System identification
9,884 resources related to System identification
IEEE Organizations related to System identification
Back to TopConferences related to System identification
Back to Top2017 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.
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 microsystems.
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, HumanRobot 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.
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Periodicals related to System identification
Back to TopInstrumentation 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 audiofrequency 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.
Xplore Articles related to System identification
Back to TopAn improved dynamic framed slotted aloha algorithm for RFID anticollision
Shuqin Geng; Daming Gao; Chao Zhu; Ming He; Wuchen 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 anticollision 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 singlelayer 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 steadystate and transient properties of the singlelayer 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 meansquare error decreases towards zero. These ...
Measurement of ContinuousTime Linear System Parameters Via Walsh Functions
E. V. Bohn IEEE Transactions on Industrial Electronics, 1982
The parameters of a continuoustime linear system are identified by use of an integral equation representation of plantdynamics. 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 nonlinear 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 discretetime SISO systems containing nonlinearities. Two design steps are assumed: (1) the controller is designed for optimal setpoint tracking and disturbance rejection or model uncertainty and (2) the controller is detuned for robust performance. Comparative studies between NIMC and a ...
A twostep 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 nonminimumphase ARMA (autoregressive moving average) system with inaccessible input is considered as a twostep procedure. In the first step, a spectrally equivalent minimumphase system is estimated by using an orthogonal leastsquares method. In the second step, D spectrally equivalent ARMA systems are considered, where D is the ...
More Xplore Articles
Educational Resources on System identification
Back to TopeLearning
An improved dynamic framed slotted aloha algorithm for RFID anticollision
Shuqin Geng; Daming Gao; Chao Zhu; Ming He; Wuchen 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 anticollision 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 singlelayer 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 steadystate and transient properties of the singlelayer 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 meansquare error decreases towards zero. These ...
Measurement of ContinuousTime Linear System Parameters Via Walsh Functions
E. V. Bohn IEEE Transactions on Industrial Electronics, 1982
The parameters of a continuoustime linear system are identified by use of an integral equation representation of plantdynamics. 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 nonlinear 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 discretetime SISO systems containing nonlinearities. Two design steps are assumed: (1) the controller is designed for optimal setpoint tracking and disturbance rejection or model uncertainty and (2) the controller is detuned for robust performance. Comparative studies between NIMC and a ...
A twostep 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 nonminimumphase ARMA (autoregressive moving average) system with inaccessible input is considered as a twostep procedure. In the first step, a spectrally equivalent minimumphase system is estimated by using an orthogonal leastsquares method. In the second step, D spectrally equivalent ARMA systems are considered, where D is the ...
More eLearning Resources
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IEEEUSA EBooks

Pattern Discovery, Pattern Recognition, and System Identification
March 13, 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. SelfAdaptation 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

SelfAdaptation in Evolutionary Computation
March 13, 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. SelfAdaptation 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 TimeInvariant Systems (Random Inputs) Continuous TimeVarying Systems with Random Input Discrete TimeInvariant Linear Systems with Random Inputs Discrete TimeVarying Linear Systems with Random Inputs Linear System Identification Derivatives of Random Processes Multiinput, Multioutput 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 13, 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. SelfAdaptation 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 13, 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. SelfAdaptation 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
Back to TopNo standards are currently tagged "System identification"