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, tutotials and more

  • 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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An improved dynamic framed slotted aloha algorithm for RFID anti-collision

Shu-qin Geng; Da-ming Gao; Chao Zhu; Ming He; Wu-chen Wu 2008 9th International Conference on Signal Processing, 2008

One of the largest disadvantages in radio frequency identification (RFID) system is its low tag identification efficiency by tag collision. When the number of tags is large, for the conventional RFID anti-collision algorithm the number of slots required to read the tags increases exponentially as the number of tags does. The proposed IDFSA algorithm solved this problem by dividing frequency ...


Statistical analysis of the single-layer backpropagation algorithm

N. J. Bershad; J. J. Shynk; P. L. Feintuch [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991

The authors present a statistical analysis of the steady-state and transient properties of the single-layer backpropagation algorithm for Gaussian input signals. It is based on a nonlinear system identification model of the desired response which is capable of generating an arbitrary hyperplane decision boundary. It is demonstrated that, although the weights grow unbounded, the mean-square error decreases towards zero. These ...


Measurement of Continuous-Time Linear System Parameters Via Walsh Functions

E. V. Bohn IEEE Transactions on Industrial Electronics, 1982

The parameters of a continuous-time linear system are identified by use of an integral equation representation of plant-dynamics. Walsh functions are used to express the integral functions in terms of measured periodic output data. A simple method for numerical evaluation of the integral functions using matrices is given. Emphasis is placed on reducing computational requirements and in developing compact programs ...


Dynamic modelling and control for a class of non-linear systems using neural nets

A. Abdulaziz; M. Farsi Industrial Electronics, 1993. Conference Proceedings, ISIE'93 - Budapest., IEEE International Symposium on, 1993

The paper describes a new neural network Controller using an IMC structure (NIMC). The structure is suitable for control of discrete-time SISO systems containing nonlinearities. Two design steps are assumed: (1) the controller is designed for optimal set-point tracking and disturbance rejection or model uncertainty and (2) the controller is detuned for robust performance. Comparative studies between NIMC and a ...


A two-step method for nonminimum phase ARMA identification

Z. -Z. Fu; L. D. Paarmann; M. J. Korenberg [1991] Proceedings of the 34th Midwest Symposium on Circuits and Systems, 1991

An identification method to estimate the unknown parameters of a time- invariant, possibly nonminimum-phase ARMA (autoregressive moving average) system with inaccessible input is considered as a two-step procedure. In the first step, a spectrally equivalent minimum-phase system is estimated by using an orthogonal least-squares method. In the second step, D spectrally equivalent ARMA systems are considered, where D is the ...


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Educational Resources on System identification

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eLearning

An improved dynamic framed slotted aloha algorithm for RFID anti-collision

Shu-qin Geng; Da-ming Gao; Chao Zhu; Ming He; Wu-chen Wu 2008 9th International Conference on Signal Processing, 2008

One of the largest disadvantages in radio frequency identification (RFID) system is its low tag identification efficiency by tag collision. When the number of tags is large, for the conventional RFID anti-collision algorithm the number of slots required to read the tags increases exponentially as the number of tags does. The proposed IDFSA algorithm solved this problem by dividing frequency ...


Statistical analysis of the single-layer backpropagation algorithm

N. J. Bershad; J. J. Shynk; P. L. Feintuch [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991

The authors present a statistical analysis of the steady-state and transient properties of the single-layer backpropagation algorithm for Gaussian input signals. It is based on a nonlinear system identification model of the desired response which is capable of generating an arbitrary hyperplane decision boundary. It is demonstrated that, although the weights grow unbounded, the mean-square error decreases towards zero. These ...


Measurement of Continuous-Time Linear System Parameters Via Walsh Functions

E. V. Bohn IEEE Transactions on Industrial Electronics, 1982

The parameters of a continuous-time linear system are identified by use of an integral equation representation of plant-dynamics. Walsh functions are used to express the integral functions in terms of measured periodic output data. A simple method for numerical evaluation of the integral functions using matrices is given. Emphasis is placed on reducing computational requirements and in developing compact programs ...


Dynamic modelling and control for a class of non-linear systems using neural nets

A. Abdulaziz; M. Farsi Industrial Electronics, 1993. Conference Proceedings, ISIE'93 - Budapest., IEEE International Symposium on, 1993

The paper describes a new neural network Controller using an IMC structure (NIMC). The structure is suitable for control of discrete-time SISO systems containing nonlinearities. Two design steps are assumed: (1) the controller is designed for optimal set-point tracking and disturbance rejection or model uncertainty and (2) the controller is detuned for robust performance. Comparative studies between NIMC and a ...


A two-step method for nonminimum phase ARMA identification

Z. -Z. Fu; L. D. Paarmann; M. J. Korenberg [1991] Proceedings of the 34th Midwest Symposium on Circuits and Systems, 1991

An identification method to estimate the unknown parameters of a time- invariant, possibly nonminimum-phase ARMA (autoregressive moving average) system with inaccessible input is considered as a two-step procedure. In the first step, a spectrally equivalent minimum-phase system is estimated by using an orthogonal least-squares method. In the second step, D spectrally equivalent ARMA systems are considered, where D is the ...


More eLearning Resources

IEEE-USA E-Books

  • Pattern Discovery, Pattern Recognition, and System Identification

    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

  • Self-Adaptation in Evolutionary Computation

    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

  • Measurements of Frequency Response Functions

    This chapter contains sections titled: Introduction An Introduction to the Discrete Fourier Transform Spectral Representations of Periodic Signals Analysis of FRF Measurements Using Periodic Excitations Reducing FRF Measurement Errors for Periodic Excitations FRF Measurements Using Random Excitations FRF Measurements of Multiple Input, Multiple Output Systems Guidelines for FRF Measurements Conclusion Exercises Appendixes

  • 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

  • Linear Systems: Random Processes

    This chapter contains sections titled: Introduction Classification of Systems Continuous Linear Time-Invariant Systems (Random Inputs) Continuous Time-Varying Systems with Random Input Discrete Time-Invariant Linear Systems with Random Inputs Discrete Time-Varying Linear Systems with Random Inputs Linear System Identification Derivatives of Random Processes Multi-input, Multi-output Linear Systems Transient in Linear Systems Summary This chapter contains sections titled: Problems References

  • Frequency Response Function Measurements in the Presence of Nonlinear Distortions

    This chapter contains sections titled: Introduction Intuitive Understanding of the Behavior of Nonlinear Systems A Formal Framework to Describe Nonlinear Distortions Study of the Properties of FRF Measurements in the Presence of Nonlinear Distortions Detection of Nonlinear Distortions Minimizing the Impact of Nonlinear Distortions on FRF Measurements Conclusion Exercises Appendixes

  • Novel Areas of Evolutionary Programming and Evolution Strategies

    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

  • 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

  • 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

  • Estimation with Unknown Noise Model - Standard Solutions

    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



Standards related to System identification

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