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2009 10th European control Conference, ECC 2009

作者： Csercsik, David Szederkenyi, Gabor Hangos, Katalin M. Farkas, Imre Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute Budapest Hungary Department of Endocrine Neurobiology Institute of Experimental Medicine HAS Budapest Hungary

ISBN: (纸本)9783952417393

GnRH neurons are key elements of the reproductive neuroendocrine system and play important central regulating role in the dynamics of the hormonal cycle. A conductance-based Hodgkin-Huxley model structure is proposed in this paper in the form of nonlinear ordinary differential equations that is able to take into account up-to-date biological literature data related to ion channels. Measurement data were available for parameter estimation, which are originated from laboratory experiments done in the Institute of Experimental Medicine of the Hungarian Academy of Sciences in the form of whole cell patch-clamp recordings. The proposed neuron model is highly nonlinear in parameters and the evaluation of the objective function is computationally expensive, therefore the asynchronous parallel pattern search (APPS) procedure has been used for identification which is a gradient-free optimization method that can handle linear equality and inequality constraints and has advantageous convergence properties. The model with high number of estimated parameters provides a qualitatively good fit of both voltage clamp and current clamp traces. © 2009 EUCA.

关键词： Parameter estimation

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Dynamical Modeling and Identification of a GnRH neuron *

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IFAC Proceedings Volumes 2009年第12期42卷 425-430页

作者： Dávid Csercsik Gábor Szederkényi Katalin M. Hangos Imre Farkas Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute HAS Budapest Hungary Department of Endocrine Neurobiology Institute of Experimental Medicine HAS Budapest Hungary

GnRH neurons, as key elements of the reproductive neuroendocrine system, have important central regulating role in the dynamics of the hormonal cycle. A Hodgkin-Huxley type neural model is proposed in this paper, that takes into account up-to-date biological literature data related to ion channels. The proposed neuron model is highly nonlinear in parameters and the evaluation of the objective function is computationally expensive, therefore the asynchronous parallel pattern search (APPS) procedure has been used for identification. The model with high number of estimated parameters provides a qualitatively good fit of both voltage clamp and current clamp traces.

关键词： Neural dynamics Dynamic modelling Identification Nonlinear systems

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Modeling and identification of a Nuclear reactor with temperature dependent reactivity

Modeling and identification of a Nuclear reactor with temper...

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European control Conference (ECC)

作者： Csaba Fazekas Attila Gábor Katalin M. Hangos Process Control Research Group Computer and Automation Research Institute Budapest Hungary

A simple dynamic model of a nuclear reactor with temperature dependent reactivity and six groups of neutron emitting nuclei is developed in this paper based on first engineering principles. The proposed model contains the possible minimal number of differential equations and it is capable of describing important dynamic phenomena such as load change transients between day and night periods. Because the model is nonlinear in its parameters, an optimization-based parameter estimation procedure has been applied by using the Nelder-Mead algorithm and measured data from the Paks Nuclear Power Plant. The estimated parameter values fall into physically meaningful ranges and the response of the constructed reactor model shows a good fit to the measurement data.

关键词： Neutrons Mathematical model Inductors Temperature measurement Load modeling Fuels Integrated circuit modeling

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The Effect of Tuning in Multiple-Model Adaptive controllers: A Case Study

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IFAC Proceedings Volumes 2009年第11期42卷 560-565页

作者： Ehsan Peymani Alireza Fatehi Ali Khaki Sedigh Advanced Process Automation & Control (APAC) Research Group K. N. Toosi University of Technology Tehran Iran

In this paper, two types of multiple-model adaptive controllers are practically evaluated on a laboratory-scale pH neutralization process. The first one is supervisory switching multiple-model adaptive controller (SMMAC) whose model bank is fixed and selected a priori, and another one is a controller based on multiple models, switching, and tuning strategy (MMST) which uses the possibility of model bank tuning. In addition to investigation of the effect of tuning, the advantage of a disturbance rejection supervisor is studied. Various experiments and exhaustive numerical analyses are provided to assess the abilities of the proposed algorithms.

关键词： multiple models adaptive control pH control switching control pole-placement control

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Modeling and identification of a nuclear reactor with temperature effects and Xenon poisoning

Modeling and identification of a nuclear reactor with temper...

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Annual Conference of Industrial Electronics Society

作者： Attila Gábor Csaba Fazekas Gábor Szederkényi Katalin M. Hangos Process Control Research Group Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

This paper presents the modeling and identification procedure for a VVER-type pressurized water reactor. The modeling goal is to produce a mathematical description in nonlinear state-space form that is suitable for control-oriented model analysis and preliminary controller design experiments. The proposed model takes temperature effects and xenon poisoning into consideration and thus it is an extension of formerly published simpler model structures. Real transient measurement data from the plant has been used for the identification that is based on standard prediction error minimization. It is shown that the model is fairly well identifiable and the newly inserted model components significantly improve the quality of fit between the measured and computed model outputs. Furthermore, the estimated parameter values fall into physically meaningful ranges.

关键词： Temperature Xenon Inductors Neutrons Parameter estimation Power system modeling Steady-state Mathematical model Power generation Delay

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Enhancement of Multi-Objective control Performance via Switching

Enhancement of Multi-Objective Control Performance via Switc...

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2009中国控制与决策会议

作者： Laven Soltanian Ali Khaki Sedigh Omid Namaki-Shoushtari Advanced Process Automation & Control(APAC) Research Group K.N.Toosi University of TechnologyTehranIran Control Engineering Department K.N.Toosi University of TechnologyTehranIran

control system design for a complex system encompasses the optimization of different and often conflicting objectives leading to a multiple objective design *** this paper,a switching strategy is proposed to solve the multiple objective controller *** controller is designed based on a set of performance *** realization considerations are used to ensure overall closed loop *** matrix inequalities are employed in the controller design *** objective designs are always prone to over conservatism,which is greatly reduced by the switching *** results are used to show the effectiveness of the design methodology.

关键词： multi-objective control switching stability state space realization linear matrix inequalities(LMI)

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Bioluminescence tomography based on Bayesian approach

Bioluminescence tomography based on Bayesian approach

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Feng, Jinchao Jia, Kebin Tian, Jie Yan, Guorui Qin, Chenghu The College of Electronic Information and Control Engineering Beijing University of Technology Beijing 100022 China Medical Image Processing Group Key Laboratory of Complex Systems and Intelligence Science Institute of Automation P. O. Box 2728 Beijing 100190 China Life Science Research Center Xidian University Xi'an 710071 China

ISBN: (纸本)9780819475138

As a new mode of molecular imaging, bioluminescence tomography (BLT) will have *** *** on revealing the molecular and cellular information in vivo at the whole-body small animal level because of its high sensitive detection and facile operation. However, BLT is an ill-posed problem, it is necessary to incorporate a priori knowledge into the tomographic algorithm. In this paper, a novel Bayesian reconstruction algorithm for BLT is firstly proposed. In the algorithm, a priori permissible source region strategy is incorporated into the Bayesian network to reduce the ill-posedness of BLT. Then a generalized adaptive Gaussian Markov random field (GAGMRF) prior model for unknown source density estimation is developed to further reduce the ill-posedness of BLT on the basis of adaptive finite element analysis. Finally, the algorithm maximizes the log posterior probability with respect to a noise parameter and the unknown source density, the distribution of bioluminescent source can be reconstructed. In addition, the novel tomography algorithm based adaptive finite element makes the method more appropriate for complex phantom such as real mouse. In the numerical simulation, a heterogeneous phantom is used to evaluate the performance of the proposed algorithm with the Monte Carlo based synthetic data. The accurate localization of bioluminescent source and quantitative results show the effectiveness and potential of the tomographic algorithm for BLT. © 2009 SPIE.

关键词： Monte Carlo methods

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Three-dimensional localization of in vivo bioluminescent source based on multispectral imaging

Three-dimensional localization of in vivo bioluminescent sou...

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Feng, Jinchao Jia, Kebin Tian, Jie Yan, Guorui Zhu, Shouping The College of Electronic Information and Control Engineering Beijing University of Technology Beijing 100022 China Medical Image Processing Group Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences P.O. Box 2728 Beijing 100190 China Life Science Research Center Xidian University Xi'an 710071 China

ISBN: (纸本)9780819475138

Bioluminescence tomography (BLT) is a novel in vivo technique in small animal studies, which can reveal the molecular and cellular information at the whole-body small animal level. At present, there is an increasing interest in multispectral bioluminescence tomography, since multispectral data acquisition could improve the BLT performance significantly. In view to the ill-posedness of BLT problem, we develop an optimal permissible source region strategy to constrain the possible solution of the source by utilizing spectrum character of bioluminescent source. Then a linear system to link the measured data with the unknown light source variables is established by utilizing the optimal permissible region strategy based on adaptive finite element analysis. Furthermore, singular value decomposition analysis is used for data dimensionality reduction and improving computational efficiency in multispectral case. The reconstructed speed and stability benefit from adaptive finite element, the permissible region strategy and singular value decomposition. In the numerical simulation, the heterogeneous phantom experiment has been used to evaluate the performance of the proposed algorithm with the Monte Carlo based synthetic data. The reconstruction results demonstrate the merits and potential of our methodology for localizing bioluminescent source. © 2009 SPIE.

关键词： Singular value decomposition

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Identification, Prediction and Detection of the process Fault in a Cement Rotary Kiln by Locally Linear Neuro-Fuzzy Technique

Identification, Prediction and Detection of the Process Faul...

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International Conference on Computer and Electrical Engineering, ICCEE

作者： Masoud Sadeghian Alireza Fatehi Department of Mechatronics Engineering Sharif University of Technology International Campus Kish Island Iran Advanced Process Automation and Control (APAC) Research group Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran

In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, by using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes prediction horizon are presented. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used for in this study.

关键词： Fault diagnosis Fault detection Kilns Production facilities Electrical fault detection Fuzzy systems Delay estimation Nonlinear systems Predictive models automation

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Identification of Nonlinear Predictor and Simulator Models of a Cement Rotary Kiln by Locally Linear Neuro-Fuzzy Technique

Identification of Nonlinear Predictor and Simulator Models o...

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International Conference on Computer and Electrical Engineering, ICCEE

作者： Masoud Sadeghian Alireza Fatehi Department of Mechatronics Engineering International Campus Sharif University of Technology Kish Island Iran Advanced Process Automation and Control (APAC) Research group Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran

One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only in exceptional case; however, a large number of the involved parameters were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement rotary kiln by using nonlinear identification technique on the Locally Linear Neuro-Fuzzy (LLNF) model. For the first time, a simulator model as well as a predictor one with a precise fifteen minute prediction horizon for a cement rotary kiln is presented. These models are trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. At the end, the characteristics of these models are expressed. Furthermore, we presented the pros and cons of these models. The data collected from White Saveh Cement Company is used for modeling.

关键词： Predictive models Kilns Computational modeling Power system modeling Production facilities Nonlinear control systems Chemical technology Nonlinear equations Automatic control process control

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