System identification

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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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


ICC 2021 - IEEE International Conference on Communications

IEEE ICC is one of the two flagship IEEE conferences in the field of communications; Montreal is to host this conference in 2021. Each annual IEEE ICC conference typically attracts approximately 1,500-2,000 attendees, and will present over 1,000 research works over its duration. As well as being an opportunity to share pioneering research ideas and developments, the conference is also an excellent networking and publicity event, giving the opportunity for businesses and clients to link together, and presenting the scope for companies to publicize themselves and their products among the leaders of communications industries from all over the world.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society

IECON is focusing on industrial and manufacturing theory and applications of electronics, controls, communications, instrumentation and computational intelligence.


2020 IEEE 16th International Workshop on Advanced Motion Control (AMC)

AMC2020 is the 16th in a series of biennial international workshops on Advanced Motion Control which aims to bring together researchers from both academia and industry and to promote omnipresent motion control technologies and applications.



Periodicals related to System identification

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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)



Most published Xplore authors for System identification

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No authors for "System identification"


Xplore Articles related to System identification

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About the Authors

System Identification: A Frequency Domain Approach, None

No abstract.


Session MP6: Blind system identification, multi-channel system inversion, and speech dereverberation

2008 42nd Asilomar Conference on Signals, Systems and Computers, 2008

None


Identification of Nonlinear Time-delay System Using Multi-dimensional Taylor Network Model

2018 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO), 2018

Combining multi-dimensional Taylor network (MTN) model with improved conjugate gradient (ICG) method, named ICG-MTN, is proposed for identification of nonlinear time-delay system in this paper. MTN is regarded as the identification model relying on its characteristic of strong approximation, and ICG method is regarded as the learning algorithm of MTN. Meanwhile, back propagation neural network (BPNN) is regarded as the ...


An algorithm for continuous-time state space identification

Proceedings of 1995 34th IEEE Conference on Decision and Control, 1995

We have developed a system identification algorithm to fit continuous-time state-space models to data. The methodology includes a continuous-time operator translation, permitting an algebraic reformulation and the use of subspace and realization algorithms. The new approach has proved effective in identification and modeling of ultrasonic echo applications.


CONVERGENCE ANALYSIS OF A SINGLE-LAYER PERCEPTRON BASED ON A SYSTEM IDENTIFICATION MODEL

1990 Conference Record Twenty-Fourth Asilomar Conference on Signals, Systems and Computers, 1990., 1990

None



Educational Resources on System identification

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IEEE-USA E-Books

  • About the Authors

    No abstract.

  • Session MP6: Blind system identification, multi-channel system inversion, and speech dereverberation

    None

  • Identification of Nonlinear Time-delay System Using Multi-dimensional Taylor Network Model

    Combining multi-dimensional Taylor network (MTN) model with improved conjugate gradient (ICG) method, named ICG-MTN, is proposed for identification of nonlinear time-delay system in this paper. MTN is regarded as the identification model relying on its characteristic of strong approximation, and ICG method is regarded as the learning algorithm of MTN. Meanwhile, back propagation neural network (BPNN) is regarded as the method of comparison. By the experimental results, the nonlinear time-delay system can be identified effectively by the proposed method, and the effectiveness is better than the BPNN.

  • An algorithm for continuous-time state space identification

    We have developed a system identification algorithm to fit continuous-time state-space models to data. The methodology includes a continuous-time operator translation, permitting an algebraic reformulation and the use of subspace and realization algorithms. The new approach has proved effective in identification and modeling of ultrasonic echo applications.

  • CONVERGENCE ANALYSIS OF A SINGLE-LAYER PERCEPTRON BASED ON A SYSTEM IDENTIFICATION MODEL

    None

  • Mean square error analysis of RLS algorithm for WSSUS fading channels

    This paper presents a mean square error (MSE) analysis of the recursive least square (RLS) algorithm for system identification over wide-sense stationary uncorrelated scattering (WSSUS) fading channels. A new sum-of-sinusoids (SOS) model for modeling fading channel is proposed. Unlike most of RLS analyses, our analysis takes the correlation of the inverse of correlation matrix into account and hence yields an improved recursive formula for the MSE, which can be computed recursively with the information of the second order statistics of fading channels. The steady state MSE of RLS is derived for Clarke's model. It is shown that the MSE analysis using the SOS model have much better agreement with experimental results than the AR model

  • A method to determine the required number of neural-network training repetitions

    Conventional neural-network training algorithms often get stuck in local minima. To find the global optimum, training is conventionally repeated with ten, or so, random starting values for the weights. Here we develop an analytical procedure to determine how many times a neural network needs to be trained, with random starting weights, to ensure that the best of those is within a desirable lower percentile of all possible trainings, with a certain level of confidence. The theoretical developments are validated by experimental results. While applied to neural-network training, the method is generally applicable to nonlinear optimization.

  • New block recursive MLP training algorithms using the Levenberg-Marquardt algorithm

    A block formulation of the Levenberg-Marquardt algorithm to train feedforward MLPs is designed to track time-varying nonlinear functions. The resulting algorithm is called the block Levenberg-Marquardt algorithm. There are two varieties of the algorithm: the overlapping and the non-overlapping block Levenberg-Marquardt. The two algorithms are developed in terms of a block presentation of the input/output training set. The tracking problem can be viewed as one of solving a sequence of nonlinear identification problems. With the persistent excitation and slowly-varying system conditions satisfied, the Levenberg-Marquardt algorithm can be shown to have a uniform rate of convergence over the entire sequence of problems. The block Levenberg- Marquardt algorithms are tested on a nonlinear time-varying function tracking problem. The algorithms show performance that is superior to the performance of existing algorithms like the global extended Kalman filter algorithm with state noise in the system equations.

  • Blind system identification using cyclostationarity and the complex cepstrum

    None

  • On an identification method for linear discrete systems

    The problem of linear discrete system parameter identification in the presence of noise is studied. An existing technique used in adaptive systems is examined and modification is made to solve the identification problem in the open-loop configuration.