  • Cai, C; Ferrari, S, A Q-learning approach to developing an automated neural computer player for the board game of CLUE®, Proceedings of the International Joint Conference on Neural Networks (2008), pp. 2346-2352 [10.1109/IJCNN.2008.4634123] [abs].
  • Ferrari, S; Mehta, B; Muro, GD; VanDongen, AMJ; Henriquez, C, Biologically realizable reward-modulated hebbian training for spiking neural networks, Proceedings of the International Joint Conference on Neural Networks (2008), pp. 1780-1786 [10.1109/IJCNN.2008.4634039] [abs].
  • Muro, GD; Ferrari, S, A constrained-optimization approach to training neural networks for smooth function approximation and system identification, Proceedings of the International Joint Conference on Neural Networks (2008), pp. 2353-2359 [10.1109/IJCNN.2008.4634124] [abs].
  • Cai, C; Ferrari, S; Qian, M, Bayesian network modeling of acoustic sensor measurements, Proceedings of IEEE Sensors (2007), pp. 345-348 [10.1109/ICSENS.2007.4388406] [abs].
  • Ferrari, S; Cai, C; Fierro, R; Perteet, B, A geometric optimization approach to detecting and intercepting dynamic targets, Proceedings of the American Control Conference (2007), pp. 5316-5321 [10.1109/ACC.2007.4282986] [abs].
  • Baumgartner, K; Ferrari, S, Optimal placement of a moving sensor network for track coverage, Proceedings of the American Control Conference (2007), pp. 4040-4046 [10.1109/ACC.2007.4282825] [abs].
  • Cai, C; Ferrari, S, Comparison of information-theoretic objective functions for decision support in sensor systems, Proceedings of the American Control Conference (2007), pp. 3559-3564 [10.1109/ACC.2007.4282852] [abs].
  • Chandramohan, R; Steck, JE; Rokhsaz, K; Ferrari, S, Adaptive critic flight control for a general aviation aircraft: Simulations for the beech bonanza fly-by-wire test bed, Collection of Technical Papers - 2007 AIAA InfoTech at Aerospace Conference, vol 1 (2007), pp. 840-855 [abs].
  • Ferrari, S; Vaghi, A, Demining sensor modeling and feature-level fusion by bayesian networks, IEEE Sensors Journal, vol 6 no. 2 (2006), pp. 471-483 [10.1109/JSEN.2006.870162] [abs].
  • Cai, C; Ferrari, S, On the development of an intelligent computer player for CLUE®: A case study on preposterior decision analysis, Proceedings of the American Control Conference, vol 2006 (2006), pp. 4350-4355 [abs].
  • Ferrari, S, Track coverage in sensor networks, Proceedings of the American Control Conference, vol 2006 (2006), pp. 2053-2059 [abs].
  • Ferrari, S; Stengel, RF, Smooth function approximation using neural networks., IEEE Transactions on Neural Networks, vol 16 no. 1 (2005), pp. 24-38 [10.1109/TNN.2004.836233] [abs].
  • Qian, M; Ferrari, S, Probabilistic deployment for multiple sensor systems, Proceedings of SPIE - The International Society for Optical Engineering, vol 5765 no. PART 1 (2005), pp. 85-96 [10.1117/12.601597] [abs].
  • Ferrari, S; Jensenius, M, Robust and reconfigurable flight control by neural networks, Collection of Technical Papers - InfoTech at Aerospace: Advancing Contemporary Aerospace Technologies and Their Integration, vol 2 (2005), pp. 1161-1166 [abs].
  • Baumgartner, KC; Ferrari, S; Salfati, CG, Bayesian network modeling of offender behavior for criminal profiling, Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05, vol 2005 (2005), pp. 2702-2709 [10.1109/CDC.2005.1582571] [abs].
  • Ferrari, S; Stengel, RF, Online adaptive critic flight control, Journal of Guidance, Control, and Dynamics: devoted to the technology of dynamics and control, vol 27 no. 5 (2004), pp. 777-786 [abs].
  • Ferrari, S; Stengel, RF, Classical/neural synthesis of nonlinear control systems, Journal of Guidance, Control, and Dynamics, vol 25 no. 3 (2002), pp. 442-448 [abs].
  • Ferrari, S; Stengel, RF, An adaptive critic global controller, Proceedings of the American Control Conference, vol 4 (2002), pp. 2665-2670 [10.1109/ACC.2002.1025189] [abs].
  • Ferrari, S; Stengel, RF, Algebraic training of a neural network, Proceedings of the American Control Conference, vol 2 (2001), pp. 1605-1610 [abs].
  • Ferrari, S; Stengel, RF, Classical/neural synthesis of nonlinear control systems, AIAA Guidance, Navigation, and Control Conference and Exhibit (2000) [abs].
  • Crispin, Y; Ferrari, S, Adaptive control of chaos induced capsizing of a ship, Intelligent Engineering Systems Through Artificial Neural Networks, vol 5 (1995), pp. 569-574 [abs].