System identification

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In control engineering, the field of system identification uses statistical methods to build mathematical models of dynamical systems from measured data. (Wikipedia.org)






Conferences related to System identification

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2017 IEEE Power & Energy Society General Meeting

The annual IEEE Power & Energy Society General Meeting will bring together over 2000 attendees for technical sessions, student program, awards ceremony, committee meetings, and tutorials.

  • 2015 IEEE Power & Energy Society General Meeting

    The annual IEEE PES General Meeting will bring together over 2500 attendees for technical sessions, administrative sessions, super sessions, poster sessions, student programs, awards ceremonies, committee meetings, tutorials and more PLEASE NOTE: Abstracts are not accepted for the 2015 IEEE PES General Meeting, full papers only can be submitted to the submission site 24 October 2014 through 21 November 2014.  The site will be available from the PES home page www.ieee-pes.org

  • 2014 IEEE Power & Energy Society General Meeting

    The annual IEEE PES General Meeting will bring together over 2500 attendees for technical sessions, administrative sessions, super sessions, poster sessions, student programs, awards ceremonies, committee meetings, tutotials and more

  • 2013 IEEE Power & Energy Society General Meeting

    The annual IEEE Power & Energy Society General Meeting will bring together over 2000 attendees for technical sessions, student program, awards ceremony, committee meetings, and tutorials.

  • 2012 IEEE Power & Energy Society General Meeting

    The annual IEEE Power & Energy Society General Meeting will bring together over 2000 attendees for technical sessions, student program, awards ceremony, committee meetings, and tutorials.

  • 2011 IEEE Power & Energy Society General Meeting

    IEEE Power & Energy Annual Meeting --Papers --Awards --Plenary --Committee Meetings --Governing Board --Receptions --Tech tours --Tutorials --Companions Program

  • 2010 IEEE Power & Energy Society General Meeting

    IEEE Power & Energy Society Annual Meeting --Technical Sessions --Committee Meetings --Plenary Session --Gove Board Meeting --Awards Banquet --Tutorials --Student Activities --Social Events --Companions Program

  • 2009 IEEE Power & Energy Society General Meeting

    Paper and Panel sessions involving topics of interest to electric power engineers, technical committee meetings, administrative committee meetings, awards luncheon and plenary session.


2014 American Control Conference - ACC 2014

All areas of the theory and practice of automatic control, including but not limited to network control systems, model predictive control, systems analysis in biology and medicine, hybrid and switched systems, aerospace systems, power and energy systems and control of nano- and micro-systems.

  • 2013 American Control Conference (ACC)

    Control systems theory and practice. Conference themes on sustainability, societal challenges for control, smart healthcare systems. Conference topics include biological systems, vehicle dynamics and control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2012 American Control Conference - ACC 2012

    All areas of control engineering and science.

  • 2011 American Control Conference - ACC 2011

    ACC provides a forum for bringing industry and academia together to discuss the latest developments in the area of Automatic Control Systems, from new control theories, to the advances in sensors and actuator technologies, and to new applications areas for automation.

  • 2010 American Control Conference - ACC 2010

    Theory and practice of automatic control


2013 13th International Conference on Control, Automaton and Systems (ICCAS)

Control Theory and Application, Intelligent Systems, Industrial Applications of Control,Sensor and Signal Processing, Control Devices and Instruments, Robot Control, RobotVision, Human-Robot Interaction, Robotic Applications, Unmanned Vehicle Systems...


2012 16th IFAC Symposium on System Identification (SYSID 2012)

SYSID aims at promoting research and development activities in the area of system identification, experimental modeling, signal processing and adaptive control. The scope of the symposium covers all aspects of these areas, ranging from theoretical investigations to a large variety of applications.


2012 International Conference on Modelling, Identification and Control (ICMIC)

Static and dynamic systems modeling, control and optimization techniques, and nonlinear & linear system identification.

  • 2011 International Conference on Modelling, Identification and Control (ICMIC)

    The 3rd Conference provides a forum for professionals, academics, and researchers to present latest developments from interdisciplinary studies, algorithms and applications. It particularly welcomes those emerging methodologies and techniques that bridge theoretical studies and applications in all engineering and science branches. Novel quantitative economical, financial studies are considered as well.

  • 2010 International Conference on Modelling, Identification and Control (ICMIC)

    This international conference will bring together top researchers, practitioners and students from around the world to discuss the latest advances in the field of system modelling, system identification and system control.


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Periodicals related to System identification

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Instrumentation and Measurement, IEEE Transactions on

Measurements and instrumentation utilizing electrical and electronic techniques.


Power Delivery, IEEE Transactions on

Research, development, design, application, construction, the installation and operation of apparatus, equipment, structures, materials, and systems for the safe, reliable, and economic delivery and control of electric energy for general industrial, commercial, public, and domestic consumption.


Power Systems, IEEE Transactions on

Requirements, planning, analysis, reliability, operation, and economics of electrical generating, transmission, and distribution systems for industrial, commercial, public, and domestic consumption.


Signal Processing, IEEE Transactions on

The technology of transmission, recording, reproduction, processing, and measurement of speech; other audio-frequency waves and other signals by digital, electronic, electrical, acoustic, mechanical, and optical means; the components and systems to accomplish these and related aims; and the environmental, psychological, and physiological factors of thesetechnologies.



Most published Xplore authors for System identification

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Xplore Articles related to System identification

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Coupled adaptive prediction and system identification: a statistical model and transient analysis

M. Mboup; M. Bonnet; N. Bershad Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on, 1992

A significant drawback of the least mean square (LMS) algorithm is slow convergence speed when the input covariance matrix is ill-conditioned. Two structures are presented and studied for increasing the convergence speed for this case. The structures incorporate a prewhitening filter prior to the usual LMS adaptation. When the prewhitening filter is also adaptive the input to the LMS algorithm ...


Stabilization of a plant with time-varying operating conditions: an interpolated stabilizing controller

E. Muramatsu; M. Ikeda Decision and Control, 1997., Proceedings of the 36th IEEE Conference on, 1997

A stabilization problem is considered for a multi-input multi-output plant with time-varying operating conditions. The plant is described as an interpolation of two representative models defined at two representative operating points. To deal with the change of the plant dynamics, a state space description of an interpolated model, originally defined by coprime factorizations of transfer functions, is introduced. Corresponding to ...


Real-time multi-network based identification with dynamic selection implemented for a low cost UAV

Vishwas R. Puttige; Sreenatha G. Anavatti 2007 IEEE International Conference on Systems, Man and Cybernetics, 2007

This paper describes a system identification technique based on dynamic selection of multiple neural networks for the Unmanned Aerial Vehicle (UAV). The UAV is a multi- input multi-output (MIMO) nonlinear system. The neural network models are based on the autoregressive technique. The multi-network dynamic selection method allows a combination of online and offline neural network models to be used in ...


Adaptive parallel identification of dynamical systems by adaptive recurrent neural networks

J. T. Lo; D. Bassu Neural Networks, 2003. Proceedings of the International Joint Conference on, 2003

Under mild regularity conditions, a dynamical system can be approximated to any accuracy by a recurrent neural networks (NN) [J. T. Lo, July 1993]. This universal approximation property qualifies recurrent NNs as system identifiers in the parallel formulations. If a dynamical system under identification is affected by an uncertain environmental parameter, online adjustment of the weights of the system identifier ...


System identification of an interacting series process for real-time model predictive control

Tri Chandra S. Wibowo; Nordin Saad; Mohd Noh Karsiti 2009 American Control Conference, 2009

This paper presents the empirical modeling of the gaseous pilot plant which is a kind of interacting series process with presence of nonlinearities. In this study, the discrete-time identification approach based on subspace method with N4SID algorithm is applied to construct the state space model around a given operating point, by probing the system in open-loop with variation of input ...


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Educational Resources on System identification

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eLearning

Coupled adaptive prediction and system identification: a statistical model and transient analysis

M. Mboup; M. Bonnet; N. Bershad Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on, 1992

A significant drawback of the least mean square (LMS) algorithm is slow convergence speed when the input covariance matrix is ill-conditioned. Two structures are presented and studied for increasing the convergence speed for this case. The structures incorporate a prewhitening filter prior to the usual LMS adaptation. When the prewhitening filter is also adaptive the input to the LMS algorithm ...


Stabilization of a plant with time-varying operating conditions: an interpolated stabilizing controller

E. Muramatsu; M. Ikeda Decision and Control, 1997., Proceedings of the 36th IEEE Conference on, 1997

A stabilization problem is considered for a multi-input multi-output plant with time-varying operating conditions. The plant is described as an interpolation of two representative models defined at two representative operating points. To deal with the change of the plant dynamics, a state space description of an interpolated model, originally defined by coprime factorizations of transfer functions, is introduced. Corresponding to ...


Real-time multi-network based identification with dynamic selection implemented for a low cost UAV

Vishwas R. Puttige; Sreenatha G. Anavatti 2007 IEEE International Conference on Systems, Man and Cybernetics, 2007

This paper describes a system identification technique based on dynamic selection of multiple neural networks for the Unmanned Aerial Vehicle (UAV). The UAV is a multi- input multi-output (MIMO) nonlinear system. The neural network models are based on the autoregressive technique. The multi-network dynamic selection method allows a combination of online and offline neural network models to be used in ...


Adaptive parallel identification of dynamical systems by adaptive recurrent neural networks

J. T. Lo; D. Bassu Neural Networks, 2003. Proceedings of the International Joint Conference on, 2003

Under mild regularity conditions, a dynamical system can be approximated to any accuracy by a recurrent neural networks (NN) [J. T. Lo, July 1993]. This universal approximation property qualifies recurrent NNs as system identifiers in the parallel formulations. If a dynamical system under identification is affected by an uncertain environmental parameter, online adjustment of the weights of the system identifier ...


System identification of an interacting series process for real-time model predictive control

Tri Chandra S. Wibowo; Nordin Saad; Mohd Noh Karsiti 2009 American Control Conference, 2009

This paper presents the empirical modeling of the gaseous pilot plant which is a kind of interacting series process with presence of nonlinearities. In this study, the discrete-time identification approach based on subspace method with N4SID algorithm is applied to construct the state space model around a given operating point, by probing the system in open-loop with variation of input ...


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IEEE-USA E-Books

  • Estimation with Unknown Noise Model

    This chapter contains sections titled: Introduction Discussion of the Disturbing Noise Assumptions Properties of the ML Estimator Using a Sample Covariance Matrix Properties of the GTLS Estimator Using a Sample Covariance Matrix Properties of the BTLS Estimator Using a Sample Covariance Matrix Properties of the SUB Estimator Using a Sample Covariance Matrix Identification in the Presence of Nonlinear Distortions Illustration and Overview of the Properties Identification of Parametric Noise Models Identification in Feedback Appendixes

  • Index

    Most neural network programs for personal computers simply control a set of fixed, canned network-layer algorithms with pulldown menus. This new tutorial offers hands-on neural network experiments with a different approach. A simple matrix language lets users create their own neural networks and combine networks, and this is the only currently available software permitting combined simulation of neural networks together with other dynamic systems such as robots or physiological models. The enclosed student version of DESIRE/NEUNET differs from the full system only in the size of its data area and includes a screen editor, compiler, color graphics, help screens, and ready-to-run examples. Users can also add their own help screens and interactive menus.The book provides an introduction to neural networks and simulation, a tutorial on the software, and many complete programs including several backpropagation schemes, creeping random search, competitive learning with and without adaptive-resonance function and "conscience," counterpropagation, nonlinear Grossberg-type neurons, Hopfield-type and bidirectional associative memories, predictors, function learning, biological clocks, system identification, and more.In addition, the book introduces a simple, integrated environment for programming, displays, and report preparation. Even differential equations are entered in ordinary mathematical notation. Users need not learn C or LISP to program nonlinear neuron models. To permit truly interactive experiments, the extra-fast compilation is unnoticeable, and simulations execute faster than PC FORTRAN.The nearly 90 illustrations include block diagrams, computer programs, and simulation-output graphs.Granino A. Kom has been a Professor of Electrical Engineering at the University of Arizona and has worked in the aerospace industry for a decade. He is the author of ten other engineering texts and handbooks.

  • Model Selection and Validation

    This chapter contains sections titled: Introduction Assessing the Model Quality: Quantifying the Stochastic Errors Avoiding Overmodeling Detection of Undermodeling Model Selection Guidelines for the User Exercises Appendixes

  • References

    No abstract.

  • Basic Choices in System Identification

    This chapter contains sections titled: Introduction Intersample Assumptions: Facts The Intersample Assumption: Appreciation of the Facts Periodic Excitations: Facts Periodic Excitations: Detailed Discussion and Appreciation of the Facts Periodic versus Random Excitations: User Aspects Time and Frequency Domain Identification Time and Frequency Domain Identification: Equivalences Time and Frequency Domain Identification: Differences Conclusions Exercises Appendix

  • Subject Index

    No abstract.

  • Models of Linear Time-Invariant Systems

    This chapter contains sections titled: Introduction Plant Models Relation Between the Input-Output DFT Spectra Models for Damped (Complex) Exponentials Identifiability Multivariable Systems Noise Models Nonlinear Systems Exercises Appendixes

  • Issues in Evolutionary Optimization I

    March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms.Topics include :- Novel Areas of Evolutionary Programming and Evolution Strategies.- Evolutionary Computation with Medical Applications.- Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification.- Hierarchical Levels of Learning.- Self-Adaptation in Evolutionary Computation.- Morphogenic Evolutionary Computation.- Issues in Evolutionary Optimization.- Evolutionary Applications to VLSI and Part Placement.- Applications of Evolutionary Computation to Biology and Biochemistry Control.- Applications of Evolutionary Computation.- Genetic and Inductive Logic Programming.- Genetic Neural Networks.- The Future of Evolutionary Computation.A Bradford Book. Complex Adaptive Systems series

  • Guidelines for the User

    This chapter contains sections titled: Introduction Selection of an Identification Scheme Identification Step-by-Step Validation Conclusion Appendixes

  • Subject Index

    No abstract.



Standards related to System identification

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Jobs related to System identification

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