System identification

View this topic in
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

Back to Top

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.


More Conferences

Periodicals related to System identification

Back to Top

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

Back to Top

Xplore Articles related to System identification

Back to Top

MA parameter estimation and cumulant enhancement

A. G. Stogioglou; S. McLaughlin IEEE Transactions on Signal Processing, 1996

This paper addresses the problem of estimating the parameters of a moving average (MA) model from either only third- or fourth-order cumulants of the noisy observations of the system output. The system is driven by an independent and identically distributed non-Gaussian sequence that is not observed. The unknown model parameters are obtained using a batch least squares method. Recursive methods ...


Data prediction based on qualitative information by means of the fuzzy method

Zaijun Hu Proceedings of 1995 IEEE International Conference on Fuzzy Systems., 1995

In this paper an approach to use qualitative information to infer unknown data by means of the fuzzy method is presented. The qualitative information are classified into tendency, relation, modification and quantified information which will be represented by the fuzzy set, fuzzy operation and fuzzy relation. Different fuzzy inference rules are discussed and the corresponding fuzzy relation equations are established ...


Bank of adaptive filters for fault detection in multiple regime process using unsupervised training

Dubravko Miljković The 33rd International Convention MIPRO, 2010

Change detection is kind of adaptive filtering for non-stationary signals, and is the tool in fault detection and diagnosis. In this paper bank of adaptive filter is used for fault detection in multiple regime process. Bank of filters is trained by proposed unsupervised method suitable for periodic signals like vibrations from rotational machinery.


Application of blind second order statistics MIMO identification methods to the blind CDMA forward link channel estimation

Ph. Loubaton; E. Moulines 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999

Blind channel estimation for periodic sequence DS-CDMA systems can be cast into the framework of "structured" blind estimation of multi-input/multi- output (MIMO) FIR systems, where the structure is imposed by the user's signatures. A possible approach to tackle this problem consists in looking for a structured solution to one of the so-called "blind" MIMO-FIR system identification techniques proposed previously. This ...


Exact convergence analysis of LMS algorithm for tapped-delay i.i.d. input with large step-size

Gu Yuantao; Tang Kun; Cui Huijuan; Du Wen TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, 2002

The celebrated least mean square (LMS) algorithm is the widely used system identification approach which can be easily implemented. With the assumption of no dependence among the tapped-delay input vectors, the mean square analysis of LMS algorithm based on independence theory is only an approximate description of its convergence behavior, especially when updated with a large step-size. In this paper, ...


More Xplore Articles

Educational Resources on System identification

Back to Top

eLearning

MA parameter estimation and cumulant enhancement

A. G. Stogioglou; S. McLaughlin IEEE Transactions on Signal Processing, 1996

This paper addresses the problem of estimating the parameters of a moving average (MA) model from either only third- or fourth-order cumulants of the noisy observations of the system output. The system is driven by an independent and identically distributed non-Gaussian sequence that is not observed. The unknown model parameters are obtained using a batch least squares method. Recursive methods ...


Data prediction based on qualitative information by means of the fuzzy method

Zaijun Hu Proceedings of 1995 IEEE International Conference on Fuzzy Systems., 1995

In this paper an approach to use qualitative information to infer unknown data by means of the fuzzy method is presented. The qualitative information are classified into tendency, relation, modification and quantified information which will be represented by the fuzzy set, fuzzy operation and fuzzy relation. Different fuzzy inference rules are discussed and the corresponding fuzzy relation equations are established ...


Bank of adaptive filters for fault detection in multiple regime process using unsupervised training

Dubravko Miljković The 33rd International Convention MIPRO, 2010

Change detection is kind of adaptive filtering for non-stationary signals, and is the tool in fault detection and diagnosis. In this paper bank of adaptive filter is used for fault detection in multiple regime process. Bank of filters is trained by proposed unsupervised method suitable for periodic signals like vibrations from rotational machinery.


Application of blind second order statistics MIMO identification methods to the blind CDMA forward link channel estimation

Ph. Loubaton; E. Moulines 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999

Blind channel estimation for periodic sequence DS-CDMA systems can be cast into the framework of "structured" blind estimation of multi-input/multi- output (MIMO) FIR systems, where the structure is imposed by the user's signatures. A possible approach to tackle this problem consists in looking for a structured solution to one of the so-called "blind" MIMO-FIR system identification techniques proposed previously. This ...


Exact convergence analysis of LMS algorithm for tapped-delay i.i.d. input with large step-size

Gu Yuantao; Tang Kun; Cui Huijuan; Du Wen TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, 2002

The celebrated least mean square (LMS) algorithm is the widely used system identification approach which can be easily implemented. With the assumption of no dependence among the tapped-delay input vectors, the mean square analysis of LMS algorithm based on independence theory is only an approximate description of its convergence behavior, especially when updated with a large step-size. In this paper, ...


More eLearning Resources

IEEE-USA E-Books

  • A Signal Processing Framework Based on Dynamic Neural Networks with Application to Problems in Adaptation, Filtering and Classification

    We present in this paper a coherent framework, based on the use of time-lagged recurrent neural networks, for solving a variety of difficult signal processing problems. The framework relies on the assertion that time-lagged 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, time-series prediction, nonlinear filtering, adaptive filtering, or temporal pattern classification. We address the development of models of nonlinear dynamical systems, in the form of time-lagged recurrent neural networks, which can be used without further training (i.e., as fixed-weight networks). We concentrate here on the recurrent multilayer percepiron (RMLP) architecture, which generalizes the standard MLP with the possibility of single-delay 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 first-order 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 multi-stream training. We demonstrate our training framework by applying it to four problems. First, we show that a single time-lagged recurrent neural network can be trained not only to produce excellent one-time-step 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 multi-stream training may be used to enhance the stability of such models. The rem aining two problems are drawn from real-world automotive applications. The first of these involves input-output modeling of a signal that reflects the dynamic behavior of a catalytic converter in the exhaust stream. Finally we consider real-time 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.

  • Reference Index

    No abstract.

  • Properties of Least Squares Estimators with Deterministic Weighting

    This chapter contains sections titled: Introduction Strong Convergence Strong Consistency Convergence Rate Asymptotic Bias Asymptotic Normality Asymptotic Efficiency Overview of the Asymptotic Properties Exercises Appendixes

  • Model Construction

    The systematic design of feedback systems requires an ability to quantify the effect of control inputs (e.g., buffer size) on measured outputs (e.g., response times), both of which may vary with time. Indeed, developing such models is at the heart of applying control theory in practice. In this chapter we introduce linear difference equations to model the dynamics of computing systems and employ insights from queueing theory to construct such models. We discuss briefly how difference equations can be constructed from first principles. Our focus, however, is to construct models using statistical or black-box methods, a process that is referred to as system identification.

  • 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

  • No title

    Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity ($O(N)$) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity ($O(N^2)$) makes them unsuitable for many real-time applications. A well-known approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vec or instead of the entire vector or by updating part of the time. In the literature, there are only a few analyses of these partial update adaptive filter algorithms. Most analyses are based on partial update LMS and its variants. Only a few papers have addressed partial update RLS and Affine Projection (AP). Therefore, analyses for PU least- squares adaptive filter algorithms are necessary and meaningful. This monograph mostly focuses on the analyses of the partial update least-squares adaptive filter algorithms. Basic partial update methods are applied to adaptive filter algorithms including Least Squares CMA (LSCMA), EDS, and CG. The PU methods are also applied to CMA1-2 and NCMA to compare with the performance of the LSCMA. Mathematical derivation and performance analysis are provided including convergence condition, steady-state mean and mean-square performance for a time-invariant system. The steady-state mean and mean-square performance are also presented for a time-varying syste . Computational complexity is calculated for each adaptive filter algorithm. Numerical examples are shown to compare the computational complexity of the PU adaptive filters with the full-update filters. Computer simulation examples, including system identification and channel equalization, are used to demonstrate the mathematical analysis and show the performance of PU adaptive filter algorithms. They also show the convergence performance of PU adaptive filters. The performance is compared between the original adaptive filter algorithms and different partial-update methods. The performance is also compared among similar PU least-squares adaptive filter algorithms, such as PU RLS, PU CG, and PU EDS. In addition to the generic applications of system identification and channel equalization, two special applications of using partial update adaptive filters are also presented. One application uses PU adaptive filters to detect Global System for Mobile Communication (GSM) signals in a local GSM system using the Open Base Transceiver Station (OpenBTS) and Asterisk Private Branch Exchange (PBX). The other application uses PU adaptive filters to do image compression in a system combining hyperspectral image compression and classification.

  • 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

  • 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

  • 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

  • Neural Network Expert Systems

    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.



Standards related to System identification

Back to Top

No standards are currently tagged "System identification"


Jobs related to System identification

Back to Top