System identification
9,753 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 TopA recursive algorithm for simultaneous identification of model order and parameters
Shaohua Niu; Deyun Xiao; D. G. Fisher IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
A recursive identification algorithm for SISO CARMA systems is presented based on an augmented information matrix (AIM). Decomposition of the AIM using UDU factorization provides simultaneous, recursive estimates of both the system parameters and the loss functions from order 0 to n, where n is the maximum possible order of the real process and U and D are upper and ...
Twodimensional block adaptive filtering algorithms
W. B. Mikhael; S. M. Ghosh Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on, 1992
A technique for 2D system identification which processes 2D signals using 2D blocks is proposed. Two algorithms which perform 2D FIR (finite impulse response) adaptive filtering using 2D error blocks or windows are presented. The first algorithm uses a convergence factor that is constant for each 2D coefficient at each window iteration. This algorithm is termed the two dimensional block ...
Y. Zhou; S. C. Chan; K. L. Ho IEEE Transactions on Industrial Electronics, 2011
The sequential partialupdate least mean square (SLMS)based algorithms are efficient methods for reducing the arithmetic complexity in adaptive system identification and other industrial informatics applications. They are also attractive in acoustic applications where long impulse responses are encountered. A limitation of these algorithms is their degraded performances in an impulsive noise environment. This paper proposes new robust counterparts for the ...
Parameter estimation using biologically inspired methods
Weixing Lin; Rong Liu; Peter X. Liu; Max. Q. H. Meng Robotics and Biomimetics, 2007. ROBIO 2007. IEEE International Conference on, 2007
Identification of nonlinear systems has drawn much attention in recent years. This paper presents a new identification algorithm for Hammerstein models based on bacterial foraging. In specific, the biomimicry of the bacterial chemotaxis algorithm is used to identify model parameters. A flowchart of this identification algorithm is given. Simulation and Comparison studies show that the proposed bacterial foraging based approach ...
Adaptive filter based on TDBLMS algorithm for image noise cancellation
ChuenYau Chen; ChihWen Hsia Green Circuits and Systems (ICGCS), 2010 International Conference on, 2010
An adaptive filter for twodimensional block processing in image noise cancellation is proposed in this paper. The processing includes two phases. They are the weighttraining phase and the blockadaptation phase. The weight training phase obtains the suitable weight matrix to be the initial one for the blockadaptation phase such that a higher signaltonoise ratio can be achieved. To verify the ...
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Educational Resources on System identification
Back to TopeLearning
A recursive algorithm for simultaneous identification of model order and parameters
Shaohua Niu; Deyun Xiao; D. G. Fisher IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
A recursive identification algorithm for SISO CARMA systems is presented based on an augmented information matrix (AIM). Decomposition of the AIM using UDU factorization provides simultaneous, recursive estimates of both the system parameters and the loss functions from order 0 to n, where n is the maximum possible order of the real process and U and D are upper and ...
Twodimensional block adaptive filtering algorithms
W. B. Mikhael; S. M. Ghosh Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on, 1992
A technique for 2D system identification which processes 2D signals using 2D blocks is proposed. Two algorithms which perform 2D FIR (finite impulse response) adaptive filtering using 2D error blocks or windows are presented. The first algorithm uses a convergence factor that is constant for each 2D coefficient at each window iteration. This algorithm is termed the two dimensional block ...
Y. Zhou; S. C. Chan; K. L. Ho IEEE Transactions on Industrial Electronics, 2011
The sequential partialupdate least mean square (SLMS)based algorithms are efficient methods for reducing the arithmetic complexity in adaptive system identification and other industrial informatics applications. They are also attractive in acoustic applications where long impulse responses are encountered. A limitation of these algorithms is their degraded performances in an impulsive noise environment. This paper proposes new robust counterparts for the ...
Parameter estimation using biologically inspired methods
Weixing Lin; Rong Liu; Peter X. Liu; Max. Q. H. Meng Robotics and Biomimetics, 2007. ROBIO 2007. IEEE International Conference on, 2007
Identification of nonlinear systems has drawn much attention in recent years. This paper presents a new identification algorithm for Hammerstein models based on bacterial foraging. In specific, the biomimicry of the bacterial chemotaxis algorithm is used to identify model parameters. A flowchart of this identification algorithm is given. Simulation and Comparison studies show that the proposed bacterial foraging based approach ...
Adaptive filter based on TDBLMS algorithm for image noise cancellation
ChuenYau Chen; ChihWen Hsia Green Circuits and Systems (ICGCS), 2010 International Conference on, 2010
An adaptive filter for twodimensional block processing in image noise cancellation is proposed in this paper. The processing includes two phases. They are the weighttraining phase and the blockadaptation phase. The weight training phase obtains the suitable weight matrix to be the initial one for the blockadaptation phase such that a higher signaltonoise ratio can be achieved. To verify the ...
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IEEEUSA EBooks

Properties of Least Squares Estimators with Stochastic Weighting
This chapter contains sections titled: Introduction  Notational Conventions Strong Convergence Strong Consistency Convergence Rate Asymptotic Bias Asymptotic Normality Overview of the Asymptotic Properties Exercises Appendixes

Identification of Semilinear Models
This chapter contains sections titled: The Semilinear Model The Markov Estimator CramérRao Lower Bound Properties of the Markov Estimator Residuals of the Model Equation Mean and Variance of the Cost Function Model Selection and Model Validation Exercises Appendixes

The technique of local linear models is appealing for modeling complex time series due to the weak assumptions required and its intrinsic simplicity. Here, instead of deriving the local models from the data, we propose to estimate them directly from the weights of a self organizing map (SOM), which functions as a dynamicpreserving model of the dynamics. We introduce one modification to the Kohonen learning to ensure good representation of the dynamics and use weighted least squares to ensure continuity among the local models. The proposed scheme is tested using synthetic chaotic time series and real world data. The practicality of the method is illustrated in the identification and control of the NASA Langley wind tunnel during aerodynamic tests of model aircrafts. Modeling the dynamics with a SOM leads to a predictive multiple model control strategy (PMMC). In test runs, a comparison of the new controller against the existing controller shows the superiority of our method.

Identification of Invariants of (Over)Parameterized Models
This chapter contains sections titled: Introduction (Over)Parameterized Models and their Invariants CramérRao Lower Bound for Invariants of (Over)Parameterized Models Estimates of Invariants of (Over) Parameterized Models  Finite Sample Results A Simple Numerical Example Exercises Appendixes

Issues in Evolutionary Optimization II
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

We present in this paper a coherent framework, based on the use of timelagged recurrent neural networks, for solving a variety of difficult signal processing problems. The framework relies on the assertion that timelagged recurrent networks possess the necessary representational capabilities to act as universal approximators of nonlinear dynamical systems. This property applies to modeling problems posed as system identification, timeseries prediction, nonlinear filtering, adaptive filtering, or temporal pattern classification. We address the development of models of nonlinear dynamical systems, in the form of timelagged recurrent neural networks, which can be used without further training (i.e., as fixedweight networks). We concentrate here on the recurrent multilayer percepiron (RMLP) architecture, which generalizes the standard MLP with the possibility of singledelay connections within each layer. We have found that a weight update procedure based on the extended Kalman filter (EKF) is far more effective, for both feedforward and recurrent networks, than simple firstorder gradient methods. As a solution to the recency effect, the tendency for a network to forget earlier learning as it processes new examples, we have developed a technique called multistream training. We demonstrate our training framework by applying it to four problems. First, we show that a single timelagged recurrent neural network can be trained not only to produce excellent onetimestep predictions for two different time series, but also to be robust to severe errors in the input sequence with which it is provided. The second problem involves the modeling of a complex system containing significant process noise, which was shown in [1] to lead to unstable trained models. We illustrate how multistream training may be used to enhance the stability of such models. The rem aining two problems are drawn from realworld automotive applications. The first of these involves inputoutput modeling of a signal that reflects the dynamic behavior of a catalytic converter in the exhaust stream. Finally we consider realtime and continuous detection of engine misfire. This is cast as a signal processing problem that requires a binary decision be made at each time step on the basis of the sequence of available input variables.

This chapter contains sections titled: Introduction Selection of an Identification Scheme Identification StepbyStep Validation Conclusion Appendixes

This chapter contains sections titled: The ARMAX Model and Variations Uniqueness Properties Model Identifiability Prediction Error Methods Instrumental Variable Methods Recursive Least Squares Algorithm Model Validation Summary References Recommended Exercises

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

This chapter contains sections titled: Signals Systems and Models System Modeling System Identification How Common are Nonlinear Systems?
Standards related to System identification
Back to TopNo standards are currently tagged "System identification"