Identification and Modelling of Linear Dynamic Systems
Directory of Open Access Journals (Sweden)
Stanislav Kocur
2006-01-01
Full Text Available System identification and modelling are very important parts of system control theory. System control is only as good as good is created model of system. So this article deals with identification and modelling problems. There are simple classification and evolution of identification methods, and then the modelling problem is described. Rest of paper is devoted to two most known and used models of linear dynamic systems.
CONTROL SYSTEM IDENTIFICATION THROUGH MODEL MODULATION METHODS
identification has been achieved by using model modulation techniques to drive dynamic models into correspondence with operating control systems. The system ... identification then proceeded from examination of the model and the adaptive loop. The model modulation techniques applied to adaptive control
Model Updating Nonlinear System Identification Toolbox Project
National Aeronautics and Space Administration — ZONA Technology (ZONA) proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology that utilizes flight data with...
Nonlinear System Identification and Behavioral Modeling
Huq, Kazi Mohammed Saidul; Kabir, A F M Sultanul
2010-01-01
The problem of determining a mathematical model for an unknown system by observing its input-output data pair is generally referred to as system identification. A behavioral model reproduces the required behavior of the original analyzed system, such as there is a one-to-one correspondence between the behavior of the original system and the simulated system. This paper presents nonlinear system identification and behavioral modeling using a work assignment.
Modeling and Identification of Multirate Systems
Institute of Scientific and Technical Information of China (English)
Feng DING; Tongwen CHEN
2005-01-01
Multirate systems are abundant in industry; for example, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. The study of multirate systems goes back to the early 1950's, and has become an active research area in systems and control. This paper briefly surveys the history of development in the area of multirate systems, and introduces some basic concepts and latest results on multirate systems, including a polynomial transformation technique and the lifting technique as tools for handling multirate systems, lifted state space models, parameter identification of dual-rate systems, how to determine fast single-rate models from dual-rate models and directly from dual-rate data, and a hierarchical identification method for general multirate systems. Finally, some further research topics for multirate systems are given.
Model Updating Nonlinear System Identification Toolbox Project
National Aeronautics and Space Administration — ZONA Technology proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology by adopting the flight data with state-of-the-art...
System identification application using Hammerstein model
Indian Academy of Sciences (India)
SABAN OZER; HASAN ZORLU; SELCUK METE
2016-06-01
Generally, memoryless polynomial nonlinear model for nonlinear part and finite impulse response (FIR) model or infinite impulse response model for linear part are preferred in Hammerstein models in literature. In this paper, system identification applications of Hammerstein model that is cascade of nonlinear second order volterra and linear FIR model are studied. Recursive least square algorithm is used to identify the proposed Hammerstein model parameters. Furthermore, the results are compared to identify the success of proposed Hammerstein model and different types of models
Structural system identification: Structural dynamics model validation
Energy Technology Data Exchange (ETDEWEB)
Red-Horse, J.R.
1997-04-01
Structural system identification is concerned with the development of systematic procedures and tools for developing predictive analytical models based on a physical structure`s dynamic response characteristics. It is a multidisciplinary process that involves the ability (1) to define high fidelity physics-based analysis models, (2) to acquire accurate test-derived information for physical specimens using diagnostic experiments, (3) to validate the numerical simulation model by reconciling differences that inevitably exist between the analysis model and the experimental data, and (4) to quantify uncertainties in the final system models and subsequent numerical simulations. The goal of this project was to develop structural system identification techniques and software suitable for both research and production applications in code and model validation.
Analysis of modeling errors in system identification
Hadaegh, F. Y.; Bekey, G. A.
1986-01-01
This paper is concerned with the identification of a system in the presence of several error sources. Following some basic definitions, the notion of 'near-equivalence in probability' is introduced using the concept of near-equivalence between a model and process. Necessary and sufficient conditions for the identifiability of system parameters are given. The effect of structural error on the parameter estimates for both deterministic and stochastic cases are considered.
Model Identification and Validation for a Heating System using MATLAB System Identification Toolbox
Junaid Rabbani, Muhammad; Hussain, Kashan; khan, Asim-ur-Rehman; Ali, Abdullah
2013-12-01
This paper proposed a systematic approach to select a mathematical model for an industrial heating system by adopting system identification techniques with the aim of fulfilling the design requirement for the controller. The model identification process will begin by collecting real measurement data samples with the aid of MATLAB system identification toolbox. The criteria for selecting the model that could validate model output with actual data will based upon: parametric identification technique, picking the best model structure with low order among ARX, ARMAX and BJ, and then applying model estimation and validation tests. Simulated results have shown that the BJ model has been best in providing good estimation and validation based upon performance criteria such as: final prediction error, loss function, best percentage of model fit, and co-relation analysis of residual for output.
Keesman, K.J.
2011-01-01
Summary System Identification Introduction.- Part I: Data-based Identification.- System Response Methods.- Frequency Response Methods.- Correlation Methods.- Part II: Time-invariant Systems Identification.- Static Systems Identification.- Dynamic Systems Identification.- Part III: Time-varying
System Identification, Environmental Modelling, and Control System Design
Garnier, Hugues
2012-01-01
System Identification, Environmetric Modelling, and Control Systems Design is dedicated to Professor Peter Young on the occasion of his seventieth birthday. Professor Young has been a pioneer in systems and control, and over the past 45 years he has influenced many developments in this field. This volume is comprised of a collection of contributions by leading experts in system identification, time-series analysis, environmetric modelling and control system design – modern research in topics that reflect important areas of interest in Professor Young’s research career. Recent theoretical developments in and relevant applications of these areas are explored treating the various subjects broadly and in depth. The authoritative and up-to-date research presented here will be of interest to academic researcher in control and disciplines related to environmental research, particularly those to with water systems. The tutorial style in which many of the contributions are composed also makes the book suitable as ...
System Identification of a Vortex Lattice Aerodynamic Model
Juang, Jer-Nan; Kholodar, Denis; Dowell, Earl H.
2001-01-01
The state-space presentation of an aerodynamic vortex model is considered from a classical and system identification perspective. Using an aerodynamic vortex model as a numerical simulator of a wing tunnel experiment, both full state and limited state data or measurements are considered. Two possible approaches for system identification are presented and modal controllability and observability are also considered. The theory then is applied to the system identification of a flow over an aerodynamic delta wing and typical results are presented.
Practical Modeling and Comprehensive System Identification of a BLDC Motor
Directory of Open Access Journals (Sweden)
Changle Xiang
2015-01-01
Full Text Available The aim of this paper is to outline all the steps in a rigorous and simple procedure for system identification of BLDC motor. A practical mathematical model for identification is derived. Frequency domain identification techniques and time domain estimation method are combined to obtain the unknown parameters. The methods in time domain are founded on the least squares approximation method and a disturbance observer. Only the availability of experimental data for rotor speed and armature current are required for identification. The proposed identification method is systematically investigated, and the final identified model is validated by experimental results performed on a typical BLDC motor in UAV.
Biologically-motivated system identification: application to microbial growth modeling.
Yan, Jinyao; Deller, J R
2014-01-01
This paper presents a new method for identification of system models that are linear in parametric structure, but arbitrarily nonlinear in signal operations. The strategy blends traditional system identification methods with three modeling strategies that are not commonly employed in signal processing: linear-time-invariant-in-parameters models, set-based parameter identification, and evolutionary selection of the model structure. This paper reports recent advances in the theoretical foundation of the methods, then focuses on the operation and performance of the approach, particularly the evolutionary model determination. The method is applied to the modeling of microbial growth by Monod Kinetics.
Substructure System Identification for Finite Element Model Updating
Craig, Roy R., Jr.; Blades, Eric L.
1997-01-01
This report summarizes research conducted under a NASA grant on the topic 'Substructure System Identification for Finite Element Model Updating.' The research concerns ongoing development of the Substructure System Identification Algorithm (SSID Algorithm), a system identification algorithm that can be used to obtain mathematical models of substructures, like Space Shuttle payloads. In the present study, particular attention was given to the following topics: making the algorithm robust to noisy test data, extending the algorithm to accept experimental FRF data that covers a broad frequency bandwidth, and developing a test analytical model (TAM) for use in relating test data to reduced-order finite element models.
Dynamic Modeling and Parameter Identification of Power Systems
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
@@ The generator, the excitation system, the steam turbine and speed governor, and the load are the so called four key models of power systems. Mathematical modeling and parameter identification for the four key models are of great importance as the basis for designing, operating, and analyzing power systems.
Identification and Control of a Cylindrical Tank Based on System Identification Models
Directory of Open Access Journals (Sweden)
Mary Mol Paul
2013-06-01
Full Text Available Advancements in the process control industry has made difficulties in controlling processes which are highly complex in nature. System identification provides a better solution for this problem with the help of identification models. In this paper ARX,ARMAX,BJ and OE models were used for the identification of a cylindrical tank and Ziegler Nichols tuning method to develop the controller for controlling the level of the tank. The proposed method provides simple and accurate models and thereby improving the efficency of identification process. MATLAB and LABView softwares were used here for identification and controlling.
Modeling emotional content of music using system identification.
Korhonen, Mark D; Clausi, David A; Jernigan, M Ed
2006-06-01
Research was conducted to develop a methodology to model the emotional content of music as a function of time and musical features. Emotion is quantified using the dimensions valence and arousal, and system-identification techniques are used to create the models. Results demonstrate that system identification provides a means to generalize the emotional content for a genre of music. The average R2 statistic of a valid linear model structure is 21.9% for valence and 78.4% for arousal. The proposed method of constructing models of emotional content generalizes previous time-series models and removes ambiguity from classifiers of emotion.
Model Identification of Linear Parameter Varying Aircraft Systems
Fujimore, Atsushi; Ljung, Lennart
2007-01-01
This article presents a parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modified for polytopic models. The modified prediction error method is applied to an LPV aircraft system whose varying parameter is the flight velocity and model parameters are the stability and control derivatives (SCDs). In an identification simulation, the polytopic model is more suitable for expre...
Vortex Tube Modeling Using the System Identification Method
Energy Technology Data Exchange (ETDEWEB)
Han, Jaeyoung; Jeong, Jiwoong; Yu, Sangseok [Chungnam Nat’l Univ., Daejeon (Korea, Republic of); Im, Seokyeon [Tongmyong Univ., Busan (Korea, Republic of)
2017-05-15
In this study, vortex tube system model is developed to predict the temperature of the hot and the cold sides. The vortex tube model is developed based on the system identification method, and the model utilized in this work to design the vortex tube is ARX type (Auto-Regressive with eXtra inputs). The derived polynomial model is validated against experimental data to verify the overall model accuracy. It is also shown that the derived model passes the stability test. It is confirmed that the derived model closely mimics the physical behavior of the vortex tube from both the static and dynamic numerical experiments by changing the angles of the low-temperature side throttle valve, clearly showing temperature separation. These results imply that the system identification based modeling can be a promising approach for the prediction of complex physical systems, including the vortex tube.
Parameter Identifiability of Ship Manoeuvring Modeling Using System Identification
Directory of Open Access Journals (Sweden)
Weilin Luo
2016-01-01
Full Text Available To improve the feasibility of system identification in the prediction of ship manoeuvrability, several measures are presented to deal with the parameter identifiability in the parametric modeling of ship manoeuvring motion based on system identification. Drift of nonlinear hydrodynamic coefficients is explained from the point of view of regression analysis. To diminish the multicollinearity in a complicated manoeuvring model, difference method and additional signal method are employed to reconstruct the samples. Moreover, the structure of manoeuvring model is simplified based on correlation analysis. Manoeuvring simulation is performed to demonstrate the validity of the measures proposed.
System Identification by Dynamic Factor Models
C. Heij (Christiaan); W. Scherrer; M. Destler
1996-01-01
textabstractThis paper concerns the modelling of stochastic processes by means of dynamic factor models. In such models the observed process is decomposed into a structured part called the latent process, and a remainder that is called noise. The observed variables are treated in a symmetric way, so
Modeling and identification of a PEM fuel cell humidification system
Institute of Scientific and Technical Information of China (English)
Xianrui DENG; Guoping LIU; George WANG; Min TAN
2009-01-01
A theoretical model of a humidifier of proton exchange membrane (PEM) fuel cell systems is developed and analyzed first in this paper. The model shows that there exists a strong nonlinearity in the system. Then, the system is identified using a wavelet networks method. To avoid the curse-of-dimensionality problem, a class of wavelet networks proposed by Billings is employed. The experimental data acquired from the test bench are used for identification. The one-step-ahead predictions and the five-step-ahead predictions are compared with the real measurements, respectively. It shows that the identified model can effectively describe the real system.
Estimation and Identification for Modeling Dynamic Systems.
1980-02-01
IEEE Transactions on Automatic Control , Vol. AC-20, pp. 775- 782, December 1975. [2] J.M. Mendel...Diego, California, Septer 1975. [3) J.M. Mendel, "Extension of Friedland’s Bias Filtering Technique to a Class of Nonlinear Systems," IEEE Transactions on Automatic Control , Vol...Time Linear Systems," IEEE Transactions on Automatic Control , April 1980. [8] M.S. Grewal and K. Glover, "Relationships
Robust nonlinear system identification using neural-network models.
Lu, S; Basar, T
1998-01-01
We study the problem of identification for nonlinear systems in the presence of unknown driving noise, using both feedforward multilayer neural network and radial basis function network models. Our objective is to resolve the difficulty associated with the persistency of excitation condition inherent to the standard schemes in the neural identification literature. This difficulty is circumvented here by a novel formulation and by using a new class of identification algorithms recently obtained by Didinsky et al. We show how these algorithms can be exploited to successfully identify the nonlinearity in the system using neural-network models. By embedding the original problem in one with noise-perturbed state measurements, we present a class of identifiers (under L1 and L2 cost criteria) which secure a good approximant for the system nonlinearity provided that some global optimization technique is used. In this respect, many available learning algorithms in the current neural-network literature, e.g., the backpropagation scheme and the genetic algorithms-based scheme, with slight modifications, can ensure the identification of the system nonlinearity. Subsequently, we address the same problem under a third, worst case L(infinity) criterion for an RBF modeling. We present a neural-network version of an H(infinity)-based identification algorithm from Didinsky et al and show how, along with an appropriate choice of control input to enhance excitation, under both full-state-derivative information (FSDI) and noise-perturbed full-state-information (NPFSI), it leads to satisfaction of a relevant persistency of excitation condition, and thereby to robust identification of the nonlinearity. Results from several simulation studies have been included to demonstrate the effectiveness of these algorithms.
Reduced Complexity Volterra Models for Nonlinear System Identification
Directory of Open Access Journals (Sweden)
Hacıoğlu Rıfat
2001-01-01
Full Text Available A broad class of nonlinear systems and filters can be modeled by the Volterra series representation. However, its practical use in nonlinear system identification is sometimes limited due to the large number of parameters associated with the Volterra filter′s structure. The parametric complexity also complicates design procedures based upon such a model. This limitation for system identification is addressed in this paper using a Fixed Pole Expansion Technique (FPET within the Volterra model structure. The FPET approach employs orthonormal basis functions derived from fixed (real or complex pole locations to expand the Volterra kernels and reduce the number of estimated parameters. That the performance of FPET can considerably reduce the number of estimated parameters is demonstrated by a digital satellite channel example in which we use the proposed method to identify the channel dynamics. Furthermore, a gradient-descent procedure that adaptively selects the pole locations in the FPET structure is developed in the paper.
Thruster Modelling for Underwater Vehicle Using System Identification Method
Directory of Open Access Journals (Sweden)
Mohd Shahrieel Mohd Aras
2013-05-01
Full Text Available This paper describes a study of thruster modelling for a remotely operated underwater vehicle (ROV by system identification using Microbox 2000/2000C. Microbox 2000/2000C is an XPC target machine device to interface between an ROV thruster with the MATLAB 2009 software. In this project, a model of the thruster will be developed first so that the system identification toolbox in MATLAB can be used. This project also presents a comparison of mathematical and empirical modelling. The experiments were carried out by using a mini compressor as a dummy depth pressure applied to a pressure sensor. The thruster model will thrust and submerge until it reaches a set point and maintain the set point depth. The depth was based on pressure sensor measurement. A conventional proportional controller was used in this project and the results gathered justified its selection.
Thruster Modelling for Underwater Vehicle Using System Identification Method
Directory of Open Access Journals (Sweden)
Mohd Shahrieel Mohd Aras
2013-05-01
Full Text Available Abstract This paper describes a study of thruster modelling for a remotely operated underwater vehicle (ROV by system identification using Microbox 2000/2000C. Microbox 2000/2000C is an XPC target machine device to interface between an ROV thruster with the MATLAB 2009 software. In this project, a model of the thruster will be developed first so that the system identification toolbox in MATLAB can be used. This project also presents a comparison of mathematical and empirical modelling. The experiments were carried out by using a mini compressor as a dummy depth pressure applied to a pressure sensor. The thruster model will thrust and submerge until it reaches a set point and maintain the set point depth. The depth was based on pressure sensor measurement. A conventional proportional controller was used in this project and the results gathered justified its selection.
A Study of Thermal Contact using Nonlinear System Identification Models
Directory of Open Access Journals (Sweden)
M. H. Shojaeefard
2008-01-01
Full Text Available One interesting application of system identification method is to identify and control the heat transfer from the exhaust valve to the seat to keep away the valve from being damaged. In this study, two co-axial cylindrical specimens are used as exhaust valve and its seat. Using the measured temperatures at different locations of the specimens and with a semi-analytical method, the temperature distribution of the specimens is calculated and consequently, the thermal contact conductance is calculated. By applying the system identification method and having the temperatures at both sides of the contact surface, the temperature transfer function is calculated. With regard to the fact that the thermal contact has nonlinear behavior, two nonlinear black-box models called nonlinear ARX and NLN Hammerstein-Wiener models are taken for accurate estimation. Results show that the NLN Hammerstein-Wiener models with wavelet network nonlinear estimator is the best.
TLM modeling and system identification of optimized antenna structures
Directory of Open Access Journals (Sweden)
N. Fichtner
2008-05-01
Full Text Available The transmission line matrix (TLM method in conjunction with the genetic algorithm (GA is presented for the bandwidth optimization of a low profile patch antenna. The optimization routine is supplemented by a system identification (SI procedure. By the SI the model parameters of the structure are estimated which is used for a reduction of the total TLM simulation time. The SI utilizes a new stability criterion of the physical poles for the parameter extraction.
Modeling and identification of HAGC system of temper rolling mill
Institute of Scientific and Technical Information of China (English)
HE Shang-hong; ZHONG Jue
2005-01-01
Including servo valve, hydraulic cylinder, mill and sensor and ignoring nonlinear factors, the linear dynamic model of hydraulic automatic gage control(HAGC) system of a temper rolling mill was theoretically derived. The order of the model is 4/4, and can be reduced to 2/2. Based on modulating functions method, utilizing numerical integration, we constructed the equivalent identification model of HAGC, and the least square estimation algorithm was established. The input and output data were acquired on line at temper rolling mill in Shangshai Baosteel Group Corporation, and the continuous time model of HAGC system was estimated with the proposed method. At different modulating window intervals, the estimated parameters changed remarkably. When the frequency bandwidth of modulating filter matches that of estimated system, the parameters can be estimated accurately. Finally, the dynamic model of the HAGC was obtained and validated based on the spectral analysis result.
A forward model-based validation of cardiovascular system identification
Mukkamala, R.; Cohen, R. J.
2001-01-01
We present a theoretical evaluation of a cardiovascular system identification method that we previously developed for the analysis of beat-to-beat fluctuations in noninvasively measured heart rate, arterial blood pressure, and instantaneous lung volume. The method provides a dynamical characterization of the important autonomic and mechanical mechanisms responsible for coupling the fluctuations (inverse modeling). To carry out the evaluation, we developed a computational model of the cardiovascular system capable of generating realistic beat-to-beat variability (forward modeling). We applied the method to data generated from the forward model and compared the resulting estimated dynamics with the actual dynamics of the forward model, which were either precisely known or easily determined. We found that the estimated dynamics corresponded to the actual dynamics and that this correspondence was robust to forward model uncertainty. We also demonstrated the sensitivity of the method in detecting small changes in parameters characterizing autonomic function in the forward model. These results provide confidence in the performance of the cardiovascular system identification method when applied to experimental data.
State-Space Modeling, System Identification and Control of a 4th Order Rotational Mechanical System
2009-12-01
state-space form. Identification of the state-space parameters was accomplished using the parameter estimation function in Matlab’s System ... Identification Toolbox utilizing experimental input/output data. The identified model was then constructed in Simulink and the accuracy of the identified model
Nonlinear State Space Modeling and System Identification for Electrohydraulic Control
Directory of Open Access Journals (Sweden)
Jun Yan
2013-01-01
Full Text Available The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear block with single polynomial nonlinearity to describe it. We simplify the H-W model into a linear-in-parameters structure by using the key term separation principle and then use a modified recursive least square method with iterative estimation of internal variables to identify all the unknown parameters simultaneously. It is found that the proposed H-W model approximates the actual system better than the independent Hammerstein, Wiener, and ARX models. The prediction error of the H-W model is about 13%, 54%, and 58% less than the Hammerstein, Wiener, and ARX models, respectively.
System identification and model reduction using modulating function techniques
Shen, Yan
1993-01-01
Weighted least squares (WLS) and adaptive weighted least squares (AWLS) algorithms are initiated for continuous-time system identification using Fourier type modulating function techniques. Two stochastic signal models are examined using the mean square properties of the stochastic calculus: an equation error signal model with white noise residuals, and a more realistic white measurement noise signal model. The covariance matrices in each model are shown to be banded and sparse, and a joint likelihood cost function is developed which links the real and imaginary parts of the modulated quantities. The superior performance of above algorithms is demonstrated by comparing them with the LS/MFT and popular predicting error method (PEM) through 200 Monte Carlo simulations. A model reduction problem is formulated with the AWLS/MFT algorithm, and comparisons are made via six examples with a variety of model reduction techniques, including the well-known balanced realization method. Here the AWLS/MFT algorithm manifests higher accuracy in almost all cases, and exhibits its unique flexibility and versatility. Armed with this model reduction, the AWLS/MFT algorithm is extended into MIMO transfer function system identification problems. The impact due to the discrepancy in bandwidths and gains among subsystem is explored through five examples. Finally, as a comprehensive application, the stability derivatives of the longitudinal and lateral dynamics of an F-18 aircraft are identified using physical flight data provided by NASA. A pole-constrained SIMO and MIMO AWLS/MFT algorithm is devised and analyzed. Monte Carlo simulations illustrate its high-noise rejecting properties. Utilizing the flight data, comparisons among different MFT algorithms are tabulated and the AWLS is found to be strongly favored in almost all facets.
Modeling and identification of a fly-by-wire control system.
Fabio Luciano Demarchi
2005-01-01
This work investigates the system identification and modeling techniques applied to a fly-by-wire system for pitch control of a commercial jet aircraft. The objective of the work is to build a model based on system identification techniques and generic modeling of the system, therefore using the "grey box" approach. The identification data was obtained from experimental tests performed at Embraer "Iron Bird" laboratory. An overview on flight controls systems is presented, focusing on fly-by-...
CSM Temper Mill System Identification and Modeling of Mobarake Steel Complex
Directory of Open Access Journals (Sweden)
Ehsan Tahmasebi
2008-03-01
Full Text Available System identification is defined as modeling a system, using the input-output data. In this paper, CSM temper mill line was studied and parametric system identification was explained. Using ARX method, the experimental system was modeled and identified. Good agreement was obtained when comparing extracted model outputs with the experimental data.
Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification
Directory of Open Access Journals (Sweden)
Ayedh Alqahtani
2016-01-01
Full Text Available Solar photovoltaic (PV energy sources are rapidly gaining potential growth and popularity compared to conventional fossil fuel sources. As the merging of PV systems with existing power sources increases, reliable and accurate PV system identification is essential, to address the highly nonlinear change in PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic with a switch-mode power converter. Measured input-output data are collected from a real PV panel to be used for the identification. The data are divided into estimation and validation sets. The identification methodology is discussed. A Hammerstein-Wiener model is identified and selected due to its suitability to best capture the PV system dynamics, and results and discussion are provided to demonstrate the accuracy of the selected model structure.
Fuzzy stochastic neural network model for structural system identification
Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong
2017-01-01
This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.
Control Oriented System Identification
1993-08-01
The research goals for this grant were to obtain algorithms for control oriented system identification is to construct dynamical models of systems...and measured information. Algorithms for this type of nonlinear system identification have been given that produce models suitable for gain scheduled
Institute of Scientific and Technical Information of China (English)
姚志远; 汪凤泉
2004-01-01
An online method of identification of dynamic characteristics only using measured ambient response of structural dynamic system is widely focused on. The Ibrahim and ARMA (AutoRegressive Moving Average ) methods are basic identification methods. A model on dynamic system suffered by random ambient excitation was researched into, and a subspace decomposition method being different from traditional harmonic retrieval method was introduced. Robustness and effectiveness of this approach on identification of vibration characteristics are demonstrated on numerical experiment.
Development of a Low-Order Model of an X-Wing Aircraft by System Identification.
1982-02-01
The original purpose of this contract was to prepare a flight test plan for the proposed X-wing demonstrator using system identification to extract...demonstration of the feasibility of using system identification techniques to extract low-order math models from time history data from a detailed X-wing rotor simulation (REXOR).
Nonlinear FOPDT Model Identification for the Superheat Dynamic in a Refrigeration System
DEFF Research Database (Denmark)
Yang, Zhenyu; Sun, Zhen; Andersen, Casper
2011-01-01
the considered system is discretized, the nonlinear FOPDT identification problem is formulated as a Mixed Integer Non-Linear Programming problem, and then an identification algorithm is proposed by combining the Branch-and-Bound method and Least Square technique, in order to on-line identify these time......An on-line nonlinear FOPDT system identification method is proposed and applied to model the superheat dynamic in a supermarket refrigeration system. The considered nonlinear FOPDT model is an extension of the standard FOPDT model by means that its parameters are time dependent. After...
Model Identification for Control of Display Units in Supermarket Refrigeration Systems
DEFF Research Database (Denmark)
O'Connell, Niamh; Madsen, Henrik; Andersen, Philip Hvidthøft Delff;
in a supermarket refrigeration system. The grey-box modelling approach is adopted, using stochastic differential equations to define the dynamics of the model, combining prior knowledge of the physical system with data-driven modelling. Model identification is performed using the forward selection method...
System Identification for Nonlinear FOPDT Model with Input-Dependent Dead-Time
DEFF Research Database (Denmark)
Sun, Zhen; Yang, Zhenyu
2011-01-01
. In order to identify these parameters in an online manner, the considered system is discretized at first. Then, the nonlinear FOPDT identification problem is formulated as a stochastic Mixed Integer Non-Linear Programming problem, and an identification algorithm is proposed by combining the Branch......An on-line iterative method of system identification for a kind of nonlinear FOPDT system is proposed in the paper. The considered nonlinear FOPDT model is an extension of the standard FOPDT model by means that its dead time depends on the input signal and the other parameters are time dependent...
Energy Technology Data Exchange (ETDEWEB)
Stuart, J.G.; Wright, A.D.; Butterfield, C.P.
1996-10-01
Mitigating the effects of damaging wind turbine loads and responses extends the lifetime of the turbine and, consequently, reduces the associated Cost of Energy (COE). Active control of aerodynamic devices is one option for achieving wind turbine load mitigation. Generally speaking, control system design and analysis requires a reasonable dynamic model of {open_quotes}plant,{close_quotes} (i.e., the system being controlled). This paper extends the wind turbine aileron control research, previously conducted at the National Wind Technology Center (NWTC), by presenting a more detailed development of the wind turbine dynamic model. In prior research, active aileron control designs were implemented in an existing wind turbine structural dynamics code, FAST (Fatigue, Aerodynamics, Structures, and Turbulence). In this paper, the FAST code is used, in conjunction with system identification, to generate a wind turbine dynamic model for use in active aileron control system design. The FAST code is described and an overview of the system identification technique is presented. An aileron control case study is used to demonstrate this modeling technique. The results of the case study are then used to propose ideas for generalizing this technique for creating dynamic models for other wind turbine control applications.
Heat Transfer Parametric System Identification
1993-06-01
Transfer Parametric System Identification 6. AUTHOR(S Parker, Gregory K. 7. PERFORMING ORGANIZATION NAME(S) AND AOORESS(ES) 8. PERFORMING ORGANIZATION...distribution is unlimited. Heat Transfer Parametric System Identification by Gregory K. Parker Lieutenant, United States Navy BS., DeVry Institute of...Modeling Concept ........ ........... 3 2. Lumped Parameter Approach ...... ......... 4 3. Parametric System Identification ....... 4 B. BASIC MODELING
Modeling, estimation and identification of stochastic systems with latent variables
Bottegal, Giulio
2013-01-01
The main topic of this thesis is the analysis of static and dynamic models in which some variables, although directly influencing the behavior of certain observables, are not accessible to measurements. These models find applications in many branches of science and engineering, such as control systems, communications, natural and biological sciences and econometrics. It is well-known that models with unaccessible - or latent - variables, usually suffer from a lack of uniqueness of representat...
Identification and modeling of discoverers in online social systems
Medo, Matus; Zeng, An; Zhang, Yi-Cheng
2015-01-01
The dynamics of individuals is of essential importance for understanding the evolution of social systems. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce, all tend to what is already popular. We develop an analytical time-aware framework which shows that when individuals make choices -- which item to buy, for example -- in online social systems, a small fraction of them is consistently successful in discovering popular items long before they actually become popular. We argue that these users, whom we refer to as discoverers, are fundamentally different from the previously known opinion leaders, influentials, and innovators. We use the proposed framework to demonstrate that discoverers are present in a wide range of systems. Once identified, they can be used to predict the future success of items. We propose a network model which reproduces the discovery patterns observed in the real data. Furthermore, data produced by the model pose a fundamental cha...
Reduced-size kernel models for nonlinear hybrid system identification.
Le, Van Luong; Bloch, Grard; Lauer, Fabien
2011-12-01
This brief paper focuses on the identification of nonlinear hybrid dynamical systems, i.e., systems switching between multiple nonlinear dynamical behaviors. Thus the aim is to learn an ensemble of submodels from a single set of input-output data in a regression setting with no prior knowledge on the grouping of the data points into similar behaviors. To be able to approximate arbitrary nonlinearities, kernel submodels are considered. However, in order to maintain efficiency when applying the method to large data sets, a preprocessing step is required in order to fix the submodel sizes and limit the number of optimization variables. This brief paper proposes four approaches, respectively inspired by the fixed-size least-squares support vector machines, the feature vector selection method, the kernel principal component regression and a modification of the latter, in order to deal with this issue and build sparse kernel submodels. These are compared in numerical experiments, which show that the proposed approach achieves the simultaneous classification of data points and approximation of the nonlinear behaviors in an efficient and accurate manner.
Agnostic System Identification for Model-Based Reinforcement Learning
Ross, Stephane
2012-01-01
A fundamental problem in control is to learn a model of a system from observations that is useful for controller synthesis. To provide good performance guarantees, existing methods must assume that the real system is in the class of models considered during learning. We present an iterative method with strong guarantees even in the agnostic case where the system is not in the class. In particular, we show that any no-regret online learning algorithm can be used to obtain a near-optimal policy, provided some model achieves low training error and access to a good exploration distribution. Our approach applies to both discrete and continuous domains. We demonstrate its efficacy and scalability on a challenging helicopter domain from the literature.
Wei, H. L.; Balikhin, M.; S. A. Billings
2003-01-01
Identification techniques for nonlinear time-varying systems are investigated based on the NARMAX model and multiresolution wavelet expansions. It is shown that a NARMAX model with time-varying coefficients can be reduced to a time-invariant linear-in-the-parameters analysis problem by then adapted to estimate the parameters. An application data relating to magnetic storms is used to illustrate the realistic application of the new identification technique.
Linear system identification via backward-time observer models
Juang, Jer-Nan; Phan, Minh
1993-01-01
This paper presents an algorithm to identify a state-space model of a linear system using a backward-time approach. The procedure consists of three basic steps. First, the Markov parameters of a backward-time observer are computed from experimental input-output data. Second, the backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) from which a backward-time state-space model is realized using the Eigensystem Realization Algorithm. Third, the obtained backward-time state space model is converted to the usual forward-time representation. Stochastic properties of this approach will be discussed. Experimental results are given to illustrate when and to what extent this concept works.
Modelling of Biometric Identification System with Given Parameters Using Colored Petri Nets
Petrosyan, G.; Ter-Vardanyan, L.; Gaboutchian, A.
2017-05-01
Biometric identification systems use given parameters and function on the basis of Colored Petri Nets as a modelling language developed for systems in which communication, synchronization and distributed resources play an important role. Colored Petri Nets combine the strengths of Classical Petri Nets with the power of a high-level programming language. Coloured Petri Nets have both, formal intuitive and graphical presentations. Graphical CPN model consists of a set of interacting modules which include a network of places, transitions and arcs. Mathematical representation has a well-defined syntax and semantics, as well as defines system behavioural properties. One of the best known features used in biometric is the human finger print pattern. During the last decade other human features have become of interest, such as iris-based or face recognition. The objective of this paper is to introduce the fundamental concepts of Petri Nets in relation to tooth shape analysis. Biometric identification systems functioning has two phases: data enrollment phase and identification phase. During the data enrollment phase images of teeth are added to database. This record contains enrollment data as a noisy version of the biometrical data corresponding to the individual. During the identification phase an unknown individual is observed again and is compared to the enrollment data in the database and then system estimates the individual. The purpose of modeling biometric identification system by means of Petri Nets is to reveal the following aspects of the functioning model: the efficiency of the model, behavior of the model, mistakes and accidents in the model, feasibility of the model simplification or substitution of its separate components for more effective components without interfering system functioning. The results of biometric identification system modeling and evaluating are presented and discussed.
Optimization of Experimental Model Parameter Identification for Energy Storage Systems
Directory of Open Access Journals (Sweden)
Rosario Morello
2013-09-01
Full Text Available The smart grid approach is envisioned to take advantage of all available modern technologies in transforming the current power system to provide benefits to all stakeholders in the fields of efficient energy utilisation and of wide integration of renewable sources. Energy storage systems could help to solve some issues that stem from renewable energy usage in terms of stabilizing the intermittent energy production, power quality and power peak mitigation. With the integration of energy storage systems into the smart grids, their accurate modeling becomes a necessity, in order to gain robust real-time control on the network, in terms of stability and energy supply forecasting. In this framework, this paper proposes a procedure to identify the values of the battery model parameters in order to best fit experimental data and integrate it, along with models of energy sources and electrical loads, in a complete framework which represents a real time smart grid management system. The proposed method is based on a hybrid optimisation technique, which makes combined use of a stochastic and a deterministic algorithm, with low computational burden and can therefore be repeated over time in order to account for parameter variations due to the battery’s age and usage.
Nonlinear FOPDT Model Identification for the Superheat Dynamic in a Refrigeration System
DEFF Research Database (Denmark)
Yang, Zhenyu; Sun, Zhen; Andersen, Casper
2011-01-01
An on-line nonlinear FOPDT system identification method is proposed and applied to model the superheat dynamic in a supermarket refrigeration system. The considered nonlinear FOPDT model is an extension of the standard FOPDT model by means that its parameters are time dependent. After......-dependent parameters. The proposed method is firstly tested through a number of numerical examples, and then applied to model the superheat dynamic in a supermarket refrigeration system based on experimental data. As shown in these studies, the proposed method is quite promising in terms of reasonable accuracy, large...... the considered system is discretized, the nonlinear FOPDT identification problem is formulated as a Mixed Integer Non-Linear Programming problem, and then an identification algorithm is proposed by combining the Branch-and-Bound method and Least Square technique, in order to on-line identify these time...
Nonlinear System Identification via Basis Functions Based Time Domain Volterra Model
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Yazid Edwar
2014-07-01
Full Text Available This paper proposes basis functions based time domain Volterra model for nonlinear system identification. The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA. The accuracy and practicability of the proposed method are then assessed experimentally from a scaled 1:100 model of a prototype truss spar platform. Identification results in time and frequency domain are presented and coherent functions are performed to check the quality of the identification results. It is shown that results between experimental data and proposed method are in good agreement.
Inan, Halil Ibrahim; Sagris, Valentina; Devos, Wim; Milenov, Pavel; van Oosterom, Peter; Zevenbergen, Jaap
2010-12-01
The Common Agricultural Policy (CAP) of the European Union (EU) has dramatically changed after 1992, and from then on the CAP focused on the management of direct income subsidies instead of production-based subsidies. For this focus, Member States (MS) are expected to establish Integrated Administration and Control System (IACS), including a Land Parcel Identification System (LPIS) as the spatial part of IACS. Different MS have chosen different solutions for their LPIS. Currently, some MS based their IACS/LPIS on data from their Land Administration Systems (LAS), and many others use purpose built special systems for their IACS/LPIS. The issue with these different IACS/LPIS is that they do not have standardized structures; rather, each represents a unique design in each MS, both in the case of LAS based or special systems. In this study, we aim at designing a core data model for those IACS/LPIS based on LAS. For this purpose, we make use of the ongoing standardization initiatives for LAS (Land Administration Domain Model: LADM) and IACS/LPIS (LPIS Core Model: LCM). The data model we propose in this study implies the collaboration between LADM and LCM and includes some extensions. Some basic issues with the collaboration model are discussed within this study: registration of farmers, land use rights and farming limitations, geometry/topology, temporal data management etc. For further explanation of the model structure, sample instance level diagrams illustrating some typical situations are also included.
"GRAY-BOX" MODELING METHOD AND PARAMETERS IDENTIFICATION FOR LARGE-SCALE HYDRAULIC SYSTEM
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Modeling and digital simulation is an effective method to analyze the dynamic characteristics of hydraulic system. It is difficult to determine some performance parameters in the hydraulic system by means of currently used modeling methods. The "gray-box" modeling method for large-scale hydraulic system is introduced. The principle of the method, the submodels of some components and the parameters identification of components or subsystem are researched.
Directory of Open Access Journals (Sweden)
Arbab Nighat Khizer
2015-01-01
Full Text Available This paper presents a time-domain approach for identification of longitudinal dynamics of single rotor model helicopter. A frequency sweep excitation input signal is applied for hover flying mode widely used for space state linearized model. A fully automated programmed flight test method provides high quality flight data for system identification using the computer controlled flight simulator X-plane©. The flight test data were recorded, analyzed and reduced using the SIDPAC (System Identification Programs for Air Craft toolbox for MATLAB, resulting in an aerodynamic model of single rotor helicopter. Finally, the identified model of single rotor helicopter is validated on Raptor 30-class model helicopter at hover showing the reliability of proposed approach
System Identification of a Heaving Point Absorber: Design of Experiment and Device Modeling
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Giorgio Bacelli
2017-04-01
Full Text Available Empirically based modeling is an essential aspect of design for a wave energy converter. Empirically based models are used in structural, mechanical and control design processes, as well as for performance prediction. Both the design of experiments and methods used in system identification have a strong impact on the quality of the resulting model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followed for wave tank testing. The general system identification processes are shown to have a number of advantages, including an increased signal-to-noise ratio, reduced experimental time and higher frequency resolution. The experimental wave tank data is used to produce multiple models using different formulations to represent the dynamics of the wave energy converter. These models are validated and their performance is compared against one another. While most models of wave energy converters use a formulation with surface elevation as an input, this study shows that a model using a hull pressure measurement to incorporate the wave excitation phenomenon has better accuracy.
Optimized System Identification
Juang, Jer-Nan; Longman, Richard W.
1999-01-01
In system identification, one usually cares most about finding a model whose outputs are as close as possible to the true system outputs when the same input is applied to both. However, most system identification algorithms do not minimize this output error. Often they minimize model equation error instead, as in typical least-squares fits using a finite-difference model, and it is seen here that this distinction is significant. Here, we develop a set of system identification algorithms that minimize output error for multi-input/multi-output and multi-input/single-output systems. This is done with sequential quadratic programming iterations on the nonlinear least-squares problems, with an eigendecomposition to handle indefinite second partials. This optimization minimizes a nonlinear function of many variables, and hence can converge to local minima. To handle this problem, we start the iterations from the OKID (Observer/Kalman Identification) algorithm result. Not only has OKID proved very effective in practice, it minimizes an output error of an observer which has the property that as the data set gets large, it converges to minimizing the criterion of interest here. Hence, it is a particularly good starting point for the nonlinear iterations here. Examples show that the methods developed here eliminate the bias that is often observed using any system identification methods of either over-estimating or under-estimating the damping of vibration modes in lightly damped structures.
Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization
Institute of Scientific and Technical Information of China (English)
LI Yong; TANG Ying-Gan
2010-01-01
@@ A fuzzy Wiener model is proposed to identify chaotic systems.The proposed fuzzy Wiener model consists of two parts,one is a linear dynamic subsystem and the other is a static nonlinear part,which is represented by the Takagi-Sugeno fuzzy model Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model.Particle swarm optimization algorithm,a global optimizer,is used to search the optimal parameter of the fuzzy Wiener model.The proposed method can identify the parameters of the linear part and nonlinear part simultaneously.Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method.
Advanced System Identification for High-rise Building Using Shear-Bending Model
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Kohei Fujita
2016-11-01
Full Text Available In order to identify physical model parameters of a high-rise building, a new story stiffness identification method is presented based on a shear-bending model and the identification function. Although a shear building model may be the simplest conventional model for representing tall buildings, the system identification (SI method using that model is not necessarily appropriate. This is because the influence of bending deformation is predominant in such high-rise buildings. For this reason, a shear-bending model is used where the shear and bending stiffnesses are unknown. In the previous researches using the shear-bending model, it was difficult to identify the bending stiffnesses stably and reliably. In this paper, to overcome such instability of bending stiffness identification of the shear-bending model, a new SI algorithm using both the shear model and the shear-bending model is presented. The proposed SI algorithm is based on the observation that the fundamental-mode shape of the identified shear model is similar to that of the shear-bending model identified in the previous SI method. In order to verify the advanced SI method, two different 20-story building models are investigated in the numerical simulations. From the results of the simulations, both the shear and bending stiffnesses of the shear-bending model are identified reliably and stably in the proposed SI method.
Yuan, Jinlong; Zhang, Xu; Zhu, Xi; Feng, Enmin; Yin, Hongchao; Xiu, Zhilong
2014-06-01
The bio-dissimilation of glycerol to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae (K. pneumoniae) can be characterized by a complex metabolic system of interactions among biochemical fluxes, metabolic compounds, key enzymes and genetic regulation. In this paper, in consideration of the fact that the transport ways of 1,3-PD and glycerol with different weights across cell membrane are still unclear in batch culture, we consider 121 possible metabolic pathways and establish a novel mathematical model which is represented by a complex metabolic system. Taking into account the difficulty in accurately measuring the concentration of intracellular substances and the absence of equilibrium point for the metabolic system of batch culture, the novel approach used here is to define quantitatively biological robustness of the intracellular substance concentrations for the overall process of batch culture. To determine the most possible metabolic pathway, we take the defined biological robustness as cost function and establish an identification model, in which 1452 system parameters and 484 pathway parameters are involved. Simultaneously, the identification model is subject to the metabolic system, continuous state constraints and parameter constraints. As such, solving the identification model by a serial program is a very complicated task. We propose a parallel migration particle swarm optimization algorithm (MPSO) capable of solving the identification model in conjunction with the constraint transcription and smoothing approximation techniques. Numerical results show that the most possible metabolic pathway and the corresponding metabolic system can reasonably describe the process of batch culture.
Yan, Jun; Li, Bo; Guo, Gang; Zeng, Yonghua; Zhang, Meijun
2013-11-01
Electro-hydraulic control systems are nonlinear in nature and their mathematic models have unknown parameters. Existing research of modeling and identification of the electro-hydraulic control system is mainly based on theoretical state space model, and the parameters identification is hard due to its demand on internal states measurement. Moreover, there are also some hard-to-model nonlinearities in theoretical model, which needs to be overcome. Modeling and identification of the electro-hydraulic control system of an excavator arm based on block-oriented nonlinear(BONL) models is investigated. The nonlinear state space model of the system is built first, and field tests are carried out to reveal the nonlinear characteristics of the system. Based on the physic insight into the system, three BONL models are adopted to describe the highly nonlinear system. The Hammerstein model is composed of a two-segment polynomial nonlinearity followed by a linear dynamic subsystem. The Hammerstein-Wiener(H-W) model is represented by the Hammerstein model in cascade with another single polynomial nonlinearity. A novel Pseudo-Hammerstein-Wiener(P-H-W) model is developed by replacing the single polynomial of the H-W model by a non-smooth backlash function. The key term separation principle is applied to simplify the BONL models into linear-in-parameters structures. Then, a modified recursive least square algorithm(MRLSA) with iterative estimation of internal variables is developed to identify the all the parameters simultaneously. The identification results demonstrate that the BONL models with two-segment polynomial nonlinearities are able to capture the system behavior, and the P-H-W model has the best prediction accuracy. Comparison experiments show that the velocity prediction error of the P-H-W model is reduced by 14%, 30% and 75% to the H-W model, Hammerstein model, and extended auto-regressive (ARX) model, respectively. This research is helpful in controller design, system
An H{sup {infinity}} system identification algorithm applied to tokamak modelling
Energy Technology Data Exchange (ETDEWEB)
Coutlis, A.; Limebeer, D.J.N.; Wainwright, J. [Centre for Process Systems Eng. and Dept. of Electrical Eng., Imperial College, London (United Kingdom); Lister, J.B.; Vyas, P.; Ward, D.J. [Ecole Polytechnique Federale, Lausanne (Switzerland). Centre de Recherche en Physique des Plasma (CRPP)
1997-08-01
In this paper we describe the application of MISO System Identification to Tokamak simulations and machines. The work is motivated by the desire to create linear models for the design of modern controllers. The method described in this paper is a worst-case identification technique, in that it aims to minimise the H{sup {infinity}} error between the identified model and the plant. Such a model is particularly suited for robust controller design. The method is fully detailed from the design of identification experiments through to the creation of a low-order model from a combination of Hankel model reduction and Chebycheff approximation. We show results from the application of this method to a powerful Tokamak Simulation Code (TSC) and discuss results on the TCV Tokamak in Lausanne. (author) 2 figs., 11 refs.
Model identification of stomatognathic muscle system activity during mastication
Kijak, Edward; Margielewicz, Jerzy; Lietz-Kijak, Danuta; Wilemska-Kucharzewska, Katarzyna; Kucharzewski, Marek; Śliwiński, Zbigniew
2017-01-01
The present study aimed to determine the numeric projection of the function of the mandible and muscle system during mastication. An experimental study was conducted on a healthy 47 year-old subject. On clinical examination no functional disorders were observed. To evaluate the activity of mastication during muscle functioning, bread cubes and hazelnuts were selected (2 cm2 and 1.2/1.3 cm in diameter, respectively) for condyloid processing. An assessment of the activity of mastication during muscle functioning was determined on the basis of numeric calculations conducted with a novel software programme, Kinematics 3D, designed specifically for this study. The efficacy of the model was verified by ensuring the experimentally recorded trajectories were concordant with those calculated numerically. Experimental measurements of the characteristic points of the mandible trajectory were recorded six times. Using the configuration coordinates that were calculated, the dominant componential harmonics of the amplitude-frequency spectrum were identified. The average value of the dominant frequency during mastication of the bread cubes was ~1.16±0.06 Hz, whereas in the case of the hazelnut, this value was nearly two-fold higher at 1.84±0.07 Hz. The most asymmetrical action during mastication was demonstrated to be carried out by the lateral pterygoid muscles, provided that their functioning was not influenced by food consistency. The consistency of the food products had a decisive impact on the frequency of mastication and the number of cycles necessary to grind the food. Model tests on the function of the masticatory organ serve as effective tools since they provide qualitative and quantitative novel information on the functioning of the human masticatory organ. PMID:28123482
Identification and Modeling of Automotive Electrical Parking Brake System for SiL Simulation
Institute of Scientific and Technical Information of China (English)
PENG Yi-qiang; LI Wei; ZHANG Jin-lei
2008-01-01
To evaluate the software behavior of the electronic control unit (ECU) of automotive electrical parking brake (EPB), a software-in-the-loop (SiL) simulation system is built. The EPB is simulated by ARX (auto-regressive with auxiliary input) model, ARMAX (auto-regressive moving average with auxiliary input) model, and NNARMAX (neural network ARMAX) model. By system identification, the ARX(3,4,2), ARX(4,4,2), ARMAX(3,3,1,1), and ARMAX(4,4,3,2) models are derived. Validation results show that the four-order ARMAX model and the NNARMAX model better simulate the actuator of the EPB.
DEFF Research Database (Denmark)
Li, Chunjian; Andersen, Søren Vang
2007-01-01
We propose two blind system identification methods that exploit the underlying dynamics of non-Gaussian signals. The two signal models to be identified are: an Auto-Regressive (AR) model driven by a discrete-state Hidden Markov process, and the same model whose output is perturbed by white Gaussian...
Maritime Transportation System Safety – Modeling and Identification
Directory of Open Access Journals (Sweden)
Przemyslaw Dziula
2013-06-01
Full Text Available The article is showing a concept of critical infrastructure systems’ safety states model. Model construction is basing on: popular technical systems’ safety states models, and notions specified in acts of law and other studies concerning crisis management. Paper is including some concept of proposed model usage possibilities - methods and procedures for estimating unknown basic parameters of safety states transitions process: identifying the distributions of its conditional lifetime at safety states, estimating probabilities of its staying at safety states at the initial moment, probabilities of its transitions between safety states and parameters of the distribution for the description of its conditional lifetimes at safety states.
Bazrgari, Babak; Nussbaum, Maury A; Madigan, Michael L
2012-01-01
The use of system identification to quantify trunk mechanical properties is growing in biomechanics research. The effects of several experimental and modelling factors involved in the system identification of trunk mechanical properties were investigated. Trunk kinematics and kinetics were measured in six individuals when exposed to sudden trunk perturbations. Effects of motion sensor positioning and properties of elements between the perturbing device and the trunk were investigated by adopting different models for system identification. Results showed that by measuring trunk kinematics at a location other than the trunk surface, the deformation of soft tissues is erroneously included into trunk kinematics and results in the trunk being predicted as a more damped structure. Results also showed that including elements between the trunk and the perturbing device in the system identification model did not substantially alter model predictions. Other important parameters that were found to substantially affect predictions were the cut-off frequency used when low-pass filtering raw data and the data window length used to estimate trunk properties.
MIMO model of an interacting series process for Robust MPC via System Identification.
Wibowo, Tri Chandra S; Saad, Nordin
2010-07-01
This paper discusses the empirical modeling using system identification technique with a focus on an interacting series process. The study is carried out experimentally using a gaseous pilot plant as the process, in which the dynamic of such a plant exhibits the typical dynamic of an interacting series process. Three practical approaches are investigated and their performances are evaluated. The models developed are also examined in real-time implementation of a linear model predictive control. The selected model is able to reproduce the main dynamic characteristics of the plant in open-loop and produces zero steady-state errors in closed-loop control system. Several issues concerning the identification process and the construction of a MIMO state space model for a series interacting process are deliberated.
Boutalis, Yiannis; Kottas, Theodore; Christodoulou, Manolis A
2014-01-01
Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering s...
Kazemi, Mahdi; Arefi, Mohammad Mehdi
2016-12-15
In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used.
Common Rail System for GDI Engines Modelling, Identification, and Control
Fiengo, Giovanni; Palladino, Angelo; Giglio, Veniero
2013-01-01
Progressive reductions in vehicle emission requirements have forced the automotive industry to invest in research and development of alternative control strategies. Continual control action exerted by a dedicated electronic control unit ensures that best performance in terms of pollutant emissions and power density is married with driveability and diagnostics. Gasoline direct injection (GDI) engine technology is a way to attain these goals. This brief describes the functioning of a GDI engine equipped with a common rail (CR) system, and the devices necessary to run test-bench experiments in detail. The text should prove instructive to researchers in engine control and students are recommended to this brief as their first approach to this technology. Later chapters of the brief relate an innovative strategy designed to assist with the engine management system; injection pressure regulation for fuel pressure stabilization in the CR fuel line is proposed and validated by experiment. The resulting control scheme ...
A System Identification Software Tool for General MISO ARX-Type of Model Structures
Lindskog, Peter
1996-01-01
The typical system identification procedure requires powerful and versatile software means. In this paper we describe and exemplify the use of a prototype identi#cation software tool, applicable for the rather broad class of multi input single output model structures with regressors that are formed by delayed in- and outputs. Interesting special instances of this model structure category include, e.g., linear ARX and many semi-physical structures, feed-forward neural networks, radial basis fu...
System identification of perilymphatic fistula in an animal model
Wall, C. 3rd; Casselbrant, M. L.
1992-01-01
An acute animal model has been developed in the chinchilla for the study of perilymphatic fistulas. Micropunctures were made in three sites to simulate bony, round window, and oval window fistulas. The eye movements in response to pressure applied to the external auditory canal were recorded after micropuncture induction and in preoperative controls. The main pressure stimulus was a pseudorandom binary sequence (PRBS) that rapidly changed between plus and minus 200 mm of water. The PRBS stimulus, with its wide frequency bandwidth, produced responses clearly above the preoperative baseline in 78 percent of the runs. The response was better between 0.5 and 3.3 Hz than it was below 0.5 Hz. The direction of horizontal eye movement was toward the side of the fistula with positive pressure applied in 92 percent of the runs. Vertical eye movements were also observed. The ratio of vertical eye displacement to horizontal eye displacement depended upon the site of the micropuncture induction. Thus, such a ratio measurement may be clinically useful in the noninvasive localization of perilymphatic fistulas in humans.
System Identification Based Proxy Model of a Reservoir under Water Injection
Directory of Open Access Journals (Sweden)
Berihun M. Negash
2017-01-01
Full Text Available Simulation of numerical reservoir models with thousands and millions of grid blocks may consume a significant amount of time and effort, even when high performance processors are used. In cases where the simulation runs are required for sensitivity analysis, dynamic control, and optimization, the act needs to be repeated several times by continuously changing parameters. This makes it even more time-consuming. Currently, proxy models that are based on response surface are being used to lessen the time required for running simulations during sensitivity analysis and optimization. Proxy models are lighter mathematical models that run faster and perform in place of heavier models that require large computations. Nevertheless, to acquire data for modeling and validation and develop the proxy model itself, hundreds of simulation runs are required. In this paper, a system identification based proxy model that requires only a single simulation run and a properly designed excitation signal was proposed and evaluated using a benchmark case study. The results show that, with proper design of excitation signal and proper selection of model structure, system identification based proxy models are found to be practical and efficient alternatives for mimicking the performance of numerical reservoir models. The resulting proxy models have potential applications for dynamic well control and optimization.
Identification of reduced-order model for an aeroelastic system from flutter test data
Directory of Open Access Journals (Sweden)
Wei Tang
2017-02-01
Full Text Available Recently, flutter active control using linear parameter varying (LPV framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroelastic models. Therefore, the reduced-order model is required by synthesis for avoidance of large computation cost and high-order controller. This paper proposes a new procedure for generation of accurate reduced-order linear time-invariant (LTI models by using system identification from flutter testing data. The proposed approach is in two steps. The well-known poly-reference least squares complex frequency (p-LSCF algorithm is firstly employed for modal parameter identification from frequency response measurement. After parameter identification, the dominant physical modes are determined by clear stabilization diagrams and clustering technique. In the second step, with prior knowledge of physical poles, the improved frequency-domain maximum likelihood (ML estimator is presented for building accurate reduced-order model. Before ML estimation, an improved subspace identification considering the poles constraint is also proposed for initializing the iterative procedure. Finally, the performance of the proposed procedure is validated by real flight flutter test data.
Ebrahimian, Mahdi; Todorovska, Maria I.
2014-03-01
A layered Timoshenko beam (TB) model of a high-rise building is presented and applied to system identification of a full-scale building from recorded seismic response. This model is a new development in a wave method for earthquake damage detection and structural health monitoring being developed by the authors' research group. The method is based on monitoring changes in the wave properties of the structure, such as the velocity of wave propagation vertically through the structure. This model is an improvement over the previously used layered shear beam (SB) model because it accounts for wave dispersion caused by flexural deformation present in addition to shear. It also accounts for the rotatory inertia and the variation of the building properties with height. The case study is a 54-story steel frame building located in downtown Los Angeles. Recorded accelerations during the Northridge earthquake of 1994 are used for system identification of the NS response. The model parameters are identified by matching, in the least squares sense, the model and observed impulse response functions at all levels where motion was recorded. The model is then used to compute the building vertical phase and group velocities. Impulse responses computed by deconvolution of the recorded motions with the roof response are used, which represent the building response to a virtual source at the roof. The better match of transfer-function amplitudes of the fitted TB model than of previously fitted SB model indicates that the layered TB model is a better physical model for this building.
Pasqualetti, Fabio; Bullo, Francesco
2012-01-01
Cyber-physical systems integrate computation, communication, and physical capabilities to interact with the physical world and humans. Besides failures of components, cyber-physical systems are prone to malignant attacks, and specific analysis tools as well as monitoring mechanisms need to be developed to enforce system security and reliability. This paper proposes a unified framework to analyze the resilience of cyber-physical systems against attacks cast by an omniscient adversary. We model cyber-physical systems as linear descriptor systems, and attacks as exogenous unknown inputs. Despite its simplicity, our model captures various real-world cyber-physical systems, and it includes and generalizes many prototypical attacks, including stealth, (dynamic) false-data injection and replay attacks. First, we characterize fundamental limitations of static, dynamic, and active monitors for attack detection and identification. Second, we provide constructive algebraic conditions to cast undetectable and unidentifia...
Aguirre, Luis Antonio; Billings, S. A.
This paper investigates the identification of global models from chaotic data corrupted by additive noise. It is verified that noise has a strong influence on the identification of chaotic systems. In particular, there seems to be a critical noise level beyond which the accurate estimation of polynomial models from chaotic data becomes very difficult. Similarities with the estimation of the largest Lyapunov exponent from noisy data suggest that part of the problem might be related to the limited ability of predicting the data records when these are chaotic. A nonlinear filtering scheme is suggested in order to reduce the noise in the data and thereby enable the estimation of good models. This prediction-based filtering incorporates a resetting mechanism which enables the filtering of chaotic data and which is also applicable to non-chaotic data.
Sparse identification of a predator-prey system from simulation data of a convection model
DEFF Research Database (Denmark)
Dam, Magnus; Brøns, Morten; Rasmussen, Jens Juul
2017-01-01
of the pressure profile, the turbulent flow, and the zonal flow capture the fundamental dynamic behavior of the full system. By applying the sparse identification of nonlinear dynamics (SINDy) method, we identify a predator-prey type dynamical system that approximates the underlying dynamics of the three energy......The use of low-dimensional dynamical systems as reduced models for plasma dynamics is useful as solving an initial value problem requires much less computational resources than fluid simulations. We utilize a data-driven modeling approach to identify a reduced model from simulation data...... state variables. A bifurcation analysis of the system reveals consistency between the bifurcation structures, observed for the simulation data, and the identified underlying system....
Dynamic system identification and model-based fault diagnosis of an industrial gas turbine prototype
Energy Technology Data Exchange (ETDEWEB)
Simani, S. [Universita di Ferrara (Italy). Dipartimento di Ingegneria; Fantuzzi, C. [Universita di Modena e Reggio Emilia (Italy). Dipartimento di Scienze e Metodi per l' Ingegneria
2006-07-15
In this paper, a model-based procedure exploiting analytical redundancy for the detection and isolation of faults on a gas turbine process is presented. The main point of the present work consists of exploiting system identification schemes in connection with observer and filter design procedures for diagnostic purpose. Linear model identification (black-box modelling) and output estimation (dynamic observers and Kalman filters) integrated approaches to fault diagnosis are in particular advantageous in terms of solution complexity and performance. This scheme is especially useful when robust solutions are considered for minimising the effects of modelling errors and noise, while maximising fault sensitivity. A model of the process under investigation is obtained by identification procedures, whilst the residual generation task is achieved by means of output observers and Kalman filters designed in both noise-free and noisy assumptions. The proposed tools have been tested on a single-shaft industrial gas turbine prototype model and they have been evaluated using non-linear simulations, based on the gas turbine data. (author)
Adaptive modeling, identification, and control of dynamic structural systems. I. Theory
Safak, Erdal
1989-01-01
A concise review of the theory of adaptive modeling, identification, and control of dynamic structural systems based on discrete-time recordings is presented. Adaptive methods have four major advantages over the classical methods: (1) Removal of the noise from the signal is done over the whole frequency band; (2) time-varying characteristics of systems can be tracked; (3) systems with unknown characteristics can be controlled; and (4) a small segment of the data is needed during the computations. Included in the paper are the discrete-time representation of single-input single-output (SISO) systems, models for SISO systems with noise, the concept of stochastic approximation, recursive prediction error method (RPEM) for system identification, and the adaptive control. Guidelines for model selection and model validation and the computational aspects of the method are also discussed in the paper. The present paper is the first of two companion papers. The theory given in the paper is limited to that which is necessary to follow the examples for applications in structural dynamics presented in the second paper.
Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control.
Deshpande, Sunil; Nandola, Naresh N; Rivera, Daniel E; Younger, Jarred W
2014-12-01
The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.
Development of HT-BP nueral network system for the identification of well test interpretation model
Energy Technology Data Exchange (ETDEWEB)
Sung, W.; Hanyang, U.; Yoo, I. [and others
1995-12-31
The neural network technique that is a field of artificial intelligence (AI) has proved to be a good model classifier in all areas of engineering and especially, it has gained a considerable acceptance in well test interpretation model (WTIM) identification of petroleum engineering. Conventionally, identification of the WTIM has been approached by graphical analysis method that requires an experienced expert. Recently, neural network technique equipped with back propagation (BP) learning algorithm was presented and it differs from the AI technique such as symbolic approach that must be accompanied with the data preparation procedures such as smoothing, segmenting, and symbolic transformation. In this paper, we developed BP neural network with Hough transform (HT) technique to overcome data selection problem and to use single neural network rather sequential nets. The Hough transform method was proved to be a powerful tool for the shape detection in image processing and computer vision technologies. Along these lines, a number of exercises were conducted with the actual well test data in two steps. First, the newly developed AI model, namely, ANNIS (Artificial intelligence Neural Network Identification System) was utilized to identify WTIM. Secondly, we obtained reservoir characteristics with the well test model equipped with modified Levenberg-Marquart method. The results show that ANNIS was proved to be quite reliable model for the data having noisy, missing, and extraneous points. They also demonstrate that reservoir parameters were successfully estimated.
Burgarth, Daniel; Yuasa, Kazuya
2011-01-01
The aim of quantum system identification is to estimate the ingredients inside a black box, in which some quantum-mechanical unitary process takes place, by just looking at its input-output behavior. Here we establish a basic and general framework for quantum system identification, that allows us to classify how much knowledge about the quantum system is attainable, in principle, from a given experimental setup. Prior knowledge on some elements of the black box helps the system identification...
Dusek, Miloslav; Haderka, Ondrej; Hendrych, Martin; Myska, Robert
1998-01-01
A secure quantum identification system combining a classical identification procedure and quantum key distribution is proposed. Each identification sequence is always used just once and new sequences are ``refuelled'' from a shared provably secret key transferred through the quantum channel. Two identification protocols are devised. The first protocol can be applied when legitimate users have an unjammable public channel at their disposal. The deception probability is derived for the case of ...
Identification for automotive systems
Hjalmarsson, Håkan; Re, Luigi
2012-01-01
Increasing complexity and performance and reliability expectations make modeling of automotive system both more difficult and more urgent. Automotive control has slowly evolved from an add-on to classical engine and vehicle design to a key technology to enforce consumption, pollution and safety limits. Modeling, however, is still mainly based on classical methods, even though much progress has been done in the identification community to speed it up and improve it. This book, the product of a workshop of representatives of different communities, offers an insight on how to close the gap and exploit this progress for the next generations of vehicles.
RECURSIVE SYSTEM IDENTIFICATION
Institute of Scientific and Technical Information of China (English)
Han-Fu Chen
2009-01-01
Most of existing methods in system identification with possible exception of those for linear systems are off-line in nature, and hence are nonrecursive.This paper demonstrates the recent progress in recursive system identification.The recursive identifi-cation algorithms are presented not only for linear systems (multivariate ARMAX systems) but also for nonlinear systems such as the Hammerstein and Wiener systems, and the non-linear ARX systems.The estimates generated by the algorithms are online updated and converge a.s.to the true values as time tends to infinity.
Mixed Lp Estimators Variety for Model Order Reduction in Control Oriented System Identification
Directory of Open Access Journals (Sweden)
Christophe Corbier
2015-01-01
Full Text Available A new family of MLE type Lp estimators for model order reduction in dynamical systems identification is presented in this paper. A family of Lp distributions proposed in this work combines Lp2 (1
Li, Xingfeng; Coyle, Damien; Maguire, Liam; McGinnity, Thomas M; Benali, Habib
2011-07-01
In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study.
Ibnkahla, Mohamed
2012-12-01
Neural network (NN) approaches have been widely applied for modeling and identification of nonlinear multiple-input multiple-output (MIMO) systems. This paper proposes a stochastic analysis of a class of these NN algorithms. The class of MIMO systems considered in this paper is composed of a set of single-input nonlinearities followed by a linear combiner. The NN model consists of a set of single-input memoryless NN blocks followed by a linear combiner. A gradient descent algorithm is used for the learning process. Here we give analytical expressions for the mean squared error (MSE), explore the stationary points of the algorithm, evaluate the misadjustment error due to weight fluctuations, and derive recursions for the mean weight transient behavior during the learning process. The paper shows that in the case of independent inputs, the adaptive linear combiner identifies the linear combining matrix of the MIMO system (to within a scaling diagonal matrix) and that each NN block identifies the corresponding unknown nonlinearity to within a scale factor. The paper also investigates the particular case of linear identification of the nonlinear MIMO system. It is shown in this case that, for independent inputs, the adaptive linear combiner identifies a scaled version of the unknown linear combining matrix. The paper is supported with computer simulations which confirm the theoretical results.
Physics-based mathematical models for quantum devices via experimental system identification
Energy Technology Data Exchange (ETDEWEB)
Schirmer, S G; Oi, D K L; Devitt, S J [Department of Applied Maths and Theoretical Physics, University of Cambridge, Wilberforce Rd, Cambridge, CB3 0WA (United Kingdom); SUPA, Department of Physics, University of Strathclyde, Glasgow G4 0NG (United Kingdom); National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430 (Japan)], E-mail: sgs29@cam.ac.uk
2008-03-15
We consider the task of intrinsic control system identification for quantum devices. The problem of experimental determination of subspace confinement is considered, and simple general strategies for full Hamiltonian identification and decoherence characterization of a controlled two-level system are presented.
Yang, Wu; Liu, Li; Zhou, Si-Da; Ma, Zhi-Sai
2015-10-01
This work proposes a Moving Kriging (MK) shape function modeling method for modal identification of linear time-varying (LTV) structural systems based on vector time-dependent autoregressive moving average (VTARMA) models. It aims to avoid the functional subspaces selection of the conventional functional series VTARMA (FS-VTARMA) models. Instead of the common basis functions, it constructs the time-varying coefficients on the time nodes with the MK shape functions in a compact support domain. The merit of the MK shape function is to determine its shape parameters upon vector random vibration signals adaptively. Model identification is effectively dealt with through an optimization scheme that decomposes the identification problem into two subproblems: estimating model parameters via two-stage least squares (2SLS) method and estimating shape function parameters via a discrete-continuous-variable hybrid optimization. In addition, the model order selection is achieved by the optimization scheme. This method has been validated by a Monte Carlo study of simulation case and further by an experimental test case, and the performance and potential advantages are illustrated.
Prediction of Hemodynamic Response to Epinephrine via Model-Based System Identification.
Bighamian, Ramin; Soleymani, Sadaf; Reisner, Andrew T; Seri, Istvan; Hahn, Jin-Oh
2016-01-01
In this study, we present a system identification approach to the mathematical modeling of hemodynamic responses to vasopressor-inotrope agents. We developed a hybrid model called the latency-dose-response-cardiovascular (LDC) model that incorporated 1) a low-order lumped latency model to reproduce the delay associated with the transport of vasopressor-inotrope agent and the onset of physiological effect, 2) phenomenological dose-response models to dictate the steady-state inotropic, chronotropic, and vasoactive responses as a function of vasopressor-inotrope dose, and 3) a physiological cardiovascular model to translate the agent's actions into the ultimate response of blood pressure. We assessed the validity of the LDC model to fit vasopressor-inotrope dose-response data using data collected from five piglet subjects during variable epinephrine infusion rates. The results suggested that the LDC model was viable in modeling the subjects' dynamic responses: After tuning the model to each subject, the r (2) values for measured versus model-predicted mean arterial pressure were consistently higher than 0.73. The results also suggested that intersubject variability in the dose-response models, rather than the latency models, had significantly more impact on the model's predictive capability: Fixing the latency model to population-averaged parameter values resulted in r(2) values higher than 0.57 between measured versus model-predicted mean arterial pressure, while fixing the dose-response model to population-averaged parameter values yielded nonphysiological predictions of mean arterial pressure. We conclude that the dose-response relationship must be individualized, whereas a population-averaged latency-model may be acceptable with minimal loss of model fidelity.
System identification modelling of ship manoeuvring motion based onε- support vector regression
Institute of Scientific and Technical Information of China (English)
王雪刚; 邹早建; 侯先瑞; 徐锋
2015-01-01
Based on theε-support vector regression, three modelling methods for the ship manoeuvring motion, i.e., the white-box modelling, the grey-box modelling and the black-box modelling, are investigated. Theoo10/10,oo20/20 zigzag tests and the o35 turning circle manoeuvre are simulated. Part of the simulation data for theoo20/20 zigzag test are used to train the support vectors, and the trained support vector machine is used to predict the wholeoo20/20 zigzag test. Comparison between the simula- ted and predictedoo20/20 zigzag test shows a good predictive ability of the three modelling methods. Then all mathematical models obtained by the modelling methods are used to predict theoo10/10 zigzag test ando35 turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the modelling methods are analyzed and compared with each other in terms of the application conditions, the prediction accuracy and the computation speed. An appropriate modelling method can be chosen according to the intended use of the mathematical models and the available data for the system identification.
Dynamic fuel cell stack model for real-time simulation based on system identification
Energy Technology Data Exchange (ETDEWEB)
Meiler, M.; Schmid, O.; Schudy, M. [Department of MEA and Stack Technology, DaimlerChrysler AG, Neue Str. 95, D-73230 Kirchheim/Teck (Germany); Hofer, E.P. [Department of Measurement, Control and Microtechnology, University of Ulm, Albert-Einstein-Allee 41, D-89081 Ulm (Germany)
2008-02-01
The authors have been developing an empirical mathematical model to predict the dynamic behaviour of a polymer electrolyte membrane fuel cell (PEMFC) stack. Today there is a great number of models, describing steady-state behaviour of fuel cells by estimating the equilibrium voltage for a certain set of operating parameters, but models capable of predicting the transient process between two steady-state points are rare. However, in automotive applications round about 80% of operating situations are dynamic. To improve the reliability of fuel cell systems by model-based control for real-time simulation dynamic fuel cell stack model is needed. Physical motivated models, described by differential equations, usually are complex and need a lot of computing time. To meet the real-time capability the focus is set on empirical models. Fuel cells are highly nonlinear systems, so often used auto-regressive (AR), output-error (OE) or Box-Jenkins (BJ) models do not accomplish satisfying accuracy. Best results are achieved by splitting the behaviour into a nonlinear static and a linear dynamic subsystem, a so-called Uryson-Model. For system identification and model validation load steps with different amplitudes are applied to the fuel cell stack at various operation points and the voltage response is recorded. The presented model is implemented in MATLAB environment and has a computing time of less than 1 ms per step on a standard desktop computer with a 2.8 MHz CPU and 504 MB RAM. Lab tests are carried out at DaimlerChrysler R and D Centre with DaimlerChrysler PEMFC hardware and a good agreement is found between model simulations and lab tests. (author)
Dynamic fuel cell stack model for real-time simulation based on system identification
Meiler, M.; Schmid, O.; Schudy, M.; Hofer, E. P.
The authors have been developing an empirical mathematical model to predict the dynamic behaviour of a polymer electrolyte membrane fuel cell (PEMFC) stack. Today there is a great number of models, describing steady-state behaviour of fuel cells by estimating the equilibrium voltage for a certain set of operating parameters, but models capable of predicting the transient process between two steady-state points are rare. However, in automotive applications round about 80% of operating situations are dynamic. To improve the reliability of fuel cell systems by model-based control for real-time simulation dynamic fuel cell stack model is needed. Physical motivated models, described by differential equations, usually are complex and need a lot of computing time. To meet the real-time capability the focus is set on empirical models. Fuel cells are highly nonlinear systems, so often used auto-regressive (AR), output-error (OE) or Box-Jenkins (BJ) models do not accomplish satisfying accuracy. Best results are achieved by splitting the behaviour into a nonlinear static and a linear dynamic subsystem, a so-called Uryson-Model. For system identification and model validation load steps with different amplitudes are applied to the fuel cell stack at various operation points and the voltage response is recorded. The presented model is implemented in MATLAB environment and has a computing time of less than 1 ms per step on a standard desktop computer with a 2.8 MHz CPU and 504 MB RAM. Lab tests are carried out at DaimlerChrysler R&D Centre with DaimlerChrysler PEMFC hardware and a good agreement is found between model simulations and lab tests.
Institute of Scientific and Technical Information of China (English)
XIE Hong; HE Yi-gang; ZENG Guan-da
2006-01-01
This paper presents the hybrid model identification for a class of nonlinear circuits and systems via a combination of the block-pulse function transform with the Volterra series.After discussing the method to establish the hybrid model and introducing the hybrid model identification,a set of relative formulas are derived for calculating the hybrid model and computing the Volterra series solution of nonlinear dynamic circuits and systems.In order to significantly reduce the computation cost for fault location,the paper presents a new fault diagnosis method based on multiple preset models that can be realized online.An example of identification simulation and fault diagnosis are given.Results show that the method has high accuracy and efficiency for fault location of nonlinear dynamic circuits and systems.
Dynamic Flow Modeling Using Double POD and ANN-ARX System Identification
Siegel, Stefan; Seidel, Jürgen; Cohen, Kelly; Aradag, Selin; McLaughlin, Thomas
2007-11-01
Double Proper Orthogonal Decomposition (DPOD), a modification of conventional POD, is a powerful tool for modeling of transient flow field spatial features, in particular, a 2D cylinder wake at a Reynolds number of 100. To develop a model for control design, the interaction of DPOD mode amplitudes with open-loop control inputs needs to be captured. Traditionally, Galerkin projection onto the Navier Stokes equations has been used for that purpose. Given the stability problems as well as issues in correctly modeling actuation input, we propose a different approach. We demonstrate that the ARX (Auto Regressive eXternal input) system identification method in connection with an Artificial Neural Network (ANN) nonlinear structure leads to a model that captures the dynamic behavior of the unforced and transient forced open loop data used for model development. Moreover, we also show that the model is valid at different Reynolds numbers, for different open loop forcing parameters, as well as for closed loop flow states with excellent accuracy. Thus, we present with this DPOD-ANN-ARX model a paradigm shift for laminar circular cylinder wake modeling that is proven valid for feedback flow controller development.
Energy Technology Data Exchange (ETDEWEB)
Kanellos, F.D. [Power Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens 9, Iroon Polytecheniou Street, 157 72, Zografou Campus, Athens (Greece); Hellenic Transmission System Operator, 72, Kastoros, 18545 Athens (Greece); Tsekouras, G.J. [Department of Electrical Engineering and Computer Science, Hellenic Naval Academy, Terma Hatzikiriaku, 18539 Piraeus (Greece); Hatziargyriou, N.D. [Power Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens 9, Iroon Polytecheniou Street, 157 72, Zografou Campus, Athens (Greece)
2011-02-15
In this paper models of Wind Parks (WPs) appropriate for simulation purposes of large power systems with high wind power penetration are developed. The proposed models of the WPs are developed using system identification theory. Data obtained from the simulation of detailed WP models are used for system identification. The obtained models are general and they can be applied to different configurations of WPs as only system's input/output data are used and not any internal states of the model. Simulation results confirm the accuracy and the advantages of the proposed WP equivalent models. (author)
Efficient Parameterization for Grey-box Model Identification of Complex Physical Systems
DEFF Research Database (Denmark)
Blanke, Mogens; Knudsen, Morten Haack
2006-01-01
Grey box model identification preserves known physical structures in a model but with limits to the possible excitation, all parameters are rarely identifiable, and different parametrizations give significantly different model quality. Convenient methods to show which parameterizations are the be...... that need be constrained to achieve satisfactory convergence. Identification of nonlinear models for a ship illustrate the concept.......Grey box model identification preserves known physical structures in a model but with limits to the possible excitation, all parameters are rarely identifiable, and different parametrizations give significantly different model quality. Convenient methods to show which parameterizations...... are the better would be very useful. This paper shows how we can assess the parameter interdependence and model quality. Hessian matrix decomposition is employed to show linear dependencies between variables and to put a quality tag on different parameterizations. The method determines parameter relations...
Computational model for supporting SHM systems design: Damage identification via numerical analyses
Sartorato, Murilo; de Medeiros, Ricardo; Vandepitte, Dirk; Tita, Volnei
2017-02-01
This work presents a computational model to simulate thin structures monitored by piezoelectric sensors in order to support the design of SHM systems, which use vibration based methods. Thus, a new shell finite element model was proposed and implemented via a User ELement subroutine (UEL) into the commercial package ABAQUS™. This model was based on a modified First Order Shear Theory (FOST) for piezoelectric composite laminates. After that, damaged cantilever beams with two piezoelectric sensors in different positions were investigated by using experimental analyses and the proposed computational model. A maximum difference in the magnitude of the FRFs between numerical and experimental analyses of 7.45% was found near the resonance regions. For damage identification, different levels of damage severity were evaluated by seven damage metrics, including one proposed by the present authors. Numerical and experimental damage metrics values were compared, showing a good correlation in terms of tendency. Finally, based on comparisons of numerical and experimental results, it is shown a discussion about the potentials and limitations of the proposed computational model to be used for supporting SHM systems design.
Quantum system identification.
Burgarth, Daniel; Yuasa, Kazuya
2012-02-24
The aim of quantum system identification is to estimate the ingredients inside a black box, in which some quantum-mechanical unitary process takes place, by just looking at its input-output behavior. Here we establish a basic and general framework for quantum system identification, that allows us to classify how much knowledge about the quantum system is attainable, in principle, from a given experimental setup. We show that controllable closed quantum systems can be estimated up to unitary conjugation. Prior knowledge on some elements of the black box helps the system identification. We present an example in which a Bell measurement is more efficient to identify the system. When the topology of the system is known, the framework enables us to establish a general criterion for the estimability of the coupling constants in its Hamiltonian.
Efficient Parameterization for Grey-box Model Identification of Complex Physical Systems
DEFF Research Database (Denmark)
Blanke, Mogens; Knudsen, Morten
2006-01-01
Grey box model identification preserves known physical structures in a model but with limits to the possible excitation, all parameters are rarely identifiable, and different parametrizations give significantly different model quality. Convenient methods to show which parameterizations...... are the better would be very useful. This paper shows how we can assess the parameter interdependence and model quality. Hessian matrix decomposition is employed to show linear dependencies between variables and to put a quality tag on different parameterizations. The method determines parameter relations...... that need be constrained to achieve satisfactory convergence. Identification of nonlinear models for a ship illustrate the concept....
Lee, Jongwon; Lee, Yong Wan; O'Clock, George; Zhu, Xiaoming; Parhi, Keshab K; Warwick, Warren J
2009-01-01
High frequency chest compression (HFCC) treatment systems are used to promote mucus transport and mitigate pulmonary system clearance problems to remove sputum from the airways in patients with Cystic Fibrosis (CF) and at risk of developing chronic obstructive pulmonary disease (COPD). Every HFCC system consists of a pump generator, one or two hoses connected to a vest, to deliver the pulsation. There are three different waveforms in use; symmetric sine, the asymmetric sine and the trapezoid waveforms. There have been few studies that compared the efficacy of a sine waveform with the HFCC pulsations. In this study we present a model of the respiratory system for a young normal subject who is one of co-authors. The input signal is the pressure applied by the vest to chest, at a frequency of 6Hz. Using the system model simulation, the effectiveness of different source waveforms is evaluated and compared by observing the waveform response associated with air flow at the mouth. Also the study demonstrated that the ideal rectangle wave produced the maximum peak air flow, and followed by the trapezoid, triangle and sine waveform. The study suggests that a pulmonary system evaluation or modeling effort for CF patient might be useful as a method to optimize frequency and waveform structure choices for HFCC therapeutic intervention.
MODEL IDENTIFICATION AND COMPUTER ALGEBRA.
Bollen, Kenneth A; Bauldry, Shawn
2010-10-07
Multiequation models that contain observed or latent variables are common in the social sciences. To determine whether unique parameter values exist for such models, one needs to assess model identification. In practice analysts rely on empirical checks that evaluate the singularity of the information matrix evaluated at sample estimates of parameters. The discrepancy between estimates and population values, the limitations of numerical assessments of ranks, and the difference between local and global identification make this practice less than perfect. In this paper we outline how to use computer algebra systems (CAS) to determine the local and global identification of multiequation models with or without latent variables. We demonstrate a symbolic CAS approach to local identification and develop a CAS approach to obtain explicit algebraic solutions for each of the model parameters. We illustrate the procedures with several examples, including a new proof of the identification of a model for handling missing data using auxiliary variables. We present an identification procedure for Structural Equation Models that makes use of CAS and that is a useful complement to current methods.
Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza
2014-10-01
The purpose of this study was to estimate the torque from high-density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate-to-high isometric elbow flexion-extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro-fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro-fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black-box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG-Torque modeling in clinical applications.
Burgarth, Daniel
2011-01-01
The aim of quantum system identification is to estimate the ingredients inside a black box, in which some quantum-mechanical unitary process takes place, by just looking at its input-output behavior. Here we establish a basic and general framework for quantum system identification, that allows us to classify how much knowledge about the quantum system is attainable, in principle, from a given experimental setup. Prior knowledge on some elements of the black box helps the system identification. We present an example in which a Bell measurement is more efficient to identify the system. When the topology of the system is known, the framework enables us to establish a general criterion for the estimability of the coupling constants in its Hamiltonian.
Identification of physical models
DEFF Research Database (Denmark)
Melgaard, Henrik
1994-01-01
The problem of identification of physical models is considered within the frame of stochastic differential equations. Methods for estimation of parameters of these continuous time models based on descrete time measurements are discussed. The important algorithms of a computer program for ML or MAP...... design of experiments, which is for instance the design of an input signal that are optimal according to a criterion based on the information provided by the experiment. Also model validation is discussed. An important verification of a physical model is to compare the physical characteristics...... of the model with the available prior knowledge. The methods for identification of physical models have been applied in two different case studies. One case is the identification of thermal dynamics of building components. The work is related to a CEC research project called PASSYS (Passive Solar Components...
System Identification Tools for Control Structure Interaction
1990-01-01
DT! FILE COPY AL-TR-89-054 AD: 00 Final Report System Identification Tools for O for the period - September 1988 to Control Structure Interaction May...Classification) System Identification Tools for Control Structure Interaction (U) 12. PERSONAL AUTHOR(S) Kosut, Robert L.; Kabuli, Guntekin M. 13a. TYPE OF...identification, dynamics, 22 01 system identification , robustness, dynamic modeling, robust 22 02 control design, control design procedure 19. ABSTRACT
Institute of Scientific and Technical Information of China (English)
郭恒昌
1991-01-01
Sequential diagnosis is a very useful strategy for system-level fault identification because of its lower cost of hardware.In this paper,the characterization of sequentially t-diagnosable system is given,and a universal algorithm to seek faulty units in the system is developed.
Directory of Open Access Journals (Sweden)
Kyu-Sik Park
2015-01-01
Full Text Available Hanger cables in suspension bridges are partly constrained by horizontal clamps. So, existing tension estimation methods based on a single cable model are prone to higher errors as the cable gets shorter, making it more sensitive to flexural rigidity. Therefore, inverse analysis and system identification methods based on finite element models are suggested recently. In this paper, the applicability of system identification methods is investigated using the hanger cables of Gwang-An bridge. The test results show that the inverse analysis and systemic identification methods based on finite element models are more reliable than the existing string theory and linear regression method for calculating the tension in terms of natural frequency errors. However, the estimation error of tension can be varied according to the accuracy of finite element model in model based methods. In particular, the boundary conditions affect the results more profoundly when the cable gets shorter. Therefore, it is important to identify the boundary conditions through experiment if it is possible. The FE model-based tension estimation method using system identification method can take various boundary conditions into account. Also, since it is not sensitive to the number of natural frequency inputs, the availability of this system is high.
Kravitz, B.; MacMartin, D. G.; Rasch, P. J.; Wang, H.
2015-12-01
Identifying the influence of radiative forcing on hydrological cycle changes in climate models can be challenging due to low signal-to-noise ratios, particularly for regional changes. One method of improving the signal-to-noise ratio, even for short simulations, is to use techniques from engineering, broadly known as system identification. Through this method, forcing (or any other chosen field) in multiple regions in a climate model is perturbed simultaneously by using mutually uncorrelated signals with a chosen frequency content, depending upon the climate behavior one wishes to reveal. The result is the sensitivity of a particular climate field (e.g., temperature, precipitation, or cloud cover) to changes in any perturbed region. We demonstrate this technique in the Community Earth System Model (CESM). We perturbed surface air temperatures in 22 regions by up to 1°C. The amount of temperature perturbation was changed every day corresponding to a predetermined sequence of random numbers between -1 and 1, filtered to contain particular frequency content. The matrix of sequences was then orthogonalized such that all individual sequences were mutually uncorrelated. We performed CESM simulations with both fixed sea surface temperatures and a fully coupled ocean. We discuss the various patterns of climate response in several fields relevant to the hydrological cycle, including precipitation and surface latent heat fluxes. We also discuss the potential limits of this technique in terms of the spatial and temporal scales over which it would be appropriate to use.
Energy Technology Data Exchange (ETDEWEB)
Sinha, S.; Matsumoto, Tsuyoshi; Kojima, Toshinori [Seikei University, Tokyo (Japan). Dept. of Industrial Chemistry; Sanjay Kumar [Kyoto University (Japan). Dept. of Global Environment Engineering
2001-03-01
Uncertainties in local solar radiation, ambient temperature and thermal load data have been one of the major factors limiting the reliability and efficiency of solar thermal hybrid systems. In the present paper, moving average auto regressive erogenous (ARX) model based reasoning has been mooted and modified to include moving average method, as an effective tool for predictions of these data. The results show that the method is quite robust and is capable of predicting fairly accurate results, which would make these systems more viable in areas where meteorological data are not available or vague. (author)
Sandu, Corina; Andersen, Erik R.; Southward, Steve
2011-02-01
In this paper, we develop a multibody dynamics model of a quarter-car test-rig equipped with a McPherson strut suspension and we apply a system identification technique on it. Constrained equations of motion in the Lagrange multiplier form are derived and employed to characterise the dynamic behaviour of the test rig modelled once as a linear system and once as a non-linear system. The system of differential algebraic equations is integrated using a Hilber-Hughes-Taylor integrator. The responses of both models (linear and non-linear) to a given displacement input are obtained and compared with the experimental response recorded using the physical quarter-car test rig equipped with a McPherson strut suspension. The system identification is performed for control purposes. The results, as well as the performance and area of applicability of the test rig models derived, are discussed.
System identification. [of space structures
Juang, Jer-Nan
1993-01-01
Major issues in system identification are summarized and recent advances are reviewed. Modal testing and system identification used in control theory are examined, and the mathematical relationships and conversions of the models appropriate to modal testing and those appropriate to modern control design methods are discussed. The importance of obtaining input and output matrices in modal testing is emphasized, and the changes that may be needed in modal testing procedures to meet the needs of the control system designer are addressed. Directions for future research are considered.
Computer system identification
Lesjak, Borut
2008-01-01
The concept of computer system identity in computer science bears just as much importance as does the identity of an individual in a human society. Nevertheless, the identity of a computer system is incomparably harder to determine, because there is no standard system of identification we could use and, moreover, a computer system during its life-time is quite indefinite, since all of its regular and necessary hardware and software upgrades soon make it almost unrecognizable: after a number o...
Optimal Inputs for System Identification.
1995-09-01
The derivation of the power spectral density of the optimal input for system identification is addressed in this research. Optimality is defined in...identification potential of general System Identification algorithms, a new and efficient System Identification algorithm that employs Iterated Weighted Least
Trends and progress in system identification
Eykhoff, Pieter
1981-01-01
Trends and Progress in System Identification is a three-part book that focuses on model considerations, identification methods, and experimental conditions involved in system identification. Organized into 10 chapters, this book begins with a discussion of model method in system identification, citing four examples differing on the nature of the models involved, the nature of the fields, and their goals. Subsequent chapters describe the most important aspects of model theory; the """"classical"""" methods and time series estimation; application of least squares and related techniques for the e
Abed-Meriam, Karim; Qui, Wanzhi; Hua, Yingbo
1997-01-01
Blind system identification (BSI) is a fundamental signal processing technology aimed at retrieving a system's unknown information from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation, and blind image restoration. This paper reviews a number of recently developed concepts and techniques for BSI, which include the concept of blind system identifiability in a deterministic framework, the blind techniques...
Egli, Nicole M; Champod, Christophe; Margot, Pierre
2007-04-11
Recent challenges and errors in fingerprint identification have highlighted the need for assessing the information content of a papillary pattern in a systematic way. In particular, estimation of the statistical uncertainty associated with this type of evidence is more and more called upon. The approach used in the present study is based on the assessment of likelihood ratios (LRs). This evaluative tool weighs the likelihood of evidence given two mutually exclusive hypotheses. The computation of likelihood ratios on a database of marks of known sources (matching the unknown and non-matching the unknown mark) allows an estimation of the evidential contribution of fingerprint evidence. LRs are computed taking advantage of the scores obtained from an automated fingerprint identification system and hence are based exclusively on level II features (minutiae). The AFIS system attributes a score to any comparison (fingerprint to fingerprint, mark to mark and mark to fingerprint), used here as a proximity measure between the respective arrangements of minutiae. The numerator of the LR addresses the within finger variability and is obtained by comparing the same configurations of minutiae coming from the same source. Only comparisons where the same minutiae are visible both on the mark and on the print are therefore taken into account. The denominator of the LR is obtained by cross-comparison with a database of prints originating from non-matching sources. The estimation of the numerator of the LR is much more complex in terms of specific data requirements than the estimation of the denominator of the LR (that requires only a large database of prints from an non-associated population). Hence this paper addresses specific issues associated with the numerator or within finger variability. This study aims at answering the following questions: (1) how a database for modelling within finger variability should be acquired; (2) whether or not the visualisation technique or the
Raginsky, M
2003-01-01
We formulate and study, in general terms, the problem of quantum system identification, i.e., the determination (or estimation) of unknown quantum channels through their action on suitably chosen input density operators. We also present a quantitative analysis of the worst-case performance of these schemes.
Mastering system identification in 100 exercises
Schoukens, J; Rolain, Yves
2012-01-01
"This book enables readers to understand system identification and linear system modeling through 100 practical exercises without requiring complex theoretical knowledge. The contents encompass state-of-the-art system identification methods, with both time and frequency domain system identification methods covered, including the pros and cons of each. Each chapter features MATLAB exercises, discussions of the exercises, accompanying MATLAB downloads, and larger projects that serve as potential assignments in this learn-by-doing resource"--
DEFF Research Database (Denmark)
Ursem, Rasmus Kjær
optimization. In addition to general investigations in these areas, I introduce a number of algorithms and demonstrate their potential on real-world problems in system identification and control. Furthermore, I investigate dynamic optimization problems in the context of the three fundamental areas as well...
Performance Modelling of Automatic Identification System with Extended Field of View
DEFF Research Database (Denmark)
Lauersen, Troels; Mortensen, Hans Peter; Pedersen, Nikolaj Bisgaard
2010-01-01
This paper deals with AIS (Automatic Identification System) behavior, to investigate the severity of packet collisions in an extended field of view (FOV). This is an important issue for satellite-based AIS, and the main goal is a feasibility study to find out to what extent an increased FOV...
System for tamper identification
Energy Technology Data Exchange (ETDEWEB)
Bobbitt, III, John Thomas; Weeks, George E.
2017-09-05
A system for tamper identification. A fastener has a tamper identification surface with a unique grain structure that is altered if the fastener is removed or otherwise exposed to sufficient torque. After a period of time such as e.g., shipment and/or storage of the sealed container, a determination of whether tampering has occurred can be undertaken by examining the grain structure to determine if it has changed since the fastener was used to seal the container or secure the device.
Optimized Experiment Design for Marine Systems Identification
DEFF Research Database (Denmark)
Blanke, M.; Knudsen, Morten
1999-01-01
Simulation of maneuvring and design of motion controls for marine systems require non-linear mathematical models, which often have more than one-hundred parameters. Model identification is hence an extremely difficult task. This paper discusses experiment design for marine systems identification...
Directory of Open Access Journals (Sweden)
Mosbeh R. Kaloop
2016-10-01
Full Text Available The present study investigates the prediction efficiency of nonlinear system-identification models, in assessing the behavior of a coupled structure-passive vibration controller. Two system-identification models, including Nonlinear AutoRegresive with eXogenous inputs (NARX and adaptive neuro-fuzzy inference system (ANFIS, are used to model the behavior of an experimentally scaled three-story building incorporated with a tuned mass damper (TMD subjected to seismic loads. The experimental study is performed to generate the input and output data sets for training and testing the designed models. The parameters of root-mean-squared error, mean absolute error and determination coefficient statistics are used to compare the performance of the aforementioned models. A TMD controller system works efficiently to mitigate the structural vibration. The results revealed that the NARX and ANFIS models could be used to identify the response of a controlled structure. The parameters of both two time-delays of the structure response and the seismic load were proven to be effective tools in identifying the performance of the models. A comparison based on the parametric evaluation of the two methods showed that the NARX model outperforms the ANFIS model in identifying structures response.
Remote Intelligent Identification System of Structural Damage
Institute of Scientific and Technical Information of China (English)
RAO Wenbi; ZHANG Xiang; Bostrm Henrik
2004-01-01
The focus of this paper is to build the damage identify system, which performs "system identification" to detect the positions and extents of structural damages.The identification of structural damage can be characterized as a nonlinear process which linear prediction models such as linear regression are not suitable.However, neural network techniques may provide an effective tool for system identification.The method of damage identification using the radial basis function neural network (RBFNN) is presented in this paper.Using this method, a simple reinforced concrete structure has been tested both in the absence and presence of noise.The results show that the RBFNN identification technology can be used with related success for the solution of dynamic damage identification problems, even in the presence of a noisy identify data.Furthermore, a remote identification system based on that is set up with Java Technologies.
Pan, Jianqiang
1992-01-01
Several important problems in the fields of signal processing and model identification, such as system structure identification, frequency response determination, high order model reduction, high resolution frequency analysis, deconvolution filtering, and etc. Each of these topics involves a wide range of applications and has received considerable attention. Using the Fourier based sinusoidal modulating signals, it is shown that a discrete autoregressive model can be constructed for the least squares identification of continuous systems. Some identification algorithms are presented for both SISO and MIMO systems frequency response determination using only transient data. Also, several new schemes for model reduction were developed. Based upon the complex sinusoidal modulating signals, a parametric least squares algorithm for high resolution frequency estimation is proposed. Numerical examples show that the proposed algorithm gives better performance than the usual. Also, the problem was studied of deconvolution and parameter identification of a general noncausal nonminimum phase ARMA system driven by non-Gaussian stationary random processes. Algorithms are introduced for inverse cumulant estimation, both in the frequency domain via the FFT algorithms and in the domain via the least squares algorithm.
Nonlinear Model Identification from Operating Records.
1980-11-01
34, Submitted July 1979 to Proc. IEEE. [13] Wellstead , P., "Model Order Identification Using an Auxillary System," Proc. IEEE, vol. 123, No. 12, December...C and Systems, Nov. 1979 . I I ~I lt( -~ I -l.. .... .. . ... . .. . . , _. . - -"
System identification for robust control design
Energy Technology Data Exchange (ETDEWEB)
Dohner, J.L.
1995-04-01
System identification for the purpose of robust control design involves estimating a nominal model of a physical system and the uncertainty bounds of that nominal model via the use of experimentally measured input/output data. Although many algorithms have been developed to identify nominal models, little effort has been directed towards identifying uncertainty bounds. Therefore, in this document, a discussion of both nominal model identification and bounded output multiplicative uncertainty identification will be presented. This document is divided into several sections. Background information relevant to system identification and control design will be presented. A derivation of eigensystem realization type algorithms will be presented. An algorithm will be developed for calculating the maximum singular value of output multiplicative uncertainty from measured data. An application will be given involving the identification of a complex system with aliased dynamics, feedback control, and exogenous noise disturbances. And, finally, a short discussion of results will be presented.
[Deterministic and stochastic identification of neurophysiologic systems].
Piatigorskiĭ, B Ia; Kostiukov, A I; Chinarov, V A; Cherkasskiĭ, V L
1984-01-01
The paper deals with deterministic and stochastic identification methods applied to the concrete neurophysiological systems. The deterministic identification was carried out for the system: efferent fibres-muscle. The obtained transition characteristics demonstrated dynamic nonlinearity of the system. Identification of the neuronal model and the "afferent fibres-synapses-neuron" system in mollusc Planorbis corneus was carried out using the stochastic methods. For these purpose the Wiener method of stochastic identification was expanded for the case of pulse trains as input and output signals. The weight of the nonlinear component in the Wiener model and accuracy of the model prediction were quantitatively estimated. The results obtained proves the possibility of using these identification methods for various neurophysiological systems.
Identification and Damage Detection on Structural Systems
DEFF Research Database (Denmark)
Brincker, Rune; Kirkegaard, Poul Henning; Andersen, Palle
1994-01-01
A short introduction is given to system identification and damage assessment in civil engineering structures. The most commonly used FFT-based techniques for system identification are mentioned, and the Random decrement technique and parametric methods based on ARMA models are introduced. Speed...
Directory of Open Access Journals (Sweden)
Huiyi Hu
2013-01-01
speed of the stochastic gradient algorithm. The key term separation principle can simplify the identification model of the input nonlinear system, and the decomposition technique can enhance computational efficiencies of identification algorithms. The simulation results show that the proposed algorithm is effective for estimating the parameters of IN-CARAR systems.
A Multiple-Model Approach for Synchronous Generator Nonlinear System Identification
Ahmadi, Seyed Salman; Karrari, Mehdi
2012-07-01
In this paper, a multiple model approach is proposed for the identification of synchronous generators. In the literature, the same structure often is used for all local models. Therefore, to obtain a precise model for the operating condition of the synchronous generator with severely nonlinear behavior, many local models are required. The proposed method determines the complexity of local models based on complexity of behavior of the synchronous generator at different operating conditions. There are two choices for increasing model precision at each iteration of the proposed method: (i) increasing the number of local models in one region, or (ii) increasing local model complexity in the same region. The proposed method has been tested on experimental data collected on a 3 kVA micro-machine. In the study, the field voltage is considered as the input and the active output power and the terminal voltage are considered as the outputs of the synchronous generator. The proposed method provides a more precise model with fewer parameters compared to some well known methods such as LOLIMOT and global polynomial models.
Modal identification of system driven by levy random excitation based on continuous time AR model
Institute of Scientific and Technical Information of China (English)
DU XiuLi; WANG FengQuan
2009-01-01
Based on the continuous time AR model,this paper presents a new time-domain modal identification namic equation is first transformed into the observation equation and the state equation(namely,stochastic differential equation).Based on the property of the strong solution of the stochastic differential equation,the uniformly modulated function is identified piecewise.Then by virtue of the Girsanov theorem,we present the exact maximum likelihood estimators of parameters.Finally,the modal parameters are identified by eigen analysis.Numerical results show that the method not only has high precision and robustness but also has very high computing efficiency.
System Identification and Simulation of a Triaxial Shaker System,
1996-01-01
methods. Results of the system identification process are discussed. Certain methods are found to produce models that are in good agreement with measured response data from the actual shaker system....implemented in the simulation. The first is a physically-based model derived from a finite element analysis together with a model-updating system ... identification scheme; the second is a parametric model without direct physical significance. The advantages and disadvantages of each model for this
In-Flight System Identification
Morelli, Eugene A.
1998-01-01
A method is proposed and studied whereby the system identification cycle consisting of experiment design and data analysis can be repeatedly implemented aboard a test aircraft in real time. This adaptive in-flight system identification scheme has many advantages, including increased flight test efficiency, adaptability to dynamic characteristics that are imperfectly known a priori, in-flight improvement of data quality through iterative input design, and immediate feedback of the quality of flight test results. The technique uses equation error in the frequency domain with a recursive Fourier transform for the real time data analysis, and simple design methods employing square wave input forms to design the test inputs in flight. Simulation examples are used to demonstrate that the technique produces increasingly accurate model parameter estimates resulting from sequentially designed and implemented flight test maneuvers. The method has reasonable computational requirements, and could be implemented aboard an aircraft in real time.
Energy Technology Data Exchange (ETDEWEB)
Park, H. W.; Jeon, Y. S. [KAERI, Taejon (Korea, Republic of)
2002-10-01
Nonlinear hysteretic behaviors and stiffness changes of a seismic isolator are identified by using a Time Domain System Identification (TDSI) based on the secant stiffness model. A new regularity condition of tangent stiffness used in the current TDSI is proposed instead of that used in the conventional Duhem hysteretic operator. The proposed regularity condition is defined with respect to time while that of Duhem hysteretic operator is defined with respect to displacements and restoring forces. The secant stiffness model for the TDSI is obtained by approximating the tangent stiffness under the proposed regularity condition by the secant stiffness at each time step. A least square method is employed to minimize the difference between the calculated response and measured response for the time domain system identification of the secant stiffness. The regularity condition of the secant stiffness is utilized to alleviate ill-posedness of the TDSI and to yield physically meaningful solutions by means of the regularization technique. An optimal regularization factor determined by Geometric Mean Scheme (GMS) is adopted to yield appropriate regularization effects on the system identification. The validity of the proposed method is presented through two numerical examples.
Application of system-identification by ARMarkov and sensitivity analysis to noise-amplifier models
Dovetta, Nicolas; Schmid, Peter; Sipp, Denis; McKeon, Beverley
2011-11-01
Separated flow often exhibit amplification of external noise sources via an interaction with shear layer instabilities. In order to manipulate this amplification process we consider a data-based control design strategy. The first step is to build a state-space representation of the input-output transfer function. An auto-regressive representation is used that explicitly includes Markov parameters (ARMarkov). This is then coupled with the eigensystem realization algorithm (ERA) which yields a reduced-order state-space representation of the problem. In real experiments the data is contaminated by measurement noise or by non-linearities which are not accounted for by the present approach. In order to enforce robustness of the identification-realization procedure a sensitivity analysis of the algorithm is performed. These sensitivities provide quantitative criteria to find the most robust way of identifying the system using the ARMarkov/ERA algorithm. The system-identification and sensitivity framework will be demonstrated on the Ginzburg-Landau equation. Support from the Partner University Fund (PUF) is gratefully acknowledged.
System Identification of Civil Engineering Structures using State Space and ARMAV Models
DEFF Research Database (Denmark)
Andersen, P.; Kirkegaard, Poul Henning; Brincker, Rune
In this paper the relations between an ambient excited structural system, represented by an innovation state space system, and the Auto-Regressive Moving Average Vector (ARMAV) model are considered. It is shown how to obtain a multivariate estimate of the ARMAV model from output measurements, usi...
Process Identification through Test on Cryogenic System
Pezzetti, M; Chadli, M; Coppier, H
2008-01-01
UNICOS (UNified Industrial Control System) is the CERN object-based control standard for the cryogenics of the LHC and its experiments. It includes a variety of embedded functions, dedicated to the specific cryogenic processes. To enlarge the capabilities of the standard it is proposed to integrate the parametrical identification step in the control system of large scale cryogenic plants. Different methods of parametrical identification have been tested and the results were combined to obtain a better model. The main objective of the work is to find a compromise between an easy-to-use solution and a good level of process identification model. The study focuses on identification protocol for large delayed system, the measurement consistency and correlation between different inputs and outputs. Furthermore the paper describes in details, the results and the tests carried out on parametrical identification investigations with large scale systems.
基于NARMAX模型的Hopfield网络系统辨识%System identification based on NARMAX model using Hopfield networks
Institute of Scientific and Technical Information of China (English)
石宏理; 蔡远利; 邱祖廉
2006-01-01
An approach is proposed to avoid model structure determination in system identification using NARMAX (nonlinear autoregressive moving average with exogenous inputs) model.Identification procedure is formulated as an optimization procedure of a special class of Hopfield network in the proposed approach.The particular structure of these Hopfield networks can avoid the local optimum problem.Training of these Hopfield network achieves model structure determination and parameter estimation. Convergence of Hopfield networks guarantees that a NARMAX model of random initial state will approach a valid identification model with accurate state parameters.Results of two simulation examples illustrate that this approach is efficient and simple.
Fuzzy Identification Based on T-S Fuzzy Model and Its Application for SCR System
Zeng, Fanchun; Zhang, Bin; Zhang, Lu; Ji, Jinfu; Jin, Wenjing
An improved T-S model was introduced to identify the model of SCR system. Model structure was selected by physical analyzes and mathematics tests. Three different clustering algorithms were introduced to obtain space partitions. Then, space partitions were amended by mathematics methods. At last, model parameters were identified by least square method. Train data was sampled in 1000MW coal-fired unit SCR system. T-S model of it is identified by three cluster methods. Identify results are proved effective. The merit and demerit among them are analyzed in the end.
Őri, Zsolt P
2016-08-03
A mathematical model has been developed to facilitate indirect measurements of difficult to measure variables of the human energy metabolism on a daily basis. The model performs recursive system identification of the parameters of the metabolic model of the human energy metabolism using the law of conservation of energy and principle of indirect calorimetry. Self-adaptive models of the utilized energy intake prediction, macronutrient oxidation rates, and daily body composition changes were created utilizing Kalman filter and the nominal trajectory methods. The accuracy of the models was tested in a simulation study utilizing data from the Minnesota starvation and overfeeding study. With biweekly macronutrient intake measurements, the average prediction error of the utilized carbohydrate intake was -23.2 ± 53.8 kcal/day, fat intake was 11.0 ± 72.3 kcal/day, and protein was 3.7 ± 16.3 kcal/day. The fat and fat-free mass changes were estimated with an error of 0.44 ± 1.16 g/day for fat and -2.6 ± 64.98 g/day for fat-free mass. The daily metabolized macronutrient energy intake and/or daily macronutrient oxidation rate and the daily body composition change from directly measured serial data are optimally predicted with a self-adaptive model with Kalman filter that uses recursive system identification.
Meimon, Serge; Petit, Cyril; Fusco, Thierry; Kulcsar, Caroline
2010-11-01
Adaptive optics (AO) systems have to correct tip-tilt (TT) disturbances down to a fraction of the diffraction-limited spot. This becomes a key issue for very or extremely large telescopes affected by mechanical vibration peaks or wind shake effects. Linear quadratic Gaussian (LQG) control achieves optimal TT correction when provided with the temporal model of the disturbance. We propose a nonsupervised identification procedure that does not require any auxiliary system or loop opening and validate it on synthetic profile as well as on experimental data.
On System Identification of Wind Turbines
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Perisic, Nevena; Pedersen, B.J.
Recently several methods have been proposed for the system identification of wind turbines which can be considered as a linear time-varying system due to the operating conditions. For the identification of linear wind turbine models, either black-box or grey-box identification can be used....... The operational model analysis (OMA) methodology can provide accurate estimates of the natural frequencies, damping ratios and mode shapes of the systems as long as the measurements have a low noise to signal ratio. However, in order to take information about the wind turbine into account a grey...
On System Identification of Wind Turbines
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Perisic, Nevena; Pedersen, B.J.
. The operational model analysis (OMA) methodology can provide accurate estimates of the natural frequencies, damping ratios and mode shapes of the systems as long as the measurements have a low noise to signal ratio. However, in order to take information about the wind turbine into account a grey......Recently several methods have been proposed for the system identification of wind turbines which can be considered as a linear time-varying system due to the operating conditions. For the identification of linear wind turbine models, either black-box or grey-box identification can be used...
Directory of Open Access Journals (Sweden)
Mattia Zanon
2013-01-01
Full Text Available Continuous glucose monitoring (CGM by suitable portable sensors plays a central role in the treatment of diabetes, a disease currently affecting more than 350 million people worldwide. Noninvasive CGM (NI-CGM, in particular, is appealing for reasons related to patient comfort (no needles are used but challenging. NI-CGM prototypes exploiting multisensor approaches have been recently proposed to deal with physiological and environmental disturbances. In these prototypes, signals measured noninvasively (e.g., skin impedance, temperature, optical skin properties, etc. are combined through a static multivariate linear model for estimating glucose levels. In this work, by exploiting a dataset of 45 experimental sessions acquired in diabetic subjects, we show that regularisation-based techniques for the identification of the model, such as the least absolute shrinkage and selection operator (better known as LASSO, Ridge regression, and Elastic-Net regression, improve the accuracy of glucose estimates with respect to techniques, such as partial least squares regression, previously used in the literature. More specifically, the Elastic-Net model (i.e., the model identified using a combination of and norms has the best results, according to the metrics widely accepted in the diabetes community. This model represents an important incremental step toward the development of NI-CGM devices effectively usable by patients.
Energy Technology Data Exchange (ETDEWEB)
Dinca, Laurian; Aldemir, Tunc; Rizzoni, Giorgio
1999-06-01
A probabilistic approach is presented which can be used for the estimation of system parameters and unmonitored state variables towards model-based fault diagnosis in dynamic systems. The method can be used with any type of input-output model and can accommodate noisy data and/or parameter/modeling uncertainties. The methodology is based on Markovian representation of system dynamics in discretized state space. The example system used for the illustration of the methodology focuses on the intake, fueling, combustion and exhaust components of internal combustion engines. The results show that the methodology is capable of estimating the system parameters and tracking the unmonitored dynamic variables within user-specified magnitude intervals (which may reflect noise in the monitored data, random changes in the parameters or modeling uncertainties in general) within data collection time and hence has potential for on-line implementation.
Laguerre-Volterra model and architecture for MIMO system identification and output prediction.
Li, Will X Y; Xin, Yao; Chan, Rosa H M; Song, Dong; Berger, Theodore W; Cheung, Ray C C
2014-01-01
A generalized mathematical model is proposed for behaviors prediction of biological causal systems with multiple inputs and multiple outputs (MIMO). The system properties are represented by a set of model parameters, which can be derived with random input stimuli probing it. The system calculates predicted outputs based on the estimated parameters and its novel inputs. An efficient hardware architecture is established for this mathematical model and its circuitry has been implemented using the field-programmable gate arrays (FPGAs). This architecture is scalable and its functionality has been validated by using experimental data gathered from real-world measurement.
Experimental System Identification and Black Box Modeling of Hydraulic Directional Control Valve
Directory of Open Access Journals (Sweden)
Sondre Sanden Tørdal
2015-10-01
Full Text Available Directional control valves play a large role in most hydraulic systems. When modeling the hydraulic systems, it is important that both the steady state and dynamic characteristics of the valves are modeled correctly to reproduce the dynamic characteristics of the entire system. In this paper, a proportional valve (Brevini HPV 41 is investigated to identify its dynamic and steady state characteristics. The steady state characteristics are identified by experimental flow curves. The dynamics are determined through frequency response analysis and identified using several transfer functions. The paper also presents a simulation model of the valve describing both steady state and dynamic characteristics. The simulation results are verified through several experiments.
Kobet, Robert A.; Pan, Xiaoping; Zhang, Baohong; Pak, Stephen C.; Asch, Adam S.; Lee, Myon-Hee
2014-01-01
The nematode Caenorhabditis elegans (C. elegans) offers a unique opportunity for biological and basic medical researches due to its genetic tractability and well-defined developmental lineage. It also provides an exceptional model for genetic, molecular, and cellular analysis of human disease-related genes. Recently, C. elegans has been used as an ideal model for the identification and functional analysis of drugs (or small-molecules) in vivo. In this review, we describe conserved oncogenic signaling pathways (Wnt, Notch, and Ras) and their potential roles in the development of cancer stem cells. During C. elegans germline development, these signaling pathways regulate multiple cellular processes such as germline stem cell niche specification, germline stem cell maintenance, and germ cell fate specification. Therefore, the aberrant regulations of these signaling pathways can cause either loss of germline stem cells or overproliferation of a specific cell type, resulting in sterility. This sterility phenotype allows us to identify drugs that can modulate the oncogenic signaling pathways directly or indirectly through a high-throughput screening. Current in vivo or in vitro screening methods are largely focused on the specific core signaling components. However, this phenotype-based screening will identify drugs that possibly target upstream or downstream of core signaling pathways as well as exclude toxic effects. Although phenotype-based drug screening is ideal, the identification of drug targets is a major challenge. We here introduce a new technique, called Drug Affinity Responsive Target Stability (DARTS). This innovative method is able to identify the target of the identified drug. Importantly, signaling pathways and their regulators in C. elegans are highly conserved in most vertebrates, including humans. Therefore, C. elegans will provide a great opportunity to identify therapeutic drugs and their targets, as well as to understand mechanisms underlying the
Qualitative System Identification from Imperfect Data
Coghill, George M; Srinivasan, Ashwin; 10.1613/jair.2374
2011-01-01
Experience in the physical sciences suggests that the only realistic means of understanding complex systems is through the use of mathematical models. Typically, this has come to mean the identification of quantitative models expressed as differential equations. Quantitative modelling works best when the structure of the model (i.e., the form of the equations) is known; and the primary concern is one of estimating the values of the parameters in the model. For complex biological systems, the model-structure is rarely known and the modeler has to deal with both model-identification and parameter-estimation. In this paper we are concerned with providing automated assistance to the first of these problems. Specifically, we examine the identification by machine of the structural relationships between experimentally observed variables. These relationship will be expressed in the form of qualitative abstractions of a quantitative model. Such qualitative models may not only provide clues to the precise quantitative ...
Feru, E.; Willems, F.P.T.; Rojer, C.; Jager, B. de; Steinbuch, M.
2013-01-01
To meet future CO2 emission targets, Waste Heat Recovery systems have recently attracted much attention for automotive applications, especially for long haul trucks. This paper focuses on the development of a dynamic counter-flow heat exchanger model for control purposes. The model captures the dyna
Feru, E.; Willems, F.P.T.; Rojer, C.; Jager, B. de; Steinbuch, M.
2013-01-01
To meet future CO2 emission targets, Waste Heat Recovery systems have recently attracted much attention for automotive applications, especially for long haul trucks. This paper focuses on the development of a dynamic counter-flow heat exchanger model for control purposes. The model captures the dyna
Improved Palmprint Identification System
Harshala C. Salave; Dr. Sachin D. Pable
2015-01-01
Abstract Generally private information is provided by using passwords or Personal Identification Numbers which is easy to implement but it is very easily stolen or forgotten or hack. In Biometrics for individuals identification uses human physiological which are constant throughout life like palm face DNA iris etc. or behavioral characteristicswhich is not constant in life like voice signature keystroke etc.. But mostly gain more attention to palmprint identification and is becoming more popu...
On Early Conflict Identification by Requirements Modeling of Energy System Control Structures
DEFF Research Database (Denmark)
Heussen, Kai; Gehrke, Oliver; Niemann, Hans Henrik
2015-01-01
Control systems are purposeful systems involving goal-oriented information processing (cyber) and technical (physical) structures. Requirements modeling formalizes fundamental concepts and relations of a system architecture at a high-level design stage and can be used to identify potential design...... at later design stages. However, languages employed for requirements modeling today do not offer the expressiveness necessary to represent control purposes in relation to domain level interactions and therefore miss several types of interdependencies. This paper introduces the idea of control structure...
Energy Technology Data Exchange (ETDEWEB)
Lundgren, Astrid; Sjoeberg, Jonas; Ramstroem Erik; Sunnerstam, Fredrik
2004-10-01
The possibility to use system identification to model combustion on a grate was studied. The identification was based on collected data from the combustion unit, data which was used to determine the model parameters. A number of step response experiments have been performed, for instance with varying pusher speed and air supply. No clear response was seen and thus it is concluded that the system is poorly excited. The initial requirements on the input parameters were not met. For instance many of the input parameters are co-varying with each other which limits the possibilities to single out the influence from each parameter on the combustion process. This will obstruct the identification procedure. In an attempt to improve the model, and compensate for the poor data, theoretical insights, i.e. a mass- and heat balances, have been included. Two model approaches were suggested, one based on the measured grate temperature, and another based on the fuel bed extension on the grate (particularly the position of the burn-out of the fuel). The first approach was implemented in an existing grey-box identification software MoCaVa, but the model output was concluded to be in poor agreement with measured data. The second approach was never tested since it could not be implemented in the MoCaVa software due to a discontinuous optimisation criteria. Instead a linear model based on the grate temperature has been used for comparison. In this model, it was shown that the response time of the grate temperature signal is significantly shorter than the fuel transportation time on the grate, thus a change in grate temperature is not only a result of the fuel transport. Radiation and conduction of heat to the grate is influencing the grate temperature and needs to be included in future modeling work. A strategy in order to separate the response from each signal during normal operation have been suggested. In future work the model need to be identified by exciting the system further and
Identification of Nonlinear Systems Using Neurofuzzy Networks
Institute of Scientific and Technical Information of China (English)
LI Ying; JIAO Licheng
2001-01-01
This paper presents a compound neu-ral network model, I.e., adaptive neurofuzzy network(ANFN), which can be used for identifying the com-plicated nonlinear system. The proposed ANFN has asimple structure and exploits a hybrid algorithm com-bining supervised learning and unsupervised learning.In addition, ANFN is capable of overcoming the errorof system identification due to the existence of somechanging points and improving the accuracy of identi-fication of the whole system. The effectiveness of themodel and its algorithm are tested on the identifica-tion results of missile attacking area.
Improved Palmprint Identification System
Directory of Open Access Journals (Sweden)
Harshala C. Salave
2015-03-01
Full Text Available Abstract Generally private information is provided by using passwords or Personal Identification Numbers which is easy to implement but it is very easily stolen or forgotten or hack. In Biometrics for individuals identification uses human physiological which are constant throughout life like palm face DNA iris etc. or behavioral characteristicswhich is not constant in life like voice signature keystroke etc.. But mostly gain more attention to palmprint identification and is becoming more popular technique using for identification and promising alternatives to the traditional password or PIN based authentication techniques. In this paper propose palmprint identification using veins on the palm and fingers. Here use fusion of techniques such as Discrete Wavelet transformDWT Canny Edge Detector Gaussian Filter Principle Component AnalysisPCA.
Nonlinear identification of power electronic systems
Chau, KT; Chan, CC
1995-01-01
This paper presents a new approach to modelling power electronic systems using nonlinear system identification. By employing the nonlinear autoregressive moving average with exogenous input (NARMAX) technique, the parametric model of power electronic systems can be derived from the time-domain data. This approach possesses some advantages over available circuit-oriented modelling approaches, such as no small-signal approximation, no circuit idealization and no detailed knowledge of system ope...
Identification of physical models
DEFF Research Database (Denmark)
Melgaard, Henrik
1994-01-01
design of experiments, which is for instance the design of an input signal that are optimal according to a criterion based on the information provided by the experiment. Also model validation is discussed. An important verification of a physical model is to compare the physical characteristics...... and Systems Testing), on testing of building components related to passive solar energy conservation, tested under outdoor climate conditions. The second case study is related to the performance of a spark ignition car engine. A phenomenological model of the fuel flow is identified under various operating...
Rienksma, R.A.; Suarez Diez, M.; Spina, L.; Schaap, P.J.; Martins dos Santos, V.A.P.
2014-01-01
Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metaboli
Computer system for identification of tool wear model in hot forging
Directory of Open Access Journals (Sweden)
Wilkus Marek
2016-01-01
Full Text Available The aim of the research was to create a methodology that will enable effective and reliable prediction of the tool wear. The idea of the hybrid model, which accounts for various mechanisms of tool material deterioration, is proposed in the paper. The mechanisms, which were considered, include abrasive wear, adhesive wear, thermal fatigue, mechanical fatigue, oxidation and plastic deformation. Individual models of various complexity were used for separate phenomena and strategy of combination of these models in one hybrid system was developed to account for the synergy of various mechanisms. The complex hybrid model was built on the basis of these individual models for various wear mechanisms. The individual models expanded from phenomenological ones for abrasive wear to multi-scale methods for modelling micro cracks initiation and propagation utilizing virtual representations of granular microstructures. The latter have been intensively developed recently and they form potentially a powerful tool that allows modelling of thermal and mechanical fatigue, accounting explicitly for the tool material microstructure.
System Identification in a MIC perspective
Directory of Open Access Journals (Sweden)
Lennart Ljung
1994-07-01
Full Text Available The paper describes some sibjective aspects on some current research topics in system identification. A 'classical' standpoint is taken regarding complexity models. The need for specific tools for 'semi-physical-modeling' is also pointed out, and a discussion on different disturbance models is also included.
Freeman, Chris
2016-01-01
This book presents a comprehensive framework for model-based electrical stimulation (ES) controller design, covering the whole process needed to develop a system for helping people with physical impairments perform functional upper limb tasks such as eating, grasping and manipulating objects. The book first demonstrates procedures for modelling and identifying biomechanical models of the response of ES, covering a wide variety of aspects including mechanical support structures, kinematics, electrode placement, tasks, and sensor locations. It then goes on to demonstrate how complex functional activities of daily living can be captured in the form of optimisation problems, and extends ES control design to address this case. It then lays out a design methodology, stability conditions, and robust performance criteria that enable control schemes to be developed systematically and transparently, ensuring that they can operate effectively in the presence of realistic modelling uncertainty, physiological variation an...
System Identification with Quantized Observations
Wang, Le Yi; Zhang, Jifeng; Zhao, Yanlong
2010-01-01
This book presents recently developed methodologies that utilize quantized information in system identification and explores their potential in extending control capabilities for systems with limited sensor information or networked systems. The results of these methodologies can be applied to signal processing and control design of communication and computer networks, sensor networks, mobile agents, coordinated data fusion, remote sensing, telemedicine, and other fields in which noise-corrupted quantized data need to be processed. Providing a comprehensive coverage of quantized identification,
Keshavarz, M; Mojra, A
2015-11-01
Accurate identification of breast tissue's dynamic behavior in physical examination is critical to successful diagnosis and treatment. In this study a model reference adaptive system identification (MRAS) algorithm is utilized to estimate the dynamic behavior of breast tissue from mechanical stress-strain datasets. A robot-assisted device (Robo-Tac-BMI) is going to mimic physical palpation on a 45 year old woman having a benign mass in the left breast. Stress-strain datasets will be collected over 14 regions of both breasts in a specific period of time. Then, a 2nd order linear model is adapted to the experimental datasets. It was confirmed that a unique dynamic model with maximum error about 0.89% is descriptive of the breast tissue behavior meanwhile mass detection may be achieved by 56.1% difference from the normal tissue.
On Markov parameters in system identification
Phan, Minh; Juang, Jer-Nan; Longman, Richard W.
1991-01-01
A detailed discussion of Markov parameters in system identification is given. Different forms of input-output representation of linear discrete-time systems are reviewed and discussed. Interpretation of sampled response data as Markov parameters is presented. Relations between the state-space model and particular linear difference models via the Markov parameters are formulated. A generalization of Markov parameters to observer and Kalman filter Markov parameters for system identification is explained. These extended Markov parameters play an important role in providing not only a state-space realization, but also an observer/Kalman filter for the system of interest.
Directory of Open Access Journals (Sweden)
Nicolau Cardoso Neto
2016-09-01
Full Text Available The relationship between humans and pets, especially dogs and cats is taking gigantic proportions, this can be seen by the data recently published by IBGE, where it was verified that the number of pets grows more than the number of child birth (IBGE, 2015. As so foreseen, the purpose of this study is to acknowledge which is the federal juridical basis related to the registration of Domestic Animals in Brazil, comparing them with the international legal standards that can be of reference, such as Canada, United States of America, Republic of Ireland and United Kingdom. This increase in pet population shows problems of many dimensions that relate to several causes in urban centers, especially when referring to zoonosis and conflicts that come from irresponsible ownership, bringing up a public health issue. The intension was to verify the possibility to adequate them to the reality encountered in Brazil, creating a model of identification for the Domestic Animals in urban areas in this country. As for that, the article addresses themes related to responsible ownership, animal welfare and models of registration already accomplished in the mentioned countries. At the end the article presents the proposal of a model for a system of identification of the Domestic Animals.
LPV Identification of a Heat Distribution System
DEFF Research Database (Denmark)
Trangbæk, K; Bendtsen, Jan Dimon
2010-01-01
This paper deals with incremental system identification of district heating systems to improve control performance. As long as various parameters, e.g. valve settings, are kept fixed, the dynamics of district heating systems can be approximated well by linear models; however, the dynamics change ...
Directory of Open Access Journals (Sweden)
Li Wang
2017-02-01
Full Text Available The ability to obtain appropriate parameters for an advanced pressurized water reactor (PWR unit model is of great significance for power system analysis. The attributes of that ability include the following: nonlinear relationships, long transition time, intercoupled parameters and difficult obtainment from practical test, posed complexity and difficult parameter identification. In this paper, a model and a parameter identification method for the PWR primary loop system were investigated. A parameter identification process was proposed, using a particle swarm optimization (PSO algorithm that is based on random perturbation (RP-PSO. The identification process included model variable initialization based on the differential equations of each sub-module and program setting method, parameter obtainment through sub-module identification in the Matlab/Simulink Software (Math Works Inc., Natick, MA, USA as well as adaptation analysis for an integrated model. A lot of parameter identification work was carried out, the results of which verified the effectiveness of the method. It was found that the change of some parameters, like the fuel temperature and coolant temperature feedback coefficients, changed the model gain, of which the trajectory sensitivities were not zero. Thus, obtaining their appropriate values had significant effects on the simulation results. The trajectory sensitivities of some parameters in the core neutron dynamic module were interrelated, causing the parameters to be difficult to identify. The model parameter sensitivity could be different, which would be influenced by the model input conditions, reflecting the parameter identifiability difficulty degree for various input conditions.
Wang, Xue-gang; Zou, Zao-jian; Yu, Long; Cai, Wei
2015-06-01
Based on support vector machines, three modeling methods, i.e., white-box modeling, grey-box modeling and black-box modeling of ship manoeuvring motion in 4 degrees of freedom are investigated. With the whole-ship mathematical model for ship manoeuvring motion, in which the hydrodynamic coefficients are obtained from roll planar motion mechanism test, some zigzag tests and turning circle manoeuvres are simulated. In the white-box modeling and grey-box modeling, the training data taken every 5 s from the simulated 20°/20° zigzag test are used, while in the black-box modeling, the training data taken every 5 s from the simulated 15°/15°, 20°/20° zigzag tests and 15°, 25° turning manoeuvres are used; and the trained support vector machines are used to predict the whole 20°/20° zigzag test. Comparisons between the simulated and predicted 20?/20° zigzag tests show good predictive ability of the proposed methods. Besides, all mathematical models obtained by the proposed modeling methods are used to predict the 10°/10° zigzag test and 35° turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the proposed modeling methods are analyzed and compared with each other in aspects of application conditions, prediction accuracy and computation speed. The appropriate modeling method can be chosen according to the intended use of the mathematical models and the available data needed for system identification.
Institute of Scientific and Technical Information of China (English)
王雪刚; 邹早建; 余龙; 蔡韡
2015-01-01
Based on support vector machines, three modeling methods, i.e., white-box modeling, grey-box modeling and black-box modeling of ship manoeuvring motion in 4 degrees of freedom are investigated. With the whole-ship mathematical model for ship manoeuvring motion, in which the hydrodynamic coefficients are obtained from roll planar motion mechanism test, some zigzag tests and turning circle manoeuvres are simulated. In the white-box modeling and grey-box modeling, the training data taken every 5 s from the simulated 20°/20° zigzag test are used, while in the black-box modeling, the training data taken every 5 s from the simulated 15°/15°, 20°/20° zigzag tests and 15°, 25° turning manoeuvres are used; and the trained support vector machines are used to predict the whole 20°/20° zigzag test. Comparisons between the simulated and predicted 20°/20° zigzag tests show good predictive ability of the proposed methods. Besides, all mathematical models obtained by the proposed modeling methods are used to predict the 10°/10° zigzag test and 35° turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the proposed modeling methods are analyzed and compared with each other in aspects of application conditions, prediction accuracy and computation speed. The appropriate modeling method can be chosen according to the intended use of the mathematical models and the available data needed for system identification.
He, Fei; Wei, Hua-Liang; Billings, Stephen A.
2015-08-01
This paper introduces a new approach for nonlinear and non-stationary (time-varying) system identification based on time-varying nonlinear autoregressive moving average with exogenous variable (TV-NARMAX) models. The challenging model structure selection and parameter tracking problems are solved by combining a multiwavelet basis function expansion of the time-varying parameters with an orthogonal least squares algorithm. Numerical examples demonstrate that the proposed approach can track rapid time-varying effects in nonlinear systems more accurately than the standard recursive algorithms. Based on the identified time domain model, a new frequency domain analysis approach is introduced based on a time-varying generalised frequency response function (TV-GFRF) concept, which enables the analysis of nonlinear, non-stationary systems in the frequency domain. Features in the TV-GFRFs which depend on the TV-NARMAX model structure and time-varying parameters are investigated. It is shown that the high-dimensional frequency features can be visualised in a low-dimensional time-frequency space.
Parametric uncertain identification of a robotic system
Angel, L.; Viola, J.; Hernández, C.
2016-07-01
This paper presents the parametric uncertainties identification of a robotic system of one degree of freedom. A MSC-ADAMS / MATLAB co-simulation model was built to simulate the uncertainties that affect the robotic system. For a desired trajectory, a set of dynamic models of the system was identified in presence of variations in the mass, length and friction of the system employing least squares method. Using the input-output linearization technique a linearized model plant was defined. Finally, the maximum multiplicative uncertainty of the system was modelled giving the controller desired design conditions to achieve a robust stability and performance of the closed loop system.
Comparative Study between ARX and ARMAX System Identification
Farzin Piltan; Shahnaz TayebiHaghighi; Nasri B. Sulaiman
2017-01-01
System Identification is used to build mathematical models of a dynamic system based on measured data. To design the best controllers for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. System identification is divided into different algorithms. In this research, two important types algorithm are compared to identifying the highly nonlinear systems, namely: Au...
System identification of the brompton bicycle
Hladun, Monique Victoria Teresa
The Brompton (a European folding design) bicycle was instrumented with a variety of sensors including acceleration, angular rate, speed, and steering sensors. A bicycle state estimator was designed to obtain additional information from this data including heading, turn rate, lean angle, steer rate, and positions of the wheels during a trajectory. The first part of the thesis describes the model setup for system identification including the Steer-to-Lean dynamics and Lean-to-Steer dynamics reduced models. CIFER software was used in the system identification process of these models. The second part describes the validation of the Empirical model by using the Rider Control model ([1]) and the Complete Rider/Vehicle model ([1]) to determine the feedback gains. The Theoretical model feedback gains were also determined by using the Rider Control model ([1]) and the Complete Rider/Vehicle model ([1]).
Identification of fractional chaotic system parameters
Energy Technology Data Exchange (ETDEWEB)
Al-Assaf, Yousef E-mail: yassaf@aus.ac.ae; El-Khazali, Reyad E-mail: khazali@ece.ac.ae; Ahmad, Wajdi E-mail: wajdi@sharjah.ac.ae
2004-11-01
In this work, a technique is introduced for parameter identification of fractional order chaotic systems. Features are extracted, from chaotic system outputs obtained for different system parameters, using discrete Fourier transform (DFT), power spectral density (PSD), and wavelets transform (WT). Artificial neural networks (ANN) are then trained on these features to predict the fractional chaotic system parameters. A fractional chaotic oscillator model is used through this work to demonstrate the developed technique. Numerical results show that recurrent Jordan-Elman neural networks with features obtained by the PSD estimate via Welch functions give adequate identification accuracy compared to other techniques.
Linear stochastic systems a geometric approach to modeling, estimation and identification
Lindquist, Anders
2015-01-01
This book presents a treatise on the theory and modeling of second-order stationary processes, including an exposition on selected application areas that are important in the engineering and applied sciences. The foundational issues regarding stationary processes dealt with in the beginning of the book have a long history, starting in the 1940s with the work of Kolmogorov, Wiener, Cramér and his students, in particular Wold, and have since been refined and complemented by many others. Problems concerning the filtering and modeling of stationary random signals and systems have also been addressed and studied, fostered by the advent of modern digital computers, since the fundamental work of R.E. Kalman in the early 1960s. The book offers a unified and logically consistent view of the subject based on simple ideas from Hilbert space geometry and coordinate-free thinking. In this framework, the concepts of stochastic state space and state space modeling, based on the notion of the conditional independence of pas...
Processing system of jaws tomograms for pathology identification and surgical guide modeling
Energy Technology Data Exchange (ETDEWEB)
Putrik, M. B., E-mail: pmb-88@mail.ru; Ivanov, V. Yu. [Ural Federal University named after the first President of Russia B.N. Yeltsin, Yekaterinburg (Russian Federation); Lavrentyeva, Yu. E. [Private dental clinic «Uraldent», Yekaterinburg (Russian Federation)
2015-11-17
The aim of the study is to create an image processing system, which allows dentists to find pathological resorption and to build surgical guide surface automatically. X-rays images of jaws from cone beam tomography or spiral computed tomography are the initial data for processing. One patient’s examination always includes up to 600 images (or tomograms), that’s why the development of processing system for fast automation search of pathologies is necessary. X-rays images can be useful not for only illness diagnostic but for treatment planning too. We have studied the case of dental implantation – for successful surgical manipulations surgical guides are used. We have created a processing system that automatically builds jaw and teeth boundaries on the x-ray image. After this step, obtained teeth boundaries used for surgical guide surface modeling and jaw boundaries limit the area for further pathologies search. Criterion for the presence of pathological resorption zones inside the limited area is based on statistical investigation. After described actions, it is possible to manufacture surgical guide using 3D printer and apply it in surgical operation.
Processing system of jaws tomograms for pathology identification and surgical guide modeling
Putrik, M. B.; Lavrentyeva, Yu. E.; Ivanov, V. Yu.
2015-11-01
The aim of the study is to create an image processing system, which allows dentists to find pathological resorption and to build surgical guide surface automatically. X-rays images of jaws from cone beam tomography or spiral computed tomography are the initial data for processing. One patient's examination always includes up to 600 images (or tomograms), that's why the development of processing system for fast automation search of pathologies is necessary. X-rays images can be useful not for only illness diagnostic but for treatment planning too. We have studied the case of dental implantation - for successful surgical manipulations surgical guides are used. We have created a processing system that automatically builds jaw and teeth boundaries on the x-ray image. After this step, obtained teeth boundaries used for surgical guide surface modeling and jaw boundaries limit the area for further pathologies search. Criterion for the presence of pathological resorption zones inside the limited area is based on statistical investigation. After described actions, it is possible to manufacture surgical guide using 3D printer and apply it in surgical operation.
Gaussian process based recursive system identification
Prüher, Jakub; Šimandl, Miroslav
2014-12-01
This paper is concerned with the problem of recursive system identification using nonparametric Gaussian process model. Non-linear stochastic system in consideration is affine in control and given in the input-output form. The use of recursive Gaussian process algorithm for non-linear system identification is proposed to alleviate the computational burden of full Gaussian process. The problem of an online hyper-parameter estimation is handled using proposed ad-hoc procedure. The approach to system identification using recursive Gaussian process is compared with full Gaussian process in terms of model error and uncertainty as well as computational demands. Using Monte Carlo simulations it is shown, that the use of recursive Gaussian process with an ad-hoc learning procedure offers converging estimates of hyper-parameters and constant computational demands.
Identification of Civil Engineering Structures using Vector ARMA Models
DEFF Research Database (Denmark)
Andersen, P.
The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models.......The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models....
Modal identification of system driven by lévy random excitation based on continuous time AR model
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
Based on the continuous time AR model,this paper presents a new time-domain modal identification method of LTI system driven by the uniformly modulated lévy random excitation.The structural dynamic equation is first transformed into the observation equation and the state equation(namely,stochastic differential equation).Based on the property of the strong solution of the stochastic differential equation,the uniformly modulated function is identified piecewise.Then by virtue of the Girsanov theorem,we present the exact maximum likelihood estimators of parameters.Finally,the modal parameters are identified by eigen analysis.Numerical results show that the method not only has high precision and robustness but also has very high computing efficiency.
Directory of Open Access Journals (Sweden)
Manghui Tu
2012-12-01
Full Text Available Forensic readiness can support future forensics investigation or auditing on external/internal attacks, internal sabotage and espionage, and business frauds. To establish forensics readiness, it is essential for an organization to identify what evidences are relevant and where they can be found, to determine whether they are logged in a forensic sound way and whether all the needed evidences are available to reconstruct the events successfully. Â Our goal of this research is to ensure evidence availability. First, both external and internal attacks are molded as augmented attack trees/graphs based on the system vulnerabilities. Second, modeled attacks are conducted against a honeynet simulating an online business information system, and each honeypot's hard drive is forensic sound imaged for each individual attack. Third, an evidence tree/graph will be built after forensics examination on the disk images for each attack. The evidence trees/graphs are expected to be used for automatic crime scene reconstruction and automatic attack/fraud detection in the future.
Energy Technology Data Exchange (ETDEWEB)
Sung, W.; Yoo, I.; Ra, S. [Hanyang Univ., Seoul (Korea, Republic of). Mineral and Petroleum Engineering Dept.; Park, H.
1996-08-01
The back propagation (BP) neural network approach has been the subject of recent focus because it can identify models for incomplete or distorted data without performing data preparation procedures. However, this approach uses only partial sets of data to reduce computing time and memory, and it may miss the points representing characteristics of the curve shape. Therefore, the resulted model may not be correct, forcing one to use sequential neural nets to find the correct model. The authors present the Hough Transform (HT) method combined with the BP neural network to improve this problem. With the aid of an HT, one can extract one simple pattern, including noisy and extraneous points, from the full-set data. A number of exercises also have been conducted for the published well-test data with the artificial intelligence neutral network identification system (ANNIS) they developed. The results show that ANNIS is quite reliable, especially for the incomplete or distorted data. They also demonstrate that the modified Levenberg-Marquart interpretation model, also developed in this work, successfully estimates reservoir parameters.
System Identification A Frequency Domain Approach
Pintelon, Rik
2012-01-01
System identification is a general term used to describe mathematical tools and algorithms that build dynamical models from measured data. Used for prediction, control, physical interpretation, and the designing of any electrical systems, they are vital in the fields of electrical, mechanical, civil, and chemical engineering. Focusing mainly on frequency domain techniques, System Identification: A Frequency Domain Approach, Second Edition also studies in detail the similarities and differences with the classical time domain approach. It high??lights many of the important steps in the identi
Open quantum system identification
Schirmer, Sophie G; Zhou, Weiwei; Gong, Erling; Zhang, Ming
2012-01-01
Engineering quantum systems offers great opportunities both technologically and scientifically for communication, computation, and simulation. The construction and operation of large scale quantum information devices presents a grand challenge and a major issue is the effective control of coherent dynamics. This is often in the presence of decoherence which further complicates the task of determining the behaviour of the system. Here, we show how to determine open system Markovian dynamics of a quantum system with restricted initialisation and partial output state information.
Constrained and regularized system identification
Directory of Open Access Journals (Sweden)
Tor A. Johansen
1998-04-01
Full Text Available Prior knowledge can be introduced into system identification problems in terms of constraints on the parameter space, or regularizing penalty functions in a prediction error criterion. The contribution of this work is mainly an extension of the well known FPE (Final Production Error statistic to the case when the system identification problem is constrained and contains a regularization penalty. The FPECR statistic (Final Production Error with Constraints and Regularization is of potential interest as a criterion for selection of both regularization parameters and structural parameters such as order.
Evaluating a model of anaerobic digestion of organic wastes through system identification
Energy Technology Data Exchange (ETDEWEB)
Anex, R.P.; Kiely, G.
1999-07-01
Anaerobic digestion of the organic fraction of municipal solid waste (MSW), on its own or co-digested with primary sewage sludge (PSS), produces high quality biogas, suitable as renewable energy. Parameter estimation and evaluation of a two-stage mathematical model of the anaerobic co-digestion of the organic fraction of MSW and PSS are described. Measured data are from a bench scale laboratory experiment using a continuously stirred tank reactor and operated at 36 C for 115 days. The two-stage model simulates acidogenesis and methanogenesis, including ammonia inhibition. Model parameters are estimated using an output error, Levenberg-Marquardt (LM) algorithm. Sensitivity of the estimated parameter values and the model outputs to non-estimated model parameters and measurement errors are evaluated. The estimated mathematical model successfully predicts the performance of the anaerobic reactor. Sensitivity results provide guidance for improving the model structure and experimental procedures.
Khan, Saad Ahmad; Thakore, Vaibhav; Behal, Aman; Bölöni, Ladislau; Hickman, James J
2013-03-01
Applications of non-invasive neuroelectronic interfacing in the fields of whole-cell biosensing, biological computation and neural prosthetic devices depend critically on an efficient decoding and processing of information retrieved from a neuron-electrode junction. This necessitates development of mathematical models of the neuron-electrode interface that realistically represent the extracellular signals recorded at the neuroelectronic junction without being computationally expensive. Extracellular signals recorded using planar microelectrode or field effect transistor arrays have, until now, primarily been represented using linear equivalent circuit models that fail to reproduce the correct amplitude and shape of the signals recorded at the neuron-microelectrode interface. In this paper, to explore viable alternatives for a computationally inexpensive and efficient modeling of the neuron-electrode junction, input-output data from the neuron-electrode junction is modeled using a parametric Wiener model and a Nonlinear Auto-Regressive network with eXogenous input trained using a dynamic Neural Network model (NARX-NN model). Results corresponding to a validation dataset from these models are then employed to compare and contrast the computational complexity and efficiency of the aforementioned modeling techniques with the Lee-Schetzen technique of cross-correlation for estimating a nonlinear dynamic model of the neuroelectronic junction.
Aziz Ayyad, Ezzat
A mathematical representation is sought to model the behavior of a portable pneumatic foam bladder designed to mitigate the effects of human exposure to shock and whole body random vibration. Fluid Dynamics principles are used to derive the analytic differential equations used for the physical equations Model. Additionally, combination of Wiener and Hammerstein block oriented representation techniques have been selected to create system identification (SID) block oriented models. A number of algorithms have been iterated to obtain numerical solutions for the system of equations which was found to be coupled and non-linear, with no analytic closed form solution. The purpose is to be able to predict the response of such system due to random vibrations and shock within reasonable margin of error. The constructed models were found to be accurate within accepted confidence level. Beside the analytic set of physical equations model representation, a linear SID model was selected to take advantage of the available vast amount of mathematical tools available to further analyze and redesign the bladder as a dynamic system. Measured field-test and lab test data have been collected from several helicopter and land terrain vehicle experiments. Numerous excitation and response acceleration measurement records were collected and used to prove the agreement with predictions. The estimation of two selected models were later applied to standard metrics in the frequency domain realization and compared with measurement responses. The collected test records are obtained from measured data at the US Army fields and facilities and at UNLV-CMEST environmental lab. The emerged models have been validated for conformity with actual accelerometer measurement responses and found within accepted error tolerance that is in both time and frequency domains. Further, standard metrics have been used to further confirm the confidence in the validation results. When comparing model prediction with
QUASILINEARIZATION, SYSTEM IDENTIFICATION, AND PREDICTION
regime in an effort to improve the quality of the control exerted. A mathematical formulation and computational solution of the problems of system ... identification and the determination of unmeasurable state variables on the basis of observations of a process, two topics of central importance in the
Beyond GLMs: a generative mixture modeling approach to neural system identification.
Theis, Lucas; Chagas, Andrè Maia; Arnstein, Daniel; Schwarz, Cornelius; Bethge, Matthias
2013-01-01
Generalized linear models (GLMs) represent a popular choice for the probabilistic characterization of neural spike responses. While GLMs are attractive for their computational tractability, they also impose strong assumptions and thus only allow for a limited range of stimulus-response relationships to be discovered. Alternative approaches exist that make only very weak assumptions but scale poorly to high-dimensional stimulus spaces. Here we seek an approach which can gracefully interpolate between the two extremes. We extend two frequently used special cases of the GLM-a linear and a quadratic model-by assuming that the spike-triggered and non-spike-triggered distributions can be adequately represented using Gaussian mixtures. Because we derive the model from a generative perspective, its components are easy to interpret as they correspond to, for example, the spike-triggered distribution and the interspike interval distribution. The model is able to capture complex dependencies on high-dimensional stimuli with far fewer parameters than other approaches such as histogram-based methods. The added flexibility comes at the cost of a non-concave log-likelihood. We show that in practice this does not have to be an issue and the mixture-based model is able to outperform generalized linear and quadratic models.
Beyond GLMs: a generative mixture modeling approach to neural system identification.
Directory of Open Access Journals (Sweden)
Lucas Theis
Full Text Available Generalized linear models (GLMs represent a popular choice for the probabilistic characterization of neural spike responses. While GLMs are attractive for their computational tractability, they also impose strong assumptions and thus only allow for a limited range of stimulus-response relationships to be discovered. Alternative approaches exist that make only very weak assumptions but scale poorly to high-dimensional stimulus spaces. Here we seek an approach which can gracefully interpolate between the two extremes. We extend two frequently used special cases of the GLM-a linear and a quadratic model-by assuming that the spike-triggered and non-spike-triggered distributions can be adequately represented using Gaussian mixtures. Because we derive the model from a generative perspective, its components are easy to interpret as they correspond to, for example, the spike-triggered distribution and the interspike interval distribution. The model is able to capture complex dependencies on high-dimensional stimuli with far fewer parameters than other approaches such as histogram-based methods. The added flexibility comes at the cost of a non-concave log-likelihood. We show that in practice this does not have to be an issue and the mixture-based model is able to outperform generalized linear and quadratic models.
A model for the identification of tropical weather systems over South ...
African Journals Online (AJOL)
drinie
2002-07-03
Jul 3, 2002 ... Department of Geography, Geoinformatics and Meteorology, University of Pretoria, ... weather systems are often associated with heavy rainfall and flooding ..... average 500 to 300 hPa temperatures in a tropical circulation.
Energy Technology Data Exchange (ETDEWEB)
Domingos, Roberto Pinheiro; Schirru, Roberto [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia. Programa de Engenharia Nuclear
2000-07-01
Neurofuzzy models are attractive to system identification to combine learning and structural features of neural network and the exposition based in rules associated to fuzzy systems. Genetic programming is a genetic algorithm extension where individuals are computer programs. It was proposed a modeling scheme where it's created, through genetic programming, a population of neurofuzzy systems capable to identify a given non-linear system. The data obtained when applying the resulting system to the identification of a simple non-linear function allows to conclude the technique has a quite promising application potential, and that are necessary improvements so that solutions can be obtained with a smaller number of generations and consequently in a smaller space of time. (author)
Ebrahimian, Hamed
2015-01-01
Structural health monitoring (SHM) is defined as the capability to monitor the performance behavior of civil infrastructure systems as well as to detect, localize, and quantify damage in these systems. SHM technologies contribute to enhance the resilience of civil infrastructures, which are vulnerable to structural aging, degradation, and deterioration and to extreme events due to natural and man-made hazards. Given the limited financial resources available to renovate or replace them, it is ...
New HVAC control by system identification
Energy Technology Data Exchange (ETDEWEB)
So, A.T.P.; Chan, W.L.; Chow, T.T.; Tse, W.L. [City University of Hong Kong, Kowloon (Hong Kong)
1995-12-01
Modern air-conditioning systems for commercial buildings commonly employ the concept of a ``Central All-Air System`` and the VAV system in particular is widely used in Hong Kong, and other places around the world for energy conservation. In the lengthy wet summer season of Hong Kong centralised air-handling units (AHUs) dehumidify and cool down the appropriate mixture of return air and outdoor fresh air to feed a ducting network to various Variable Air Volume Boxes. A good controller for the AHUs is extremely desirable from both human comfort and energy saving points of view. In this paper, a simulation model for a practical air-handling system is presented. Its behaviour under a conventional system of PID controllers is studied. A new controller based on system identification is developed where input and actuating variables are incorporated into a system identification model which can predict the new system status based on past records and suggest the optimal control actions. Computer simulation has proved that such a system identification based controller is superior to the conventional PID controller from at least the following three aspects: adaptation to system change, response rate and energy conservation. (author)
Energy Technology Data Exchange (ETDEWEB)
Vilim, R. B.; Pointer, W. D.; Wei, T. Y. C.; Nuclear Engineering Division
2006-04-30
Quantification of uncertainty is a key requirement for the design of a nuclear power plant and the assurance of its safety. Historically the procedure has been to perform the required uncertainty assessment through comparison of the analytical predictions with experimental simulations. The issue with this historical approach has always been that the simulations through experiments could not be at full scale for the practical reasons of cost and scheduling. Invariably, only parts of the system were tested separately or if integral testing was performed for the complete system, the size or scale of the experimental apparatus was significantly smaller than the actual plant configuration.
A new identification method for fuzzy linear models of non-linear dynamic systems
de Bruin, H.A.E.; Roffel, B.
1996-01-01
The most promising methods for identifying a fuzzy model are data clustering, cluster merging and subsequent projection of the clusters on the input variable space. This article proposes to modify this procedure by adding a cluster rotation step, and a method for the direct calculation of the
Modeling, system identification, and control for slosh-free motion of an open container of liquid
Energy Technology Data Exchange (ETDEWEB)
Feddema, J.; Baty, R.; Dykhuizen, R.; Dohrmann, C.; Parker, G.; Robinett, R.; Romero, V.; Schmitt, D.
1996-04-01
This report discusses work performed under a Cooperative Research And Development Agreement (CRADA) with Corning, Inc., to analyze and test various techniques for controlling the motion of a high speed robotic arm carrying an open container of viscous liquid, in this case, molten glass. A computer model was generated to estimate the modes of oscillation of the liquid based on the shape of the container and the viscosity of the liquid. This fluid model was experimentally verified and tuned based on experimental data from a capacitive sensor on the side of the container. A model of the robot dynamics was also developed and verified through experimental tests on a Fanuc S-800 robot arm. These two models were used to estimate the overall modes of oscillation of an open container of liquid being carried by a robot arm. Using the estimated modes, inverse dynamic control techniques were used to determine a motion profile which would eliminate waves on the liquid`s surface. Experimental tests showed that residual surface waves in an open container of water at the end of motion were reduced by over 95% and that in-motion surface waves were reduced by over 75%.
System identification in experimental data
Energy Technology Data Exchange (ETDEWEB)
Hammel, S.; Bo Hammer, P.W. [Nonlinear Dynamics and Wavelets Group, B44, Naval Surface Warfare Center, White Oak, Maryland 20903-5640 (United States)
1996-06-01
A technique to identify the state of a dynamical system is proposed. The technique is based upon an identification of all period-one orbits present in the system. These orbits can then be classified in a way that permits an organization into a hierarchical ordering. The scheme is applied to time-series data gathered from a carefully constructed damped driven pendulum. {copyright} {ital 1996 American Institute of Physics.}
Li, Yang; Wei, Hua-Liang; Billings, Stephen. A.; Sarrigiannis, P. G.
2016-08-01
The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. An efficient common model structure selection (CMSS) algorithm is proposed to select a common model structure, with application to EEG data modelling. The time-varying parameters for the identified common-structured model are then estimated using a sliding-window recursive least squares (SWRLS) approach. The new method can effectively detect and adaptively track and rapidly capture the transient variation of nonstationary signals, and can also produce robust models with better generalisation properties. Two examples are presented to demonstrate the effectiveness and applicability of the new approach including an application to EEG data.
Model and Sensor Based Nonlinear Adaptive Flight Control with Online System Identification
Sun, L.G.
2014-01-01
Consensus exists that many loss-of-control (LOC) in flight accidents caused by severe aircraft damage or system failure could be prevented if flight performance could be recovered using the valid and remaining control authorities. However, the safe maneuverability of a post-failure aircraft will
Model and Sensor Based Nonlinear Adaptive Flight Control with Online System Identification
Sun, L.G.
2014-01-01
Consensus exists that many loss-of-control (LOC) in flight accidents caused by severe aircraft damage or system failure could be prevented if flight performance could be recovered using the valid and remaining control authorities. However, the safe maneuverability of a post-failure aircraft will ine
Performance Test of System Identification Methods for a Nuclear Reactor
Energy Technology Data Exchange (ETDEWEB)
Yu, Keuk Jong; Kim, Han Gon [KHNP, Daejeon (Korea, Republic of)
2011-05-15
An automatic controller that uses the model predictive control (MPC) method is being developed for automatic load follow operation. As described in Ref. a system identification method is important in the MPC method because MPC is based on a system model produced by system identification. There are many models and methods of system identification. In this study, AutoRegressive eXogenous (ARX) model was selected from among them, and the recursive least square (RLS) method and least square (LS) method associated with this model are used in a comparative performance analysis
TIME DOMAIN PARAMETERS IDENTIFICATION OF FOUNDATION-STRUCTURE INTERACTION SYSTEM
Institute of Scientific and Technical Information of China (English)
HUANG Yi; LIU Zeng-rong
2005-01-01
The time domain parameter identification method of the foundation-structure interaction system is presented. On the basis of building the computation mode and the motion equation of the foundation-structure interaction system, the system parameter identification method was established by using the extended Kalman filter (EKF)technique and taking the unknown parameters in the system as the augment state variables. And the time parameter identification process of the foundation-structure interaction system was implemented by using the data of the layer foundation-storehouse interaction system model test on the large vibration platform. The computation result shows that the established parameter identification method can induce good parameter estmation.
Comparative Study between ARX and ARMAX System Identification
Directory of Open Access Journals (Sweden)
Farzin Piltan
2017-02-01
Full Text Available System Identification is used to build mathematical models of a dynamic system based on measured data. To design the best controllers for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. System identification is divided into different algorithms. In this research, two important types algorithm are compared to identifying the highly nonlinear systems, namely: AutoRegressive with eXternal model input (ARX and Auto Regressive moving Average with eXternal model input (Armax Theory. These two methods are applied to the highly nonlinear industrial motor.
DEFF Research Database (Denmark)
Costanzo, Giuseppe Tommaso; Sossan, Fabrizio; Marinelli, Mattia
2013-01-01
units, which operation can be shifted within temperature and operational constraints. Even if the refrigerators are not intended to be used as smart loads, validated models are useful in predicting units consumption. This information can increase the optimality of the management of other flexible units......, such as heat pumps for space heating, in order to smooth the load factor during peak hours, enhance reliability and efficiency in power networks and reduce operational costs....
Identification of Hysteresis in Human Meridian Systems Based on NARMAX Model
Yonghong Tan; Ruili Dong; Hui Chen; Hong He
2012-01-01
It has been found that the response of acupuncture point on the human meridian line exhibits nonlinear dynamic behavior when excitation of electroacupuncture is implemented on another meridian point. This nonlinear phenomenon is in fact a hysteretic phenomenon. In order to explore the characteristic of human meridian and finally find a way to improve the treatment of diseases via electro-acupuncture method, it is necessary to identify the model to describe the corresponding dynamic hysteretic...
Applied methods and techniques for mechatronic systems modelling, identification and control
Zhu, Quanmin; Cheng, Lei; Wang, Yongji; Zhao, Dongya
2014-01-01
Applied Methods and Techniques for Mechatronic Systems brings together the relevant studies in mechatronic systems with the latest research from interdisciplinary theoretical studies, computational algorithm development and exemplary applications. Readers can easily tailor the techniques in this book to accommodate their ad hoc applications. The clear structure of each paper, background - motivation - quantitative development (equations) - case studies/illustration/tutorial (curve, table, etc.) is also helpful. It is mainly aimed at graduate students, professors and academic researchers in related fields, but it will also be helpful to engineers and scientists from industry. Lei Liu is a lecturer at Huazhong University of Science and Technology (HUST), China; Quanmin Zhu is a professor at University of the West of England, UK; Lei Cheng is an associate professor at Wuhan University of Science and Technology, China; Yongji Wang is a professor at HUST; Dongya Zhao is an associate professor at China University o...
MICE Particle Identification System
Bogomilov, M
2010-01-01
The Muon Ionization Cooling Experiment, MICE, at the ISIS accelerator lo- cated at the Rutherford Appleton Laboratory, UK, will be the first experiment to study muon cooling at high precision. Demonstration of muon ionization cooling is an essential step towards the construction of a neutrino factory or a muon collider. Muons are produced by pion decay in a superconducting solenoid and reach MICE with a range of emittances and momenta. The purity of the muon beam is ensured by a system of particle detectors we will briefly describe here.
Continuous-Time Bilinear System Identification
Juang, Jer-Nan
2003-01-01
The objective of this paper is to describe a new method for identification of a continuous-time multi-input and multi-output bilinear system. The approach is to make judicious use of the linear-model properties of the bilinear system when subjected to a constant input. Two steps are required in the identification process. The first step is to use a set of pulse responses resulting from a constant input of one sample period to identify the state matrix, the output matrix, and the direct transmission matrix. The second step is to use another set of pulse responses with the same constant input over multiple sample periods to identify the input matrix and the coefficient matrices associated with the coupling terms between the state and the inputs. Numerical examples are given to illustrate the concept and the computational algorithm for the identification method.
Völker, Sebastian; Schreiber, Christiane; Kistemann, Thomas
2016-01-01
For the surveillance of drinking water plumbing systems (DWPS) and the identification of risk factors, there is a need for an early estimation of the risk of Legionella contamination within a building, using efficient and assessable parameters to estimate hazards and to prioritize risks. The precision, accuracy and effectiveness of ways of estimating the risk of higher Legionella numbers (temperature, stagnation, pipe materials, etc.) have only rarely been empirically assessed in practice, although there is a broad consensus about the impact of these risk factors. We collected n = 807 drinking water samples from 9 buildings which had had Legionella spp. occurrences of >100 CFU/100mL within the last 12 months, and tested for Legionella spp., L. pneumophila, HPC 20°C and 36°C (culture-based). Each building was sampled for 6 months under standard operating conditions in the DWPS. We discovered high variability (up to 4 log(10) steps) in the presence of Legionella spp. (CFU/100 mL) within all buildings over a half year period as well as over the course of a day. Occurrences were significantly correlated with temperature, pipe length measures, and stagnation. Logistic regression modelling revealed three parameters (temperature after flushing until no significant changes in temperatures can be obtained, stagnation (low withdrawal, qualitatively assessed), pipe length proportion) to be the best predictors of Legionella contamination (>100 CFU/100 mL) at single outlets (precision = 66.7%; accuracy = 72.1%; F(0.5) score = 0.59).
Structural Aspects of System Identification
Glover, Keith
1973-01-01
The problem of identifying linear dynamical systems is studied by considering structural and deterministic properties of linear systems that have an impact on stochastic identification algorithms. In particular considered is parametrization of linear systems so that there is a unique solution and all systems in appropriate class can be represented. It is assumed that a parametrization of system matrices has been established from a priori knowledge of the system, and the question is considered of when the unknown parameters of this system can be identified from input/output observations. It is assumed that the transfer function can be asymptotically identified, and the conditions are derived for the local, global and partial identifiability of the parametrization. Then it is shown that, with the right formulation, identifiability in the presence of feedback can be treated in the same way. Similarly the identifiability of parametrizations of systems driven by unobserved white noise is considered using the results from the theory of spectral factorization.
System parameter identification information criteria and algorithms
Chen, Badong; Hu, Jinchun; Principe, Jose C
2013-01-01
Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors' research pr
System identification of jet engines
Energy Technology Data Exchange (ETDEWEB)
Sugiyama, N.
2000-01-01
System identification plays an important role in advanced control systems for jet engines, in which controls are performed adaptively using data from the actual engine and the identified engine. An identification technique for jet engine using the Constant Gain Extended Kalman Filter (CGEKF) is described. The filter is constructed for a two-spool turbofan engine. The CGEKF filter developed here can recognize parameter change in engine components and estimate unmeasurable variables over whole flight conditions. These capabilities are useful for an advanced Full Authority Digital Electric Control (FADEC). Effects of measurement noise and bias, effects of operating point and unpredicted performance change are discussed. Some experimental results using the actual engine are shown to evaluate the effectiveness of CGEKF filter.
Spegazzini, Nicolas; Siesler, Heinz W; Ozaki, Yukihiro
2012-10-02
A sequential identification approach by two-dimensional (2D) correlation analysis for the identification of a chemical reaction model, activation, and thermodynamic parameters is presented in this paper. The identification task is decomposed into a sequence of subproblems. The first step is the construction of a reaction model with the suggested information by model-free 2D correlation analysis using a novel technique called derivative double 2D correlation spectroscopy (DD2DCOS), which enables one to analyze intensities with nonlinear behavior and overlapped bands. The second step is a model-based 2D correlation analysis where the activation and thermodynamic parameters are estimated by an indirect implicit calibration or a calibration-free approach. In this way, a minimization process for the spectral information by sample-sample 2D correlation spectroscopy and kinetic hard modeling (using ordinary differential equations) of the chemical reaction model is carried out. The sequential identification by 2D correlation analysis is illustrated with reference to the isomeric structure of diphenylurethane synthesized from phenylisocyanate and phenol. The reaction was investigated by FT-IR spectroscopy. The activation and thermodynamic parameters of the isomeric structures of diphenylurethane linked through a hydrogen bonding equilibrium were studied by means of an integration of model-free and model-based 2D correlation analysis called a sequential identification approach. The study determined the enthalpy (ΔH = 15.25 kJ/mol) and entropy (TΔS = 13.20 kJ/mol) of C═O···H hydrogen bonding of diphenylurethane through direct calculation from the differences in the kinetic parameters (δΔ(‡)H, -TδΔ(‡)S) at equilibrium in the chemical reaction system.
2013-01-01
The identification of kinetic models is an important step for the monitoring, control and optimization of industrial processes. This is particularly the case for highly competitive business sectors such as chemical and pharmaceutical industries, where the current trend of changing markets and strong competition leads to a reduction in the process development costs [1]. Moreover, the PAT initiative of the FDA advocates a better understanding and control of manufacturing processes by the use of...
Robust identification for multi-section freeway traffic models
Institute of Scientific and Technical Information of China (English)
Zhongke SHI
2005-01-01
Since it is difficult to fit measured parameters using the conventional traffic model, a new traffic density and average speed model is introduced in this paper.To determine traffic model structures accurately, a model identification method for uncertain nonlinear system is developed.To simplify uncertain nonlinear problem, this paper presents a new robust criterion to identify the multi-section traffic model structure of freeway efficiently.In the new model identification criterion,numerically efficient U-D factorization is used to avoid computing the determinant values of two complex matrices.By estimating the values of U-D factor of data matrix, both the upper and lower bounds of system uncertainties are described. Thus a model structure identification algorithm is proposed.Comparisons between identification outputs and simulation outputs of traffic states show that the traffic states can be accurately predicted by means of the new traffic models and the structure identification criterion.
An efficient automatic firearm identification system
Chuan, Zun Liang; Liong, Choong-Yeun; Jemain, Abdul Aziz; Ghani, Nor Azura Md.
2014-06-01
Automatic firearm identification system (AFIS) is highly demanded in forensic ballistics to replace the traditional approach which uses comparison microscope and is relatively complex and time consuming. Thus, several AFIS have been developed for commercial and testing purposes. However, those AFIS are still unable to overcome some of the drawbacks of the traditional firearm identification approach. The goal of this study is to introduce another efficient and effective AFIS. A total of 747 firing pin impression images captured from five different pistols of same make and model are used to evaluate the proposed AFIS. It was demonstrated that the proposed AFIS is capable of producing firearm identification accuracy rate of over 95.0% with an execution time of less than 0.35 seconds per image.
System identification of Drosophila olfactory sensory neurons.
Kim, Anmo J; Lazar, Aurel A; Slutskiy, Yevgeniy B
2011-02-01
The lack of a deeper understanding of how olfactory sensory neurons (OSNs) encode odors has hindered the progress in understanding the olfactory signal processing in higher brain centers. Here we employ methods of system identification to investigate the encoding of time-varying odor stimuli and their representation for further processing in the spike domain by Drosophila OSNs. In order to apply system identification techniques, we built a novel low-turbulence odor delivery system that allowed us to deliver airborne stimuli in a precise and reproducible fashion. The system provides a 1% tolerance in stimulus reproducibility and an exact control of odor concentration and concentration gradient on a millisecond time scale. Using this novel setup, we recorded and analyzed the in-vivo response of OSNs to a wide range of time-varying odor waveforms. We report for the first time that across trials the response of OR59b OSNs is very precise and reproducible. Further, we empirically show that the response of an OSN depends not only on the concentration, but also on the rate of change of the odor concentration. Moreover, we demonstrate that a two-dimensional (2D) Encoding Manifold in a concentration-concentration gradient space provides a quantitative description of the neuron's response. We then use the white noise system identification methodology to construct one-dimensional (1D) and two-dimensional (2D) Linear-Nonlinear-Poisson (LNP) cascade models of the sensory neuron for a fixed mean odor concentration and fixed contrast. We show that in terms of predicting the intensity rate of the spike train, the 2D LNP model performs on par with the 1D LNP model, with a root mean-square error (RMSE) increase of about 5 to 10%. Surprisingly, we find that for a fixed contrast of the white noise odor waveforms, the nonlinear block of each of the two models changes with the mean input concentration. The shape of the nonlinearities of both the 1D and the 2D LNP model appears to be
Nonlinear system identification with global and local soft computing methods
Energy Technology Data Exchange (ETDEWEB)
Runkler, T.A. [Siemens AG, Muenchen (Germany). Zentralabt. Technik Information und Kommunikation
2000-10-01
An important step in the design of control systems is system identification. Data driven system identification finds functional models for the system's input output behavior. Regression methods are simple and effective, but may cause overshoots for complicated characteristics. Neural network approaches such as the multilayer perceptron yield very accurate models, but are black box approaches which leads to problems in system and stability analysis. In contrast to these global modeling methods crisp and fuzzy rule bases represent local models that can be extracted from data by clustering methods. Depending on the type and number of models different degrees of model accuracy can be achieved. (orig.)
78 FR 58785 - Unique Device Identification System
2013-09-24
... 16, 801, 803, et al. Unique Device Identification System; Final Rule #0;#0;Federal Register / Vol. 78... 0910-AG31 Unique Device Identification System AGENCY: Food and Drug Administration, HHS. ACTION: Final... will substantially reduce existing obstacles to the adequate identification of medical devices used in...
77 FR 69393 - Unique Device Identification System
2012-11-19
... HUMAN SERVICES Food and Drug Administration 21 CFR Part 801 RIN 0910-AG31 Unique Device Identification... unique device identification system as required by recent amendments to the Federal Food, Drug, and..., FDA published a proposed rule to establish a unique device identification system, as required by...
NNSYSID - toolbox for system identification with neural networks
DEFF Research Database (Denmark)
Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad
2002-01-01
The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms...
NNSYSID - toolbox for system identification with neural networks
DEFF Research Database (Denmark)
Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad
2002-01-01
The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms...
Ladder Forms in Estimation and System Identification.
1977-01-01
system identification . Many record applications, such as in geophysical signal processing, high resolution (’maximum entropy’) spectral estimation and speech encoding, justify the interest in these forms. They appear in many contexts, such as scattering and network theory and the theory of orthogonal polynomials. The state-space model ladder realizations are very closely related in (block) Schwarz matrix canonical forms, which generally appear in the context of stability analysis. In fact they are the natural ’stability canonical form’ for
Energy Technology Data Exchange (ETDEWEB)
Saito, T. [Shimizu Construction Co. Ltd., Tokyo (Japan)
1998-06-30
Recently, with the increase of high-rise building construction, better precision is in demand in case of building model used for analyzing seismic, wind responses in the design. Particularly, the decay constant has been given uniformly for each type of structures, however, application of estimated value from practically measured results of real high-rise building in the design is preferred. In this report, purpose is to estimate the model parameters of multi-input-multi-output system by making optimum use of benefits of system identification using ARX model. Further, multi-input-multi-output ARX model based on the idea of mode analysis was established by revealing in common the self-regression coefficient for each output and algorithm for estimating its model constant was developed. Further, system identification was carried out by applying this proposed model in real seismic response record of high-rise building, effectiveness of the model was verified and seismic vibration characteristics of the building was evaluated. 27 refs.,10 figs., 2 tabs.
Identification of helicopter rotor dynamic models
Molusis, J. A.; Bar-Shalom, Y.; Warmbrodt, W.
1983-01-01
A recursive, extended Kalman-filter approach is applied to the identifiction of rotor damping levels of representative helicopter dynamic systems. The general formulation of the approach is presented in the context of a typically posed stochastic estimation problem, and the method is analytically applied to determining the damping levels of a coupled rotor-body system. The identified damping covergence characteristics are studied for sensitivity to both constant-coefficient and periodic-coefficient measurement models, process-noise covariance levels, and specified initial estimates of the rotor-system damping. A second application of the method to identifying the plant model for a highly damped, isolated flapping blade with a constant-coefficient state model (hover) and a periodic-coefficient state model (forward flight) is also investigated. The parameter-identification capability is evaluated for the effect of periodicity on the plant model coefficients and the influence of different measurement noise levels.
Sterbentz, Dane M.; Prasai, Sujan; Hofle, Mary M.; Walters, Thomas; Lin, Feng; Li, Ji-chao; Bosworth, Ken; Schoen, Marco P.
2016-04-01
In recent years, the correlation coefficient of pressure data from the same blade passage in an axial compressor unit has been used to characterize the state of flow in the blade passage. In addition, the correlation coefficient has been successfully used as an indicator for active control action using air injection. In this work, the correlation coefficient approach is extended to incorporate system identification algorithms in order to extract a mathematical model of the dynamics of the flows within a blade passage. The dynamics analyzed in this research focus on the flow streams and pressure along the rotor blades as well as on the unsteady tip leakage flow from the rotor tip gaps. The system identification results are used to construct a root locus plot for different flow coefficients, starting far away from stall to near stall conditions. As the compressor moves closer to stall, the poles of the identified models move towards the imaginary axis of the complex plane, indicating an impending instability. System frequency data is captured using the proposed correlation based system identification approach. Additionally, an oscillatory tip leakage flow is observed at a flow coefficient away from stall and how this oscillation changes as the compressor approaches stall is an interesting result of this research. Comparative research is analyzed to determine why the oscillatory flow behavior occurs at a specific sensor location within the tip region of the rotor blade.
Course teaching of system identification and modeling%系统辨识与建模课程教学探讨
Institute of Scientific and Technical Information of China (English)
史晓霞
2012-01-01
系统辨识与建模是现代控制理论的一个分支,是控制理论与控制专业工程硕士研究生的必修课程,学生通过该课程的学习掌握相关的辨识算法。文章通过对该课程的教学实践进行了研究与探讨,对教学内容、教学方式与教学手段、课程的考核等方面进行了改革,提高了该课程的教学质量。%System identification and modeling is a part of modern control theory.This course is a required course for the graduate students in control theory and control engineering specialty.The students can learn about the algorithms for system identification through this course.With the research on the teaching practice of system identification and modeling,the paper reformed the choice and purity of teaching content,the teaching method,the course test.It improved the teaching quality.
System Identification Modeling of Rotorcraft Flight Mechanics%旋翼飞行器飞行动力学系统辨识建模算法
Institute of Scientific and Technical Information of China (English)
宋彦国; 孙涛
2011-01-01
描述了旋翼飞行器飞行力学模型的系统辨识建模算法,从旋翼飞行器飞行动力学建模的共性问题入手,首先采用机理建模的方法分析了旋翼飞行嚣主要气动部件所受气动力.考虑旋翼挥舞运动对旋翼飞行器飞行动力学特性的影响,建立了旋翼飞行器的飞行力学系统辨识参数化模型集.其次以子空间方法辨识初始飞行动力学模型,采用加权频域预报误差法获得最优模型的两步辨识方法解决旋翼飞行器这一非线性不稳定,多输入-多输出系统辨识问题,且所辨识模型与机理模型具有相同的结构.最后对样例直升机的悬停飞行状态模型辨识进行了数值与试飞试验验证,表明了方法的有效性.%Based on common characteristics of rotorcraft flight mechanics modeling, theories and algorithm of model identification are studied. Firstly, by using mechanism modeling method and considering blades flapping, the parameter identification model group is established. Secondly, in order to solve multi input and output system identification problems, a two step identification method is proposed. It identifies the initial model by subspace identification method and then the optimized model by frequency prediction error method. Finally, with this two-step identification method, the simulation and flight tests are conducted to identify the example helicopter flight mechanics model in the hover state. The result shows that the method is effective and accurate.
THz identification and Bayes modeling
Sokolnikov, Andre
2017-05-01
THz Identification is a developing technology. Sensing in the THz range potentially gives opportunity for short range radar sensing because THz waves can better penetrate through obscured atmosphere, such as fog, than visible light. The lower scattering of THz as opposed to the visible light results also in significantly better imaging than in IR spectrum. A much higher contrast can be achieved in medical trans-illumination applications than with X-rays or visible light. The same THz radiation qualities produce better tomographical images from hard surfaces, e.g. ceramics. This effect comes from the delay in time of reflected THz pulses detection. For special or commercial applications alike, the industrial quality control of defects is facilitated with a lower cost. The effectiveness of THz wave measurements is increased with computational methods. One of them is Bayes modeling. Examples of this kind of mathematical modeling are considered.
Research of internet worm warning system based on system identification
Institute of Scientific and Technical Information of China (English)
Tao ZHOU; Guanzhong DAI; Huimin YE
2006-01-01
The frequent explosion of Internet worms has been one of the most serious problems in cyberspace security.In this paper, by analyzing the worm's propagation model, we propose a new worm warning system based on the method of system identification, and use recursive least squares algorithm to estimate the worm's infection rate. The simulation result shows the method we adopted is an efficient way to conduct Internet worm warning.
Futia, Gregory L.; Qamar, Lubna; Behbakht, Kian; Gibson, Emily A.
2016-04-01
Circulating tumor cell (CTC) identification has applications in both early detection and monitoring of solid cancers. The rarity of CTCs, expected at ~1-50 CTCs per million nucleated blood cells (WBCs), requires identifying methods based on biomarkers with high sensitivity and specificity for accurate identification. Discovery of biomarkers with ever higher sensitivity and specificity to CTCs is always desirable to potentially find more CTCs in cancer patients thus increasing their clinical utility. Here, we investigate quantitative image cytometry measurements of lipids with the biomarker panel of DNA, Cytokeratin (CK), and CD45 commonly used to identify CTCs. We engineered a device for labeling suspended cell samples with fluorescent antibodies and dyes. We used it to prepare samples for 4 channel confocal laser scanning microscopy. The total data acquired at high resolution from one sample is ~ 1.3 GB. We developed software to perform the automated segmentation of these images into regions of interest (ROIs) containing individual cells. We quantified image features of total signal, spatial second moment, spatial frequency second moment, and their product for each ROI. We performed measurements on pure WBCs, cancer cell line MCF7 and mixed samples. Multivariable regressions and feature selection were used to determine combination features that are more sensitive and specific than any individual feature separately. We also demonstrate that computation of spatial characteristics provides higher sensitivity and specificity than intensity alone. Statistical models allowed quantification of the required sensitivity and specificity for detecting small levels of CTCs in a human blood sample.
State-space system identification of robot manipulator dynamics
Johansson, Rolf; Robertsson, Anders; Nilsson, Klas; Verhaegen, Michel
2000-01-01
We have applied and evaluated system identification methods using both commercial software and dedicated subspace model identification software (MOESP). Results using the different software tools have been similar (but not identical) in accuracy and predictive power, the main differences being the t
一种新型非线性系统模型参数辨识方法%Novel Method for Parameter Identification of Nonlinear System Model
Institute of Scientific and Technical Information of China (English)
陶国正; 徐志成
2012-01-01
关于非线性自动控制系统优化问题,为解决复杂非线性系统的辨识问题,提出了一种基于菌群优化算法的非线性系统辨识方法.结合菌群优化算法的特点,通过将待辨识参数设置为群体细菌在参数空间的位置,并利用细菌群体觅食的动态行为来实现对系统参数的辨识,有效地提高了参数辨识的精度和效率.通过对重油热解三集总模型进行了仿真研究,得到了较为精确的过程模型,模型输出与实际输出基本一致.仿真结果表明:菌群优化算法为非线性系统模型参数估计提供了一种有效的途径.%Nonlinear system identification is one of the most important topics of modem identification. A novel approach for complex nonlinear system identification was proposed based on the bacterial swarm foraging for optimization ( BSFO). By combining the bacterial swarm foraging for optimization, BSFO was used to simulate the social behavior of foraging bacteria, in which the bacteria positions in the parameter spaces were set as the parameters of NSM, and the precision and efficiency for parameters identification were improved. Applied to heavy oil thermal cracking model , the method obtained the precise process model, and the model's outputs coincide to the actual outputs. The simulation results show that the BSFO algorithm provides an attractive method to identify parameters of NSM.
SYSTEM IDENTIFICATION OF SURFACE SHIP DYNAMICS.
The feasibility of applying a Newtonian system identification technique to a nonlinear three degree of freedom system of equations describing the...steering and maneuvering of a surface ship is investigated. The input to the system identification program is provided by both analog and digital
Power system identification toolbox: Phase two progress
Energy Technology Data Exchange (ETDEWEB)
Trudnowski, D.J.
1994-08-01
This report describes current progress on a project funded by the Bonneville Power Administration (BPA) to develop a set of state-of-the-art analysis software (termed the Power System Identification [PSI] Toolbox) for fitting dynamic models to measured data. The project is being conducted as a three-phase effort. The first phase, completed in late 1992, involved investigating the characteristics of the analysis techniques by evaluating existing software and developing guidelines for best use. Phase Two includes extending current software, developing new analysis algorithms and software, and demonstrating and developing applications. The final phase will focus on reorganizing the software into a modular collection of documented computer programs and developing user manuals with instruction and application guidelines. Phase Two is approximately 50% complete; progress to date and a vision for the final product of the PSI Toolbox are described. The needs of the power industry for specialized system identification methods are particularly acute. The industry is currently pushing to operate transmission systems much closer to theoretical limits by using real-time, large-scale control systems to dictate power flows and maintain dynamic stability. Reliably maintaining stability requires extensive system-dynamic modeling and analysis capability, including measurement-based methods. To serve this need, the BPA has developed specialized system-identification computer codes through in-house efforts and university contract research over the last several years. To make full integrated use of the codes, as well as other techniques, the BPA has commissioned Pacific Northwest Laboratory (PNL) to further develop the codes and techniques into the PSI Toolbox.
Frequency weighted system identification and linear quadratic controller design
Horta, Lucas G.; Phan, Minh; Juang, Jer-Nan; Longman, Richard W.; Sulla, Jeffrey L.
1991-01-01
Application of filters for frequency weighting of Markov parameters (pulse response functions) is described in relation to system/observer identification. The time domain identification approach recovers a model which has a pulse response weighted according to frequency. The identified model is composed of the original system and filters. The augmented system is in a form which can be used directly for frequency weighted linear quadratic controller design. Data from either single or multiple experiments can be used to recover the Markov parameters. Measured acceleration signals from a truss structure are used for system identification and the model obtained is used for frequency weighted controller design. The procedure makes the identification and controler design complementary problems.
Input-output identification of controlled discrete manufacturing systems
Estrada-Vargas, Ana Paula; López-Mellado, Ernesto; Lesage, Jean-Jacques
2014-03-01
The automated construction of discrete event models from observations of external system's behaviour is addressed. This problem, often referred to as system identification, allows obtaining models of ill-known (or even unknown) systems. In this article, an identification method for discrete event systems (DESs) controlled by a programmable logic controller is presented. The method allows processing a large quantity of observed long sequences of input/output signals generated by the controller and yields an interpreted Petri net model describing the closed-loop behaviour of the automated DESs. The proposed technique allows the identification of actual complex systems because it is sufficiently efficient and well adapted to cope with both the technological characteristics of industrial controllers and data collection requirements. Based on polynomial-time algorithms, the method is implemented as an efficient software tool which constructs and draws the model automatically; an overview of this tool is given through a case study dealing with an automated manufacturing system.
Identification methods for nonlinear stochastic systems.
Fullana, Jose-Maria; Rossi, Maurice
2002-03-01
Model identifications based on orbit tracking methods are here extended to stochastic differential equations. In the present approach, deterministic and statistical features are introduced via the time evolution of ensemble averages and variances. The aforementioned quantities are shown to follow deterministic equations, which are explicitly written within a linear as well as a weakly nonlinear approximation. Based on such equations and the observed time series, a cost function is defined. Its minimization by simulated annealing or backpropagation algorithms then yields a set of best-fit parameters. This procedure is successfully applied for various sampling time intervals, on a stochastic Lorenz system.
A recent case study in system identification
Hasselman, T. K.; Chrostowski, J. D.
1991-01-01
Results of a recent study of a ten-bay truss structure at the NASA Langley Research Center are reported. First, the conditioning of complex eigenvectors derived by the ERA method is discussed. Results of parameter estimation using the SSID (Structural System Identification) code are then presented. Based on the results of the study, it is concluded that (1) parameter estimation based on modal data should include eigenvectors as well as eigenvalues; (2) the eigenvectors should be orthogonalized when orthogonality is poor due to closely spaced modes; and (3) the parameters used in the estimation should enable the model to match the data.
Walker, B. K.; Gai, E.
1978-01-01
A method for determining time-varying Failure Detection and Identification (FDI) thresholds for single sample decision functions is described in the context of a triplex system of inertial platforms. A cost function consisting of the probability of vehicle loss due to FDI decision errors is minimized. A discrete Markov model is constructed from which this cost can be determined as a function of the decision thresholds employed to detect and identify the first and second failures. Optimal thresholds are determined through the use of parameter optimization techniques. The application of this approach to threshold determination is illustrated for the Space Shuttle's inertial measurement instruments.
System Identification for Indoor Climate Control
M., A W; H., P W M; Steskens,
2012-01-01
The study focuses on the applicability of system identification to identify building and system dynamics for climate control design. The main problem regarding the simulation of the dynamic response of a building using building simulation software is that (1) the simulation of a large complex building is time consuming, and (2) simulation results often lack information regarding fast dynamic behaviour (in the order of seconds), since most software uses a discrete time step, usually fixed to one hour. The first objective is to study the applicability of system identification to reduce computing time for the simulation of large complex buildings. The second objective is to research the applicability of system identification to identify building dynamics based on discrete time data (one hour) for climate control design. The study illustrates that system identification is applicable for the identification of building dynamics with a frequency that is smaller as the maximum sample frequency as used for identificat...
Stochastic system identification in structural dynamics
Safak, Erdal
1988-01-01
Recently, new identification methods have been developed by using the concept of optimal-recursive filtering and stochastic approximation. These methods, known as stochastic identification, are based on the statistical properties of the signal and noise, and do not require the assumptions of current methods. The criterion for stochastic system identification is that the difference between the recorded output and the output from the identified system (i.e., the residual of the identification) should be equal to white noise. In this paper, first a brief review of the theory is given. Then, an application of the method is presented by using ambient vibration data from a nine-story building.
Identification of the nonlinear vibration system of power transformers
Jing, Zheng; Hai, Huang; Pan, Jie; Yanni, Zhang
2017-01-01
This paper focuses on the identification of the nonlinear vibration system of power transformers. A Hammerstein model is used to identify the system with electrical inputs and the vibration of the transformer tank as the output. The nonlinear property of the system is modelled using a Fourier neural network consisting of a nonlinear element and a linear dynamic block. The order and weights of the network are determined based on the Lipschitz criterion and the back-propagation algorithm. This system identification method is tested on several power transformers. Promising results for predicting the transformer vibration and extracting system parameters are presented and discussed.
Ozil, Ipek; Plawecki, Martin H; Doerschuk, Peter C; O'Connor, Sean J
2011-01-01
The influence of family history and genetics on the risk for the development of abuse or dependence is a major theme in alcoholism research. Recent research have used endophenotypes and behavioral paradigms to help detect further genetic contributions to this disease. Electronic tasks, essentially video games, which provide alcohol as a reward in controlled environments and with specified exposures have been developed to explore some of the behavioral and subjective characteristics of individuals with or at risk for alcohol substance use disorders. A generative model (containing parameters with unknown values) of a simple game involving a progressive work paradigm is described along with the associated point process signal processing that allows system identification of the model. The system is demonstrated on human subject data. The same human subject completing the task under different circumstances, e.g., with larger and smaller alcohol reward values, is assigned different parameter values. Potential meanings of the different parameter values are described.
Cho, Soojin; Park, Jong-Woong; Sim, Sung-Han
2015-04-08
Wireless sensor networks (WSNs) facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI) technique is selected for system identification, and SSI-based decentralized system identification (SDSI) is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model.
Rotorcraft System Identification (Identification des Systemes de Voilures Tournantes)
1991-10-01
139, 1985. DuVal, R.W., Wang , .C. and Demiroz, M.Y.: A Practtcal Approach to Rotorcraft Systems Padfield, G.D., Thorne, R., Murray-Smith, D...an experimentel verification of the Kalman filter iRA)YOUG, PETER, (AB)PATTOn, ROALD J implementation, sod an experimental evaluation of filter...The estimation of the measurements wlth the RSRA compound helicopter parameter values in this model (the stability and control derivatives) (AA) WANG
Spears, Janine L.; Parrish, James L., Jr.
2013-01-01
This teaching case introduces students to a relatively simple approach to identifying and documenting security requirements within conceptual models that are commonly taught in systems analysis and design courses. An introduction to information security is provided, followed by a classroom example of a fictitious company, "Fun &…
Spears, Janine L.; Parrish, James L., Jr.
2013-01-01
This teaching case introduces students to a relatively simple approach to identifying and documenting security requirements within conceptual models that are commonly taught in systems analysis and design courses. An introduction to information security is provided, followed by a classroom example of a fictitious company, "Fun &…
Directory of Open Access Journals (Sweden)
S. V. Nikolaev
2015-01-01
motion in the range of main flight operating conditions. This model is aimed at using to not only to support testing, but at subsequent stages of the aviation system life cycle as well. These models are necessary when training the pilots to fly to the ship, when developing the simulators, at the stage of modernization, in examination of aircraft accidents, etc. As a result of the work, have been created a new technique for evaluating the stability and controllability characteristics of the naval aircraft system, including a new procedure for the identification and also the new algorithmic techniques that can be used to visualize the dependence of aerodynamic coefficients as a function of the angle of attack and a new approach to identify longitudinal channel according to lateral test flight modes. All the results are confirmed by processing of flight experiment materials.The work is fulfilled with the support of the Russian Foundation for Basic Research (RFBR, project 15-08-06237.
Identification of Stochastic Wiener Systems using Indirect Inference
2015-01-01
We study identification of stochastic Wiener dynamic systems using so-called indirect inference. The main idea is to first fit an auxiliary model to the observed data and then in a second step, often by simulation, fit a more structured model to the estimated auxiliary model. This two-step procedure can be used when the direct maximum-likelihood estimate is difficult or intractable to compute. One such example is the identification of stochastic Wiener systems, i.e.,~linear dynamic systems wi...
Dental orthopantomogram biometrics system for human identification.
Singh, Sandeep; Bhargava, Darpan; Deshpande, Ashwini
2013-07-01
Fingerprinting is the most widely accepted method of identification of people. But in cases of disfigured, decomposed, burnt or fragmented bodies, it is of limited value. Teeth and dental restorations on the other hand are extremely resistant to destruction by fire. They retain a number of their original characteristics, which are often unique and hence offer a possibility of rather accurate and legally acceptable identification of such remains. This study was undertaken to evaluate the utility of orthopantomography for human identification and propose a coding system for orthopantomogram (OPG), which can be utilized as an identification tool in forensic sciences.
System identification methods for aircraft flight control development and validation
Tischler, Mark B.
1995-01-01
System-identification methods compose a mathematical model, or series of models, from measurements of inputs and outputs of dynamic systems. The extracted models allow the characterization of the response of the overall aircraft or component subsystem behavior, such as actuators and on-board signal processing algorithms. This paper discusses the use of frequency-domain system-identification methods for the development and integration of aircraft flight-control systems. The extraction and analysis of models of varying complexity from nonparametric frequency-responses to transfer-functions and high-order state-space representations is illustrated using the Comprehensive Identification from FrEquency Responses (CIFER) system-identification facility. Results are presented for test data of numerous flight and simulation programs at the Ames Research Center including rotorcraft, fixed-wing aircraft, advanced short takeoff and vertical landing (ASTOVL), vertical/short takeoff and landing (V/STOL), tiltrotor aircraft, and rotor experiments in the wind tunnel. Excellent system characterization and dynamic response prediction is achieved for this wide class of systems. Examples illustrate the role of system-identification technology in providing an integrated flow of dynamic response data around the entire life-cycle of aircraft development from initial specifications, through simulation and bench testing, and into flight-test optimization.
Chase, J Geoffrey; Lambermont, Bernard; Starfinger, Christina; Hann, Christopher E; Shaw, Geoffrey M; Ghuysen, Alexandre; Kolh, Philippe; Dauby, Pierre C; Desaive, Thomas
2011-01-01
A cardiovascular system (CVS) model and parameter identification method have previously been validated for identifying different cardiac and circulatory dysfunctions in simulation and using porcine models of pulmonary embolism, hypovolemia with PEEP titrations and induced endotoxic shock. However, these studies required both left and right heart catheters to collect the data required for subject-specific monitoring and diagnosis-a maximally invasive data set in a critical care setting although it does occur in practice. Hence, use of this model-based diagnostic would require significant additional invasive sensors for some subjects, which is unacceptable in some, if not all, cases. The main goal of this study is to prove the concept of using only measurements from one side of the heart (right) in a 'minimal' data set to identify an effective patient-specific model that can capture key clinical trends in endotoxic shock. This research extends existing methods to a reduced and minimal data set requiring only a single catheter and reducing the risk of infection and other complications-a very common, typical situation in critical care patients, particularly after cardiac surgery. The extended methods and assumptions that found it are developed and presented in a case study for the patient-specific parameter identification of pig-specific parameters in an animal model of induced endotoxic shock. This case study is used to define the impact of this minimal data set on the quality and accuracy of the model application for monitoring, detecting and diagnosing septic shock. Six anesthetized healthy pigs weighing 20-30 kg received a 0.5 mg kg(-1) endotoxin infusion over a period of 30 min from T0 to T30. For this research, only right heart measurements were obtained. Errors for the identified model are within 8% when the model is identified from data, re-simulated and then compared to the experimentally measured data, including measurements not used in the identification
CEAI: CCM based Email Authorship Identification Model
DEFF Research Database (Denmark)
Nizamani, Sarwat; Memon, Nasrullah
2013-01-01
content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results...
Gain Scheduling Control based on Closed-Loop System Identification
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
This paper deals with system identification and gain scheduling control of multi-variable nonlinear systems. We propose a novel scheme where a linear approximation of the system model is obtained in an operating point; then, a Youla-Kucera (YJBK) parameter specifying the difference between...... the first and a second operating point is identified in closed-loop using system identification methods with open-loop properties. Next, a linear controller is designed for this linearised model, and gain scheduling control can subsequently be achieved by interpolating between each controller...
Energy Technology Data Exchange (ETDEWEB)
Johannesson, G; Glaser, R E; Lee, C L; Nitao, J J; Hanley, W G
2005-02-07
Estimating unknown system configurations/parameters by combining system knowledge gained from a computer simulation model on one hand and from observed data on the other hand is challenging. An example of such inverse problem is detecting and localizing potential flaws or changes in a structure by using a finite-element model and measured vibration/displacement data. We propose a probabilistic approach based on Bayesian methodology. This approach does not only yield a single best-guess solution, but a posterior probability distribution over the parameter space. In addition, the Bayesian approach provides a natural framework to accommodate prior knowledge. A Markov chain Monte Carlo (MCMC) procedure is proposed to generate samples from the posterior distribution (an ensemble of likely system configurations given the data). The MCMC procedure proposed explores the parameter space at different resolutions (scales), resulting in a more robust and efficient procedure. The large-scale exploration steps are carried out using coarser-resolution finite-element models, yielding a considerable decrease in computational time, which can be a crucial for large finite-element models. An application is given using synthetic displacement data from a simple cantilever beam with MCMC exploration carried out at three different resolutions.
Model Identification of a Micro Air Vehicle
Institute of Scientific and Technical Information of China (English)
Jorge Ni(n)o; Flavius Mitrache; Peter Cosyn; Robin De Keyser
2007-01-01
This paper is focused on the model identification of a Micro Air Vehicle (MAV) in straight steady flight condition. The identification is based on input-output data collected from flight tests using both frequency and time dontain techniques. The vehicle is an in-house 40 cm wingspan airplane. Because of the complex coupled, multivariable and nonlinear dynamics of the aircraft, linear SISO structures for both the lateral and longitudinal models around a reference state were derived. The aim of the identification is to provide models that can be used in future development of control techniques for the MAV.
THE THREE DIMENSIONAL MODELS AND THEIR IDENTIFICATION MINING SUBSIDENCE
Institute of Scientific and Technical Information of China (English)
WUGe; SHENGuanghan; JIXiaoming; WANGQuanke
1995-01-01
The theory and method for selecting the three dimensional prediction models of mining subsidence are studied in this paper. Namely, based on system identification and statistics theory, an optimum mining subsidence prediction model can be selected. The method proved by a typical case has a good prospect for determining the physical model of rock mass for mining subsidence prediction.
Access control and personal identification systems
Bowers, Dan M
1988-01-01
Access Control and Personal Identification Systems provides an education in the field of access control and personal identification systems, which is essential in selecting the appropriate equipment, dealing intelligently with vendors in purchases of the equipment, and integrating the equipment into a total effective system. Access control devices and systems comprise an important part of almost every security system, but are seldom the sole source of security. In order for the goals of the total system to be met, the other portions of the security system must also be well planned and executed
Thermal Signature Identification System (TheSIS)
Merritt, Scott; Bean, Brian
2015-01-01
We characterize both nonlinear and high order linear responses of fiber-optic and optoelectronic components using spread spectrum temperature cycling methods. This Thermal Signature Identification System (TheSIS) provides much more detail than conventional narrowband or quasi-static temperature profiling methods. This detail allows us to match components more thoroughly, detect subtle reversible shifts in performance, and investigate the cause of instabilities or irreversible changes. In particular, we create parameterized models of athermal fiber Bragg gratings (FBGs), delay line interferometers (DLIs), and distributed feedback (DFB) lasers, then subject the alternative models to selection via the Akaike Information Criterion (AIC). Detailed pairing of components, e.g. FBGs, is accomplished by means of weighted distance metrics or norms, rather than on the basis of a single parameter, such as center wavelength.
Energy Technology Data Exchange (ETDEWEB)
Gonzalez Gonzalez, R., E-mail: r.gonzalez@ing.unipi.it [San Piero a Grado Nuclear Research Group (GRNSPG), University of Pisa, Via Livornese 1291, 56122 San Piero a Grado, Pisa (Italy); Petruzzi, A., E-mail: a.petruzzi@ing.unipi.it [San Piero a Grado Nuclear Research Group (GRNSPG), University of Pisa, Via Livornese 1291, 56122 San Piero a Grado, Pisa (Italy); D’Auria, F., E-mail: f.dauria@ing.unipi.it [San Piero a Grado Nuclear Research Group (GRNSPG), University of Pisa, Via Livornese 1291, 56122 San Piero a Grado, Pisa (Italy); Mazzantini, O., E-mail: mazzantini@na-sa.com.ar [Nucleo-electrica Argentina Sociedad Anonima (NA-SA), Buenos Aires (Argentina)
2014-08-15
Atucha-2 is a Siemens-designed Pressurized Heavy Water Reactor (PHWR) reactor under construction in the Republic of Argentina. Its geometrical complexity and peculiarity (e.g. oblique Control Rods, Positive Void coefficient) required a developed and validated complex three dimensional (3D) neutron kinetics (NK) coupled thermal hydraulic (TH) model. Reactor shut-down is obtained by oblique CRs and, during accidental conditions, by an emergency shut-down system (JDJ) injecting a highly concentrated boron solution (boron clouds) in the moderator tank. The boron clouds reconstruction is obtained using a Computational Fluid Dynamics (CFD) CFX code calculation. A complete Large Break Loss Of Coolant Accident (LBLOCA) calculation implies the application of the RELAP5-3D{sup ©} system code. Within the framework of the third Agreement “Nucleoelèctrica Argentina-Sociedad Anonima (NA-SA) – University of Pisa/GRNSPG” (Contract, 2009), a new RELAP5-3D control system for the boron injection system was developed and implemented in the validated coupled RELAP5-3D/NESTLE model of the Atucha 2 NPP. The aim of this activity is to find out the limiting case (maximum break area size) for the Peak Cladding Temperature for LOCAs under fixed boundary conditions.
DEFF Research Database (Denmark)
Rasmussen, Jens
1990-01-01
are developed to support production planning and control processes as they are found in the present organizations. In this case, the result has been the evolution of "islands of automation" and in the CIM literature, integration is widely discussed in terms of standardization of communication protocols...... processes and production planning in these cases involve decisions within several different work domains which are normally known in detail by different people. The cases are used to illustrate how an explicit representation of the means-ends relations of the work domain can be used to identify......A predominant interest in recent design research has been the development of a general model of the design process to formulate a framework within which support systems based on modern information technology can be developed. Similarly, for manufacturing systems, advanced information systems...
Energy Technology Data Exchange (ETDEWEB)
Luz, Lucas Thadeu Orihuela da
1990-12-01
An integrated environment which was developed to give support to the activities of identification, modeling, analysis and simulation of control systems of Electric Power Systems - EPS is presented. It is a computer program to be used in compatible IBM P C microcomputers. The program has two algorithms for systems identification: one for identification in the frequency domain and other to be used in the time domain. For the analysis, were implemented the algorithms of the frequency response and the root locus. One can simulate multivariable and nonlinear models. A set of auxiliary transformations is available to obtain the dynamic equation of the model declared for the simulation and to transform it in a transfer matrix. The hierarchical menus used for the selection of the program functions are presented and the dialogues and the others communication mechanisms with the user are described. The program in EPS's is demonstrated through the design of a Power System Stabilizer. The results obtained with the program are compared with the ones that were obtained in field tests. (author)
Inverse Problems for Matrix Exponential in System Identification: System Aliasing
Yue, Zuogong; Thunberg, Johan; Goncalves, Jorge
2016-01-01
This note addresses identification of the $A$-matrix in continuous time linear dynamical systems on state-space form. If this matrix is partially known or known to have a sparse structure, such knowledge can be used to simplify the identification. We begin by introducing some general conditions for solvability of the inverse problems for matrix exponential. Next, we introduce "system aliasing" as an issue in the identification of slow sampled systems. Such aliasing give rise to non-unique mat...
2010 United States Automatic Identification System Database
National Oceanic and Atmospheric Administration, Department of Commerce — The 2010 United States Automatic Identification System Database contains vessel traffic data for planning purposes within the U.S. coastal waters. The database is...
Advanced 3D Object Identification System Project
National Aeronautics and Space Administration — Optra will build an Advanced 3D Object Identification System utilizing three or more high resolution imagers spaced around a launch platform. Data from each imager...
2014 United States Automatic Identification System Database
National Oceanic and Atmospheric Administration, Department of Commerce — The 2014 United States Automatic Identification System Database contains vessel traffic data for planning purposes within the U.S. coastal waters. The database is...
2009 United States Automatic Identification System Database
National Oceanic and Atmospheric Administration, Department of Commerce — The 2009 United States Automatic Identification System Database contains vessel traffic data for planning purposes within the U.S. coastal waters. The database is...
2011 United States Automatic Identification System Database
National Oceanic and Atmospheric Administration, Department of Commerce — The 2011 United States Automatic Identification System Database contains vessel traffic data for planning purposes within the U.S. coastal waters. The database is...
2012 United States Automatic Identification System Database
National Oceanic and Atmospheric Administration, Department of Commerce — The 2012 United States Automatic Identification System Database contains vessel traffic data for planning purposes within the U.S. coastal waters. The database is...
Nonlinear system identification and control based on modular neural networks.
Puscasu, Gheorghe; Codres, Bogdan
2011-08-01
A new approach for nonlinear system identification and control based on modular neural networks (MNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This is obtained using a partitioning algorithm. Each local nonlinear model is associated with a nonlinear controller. These are also implemented by neural networks. The switching between the neural controllers is done by a dynamical switcher, also implemented by neural networks, that tracks the different operating points. The proposed multiple modelling and control strategy has been successfully tested on simulated laboratory scale liquid-level system.
Smart Card Identification Management Over A Distributed Database Model
Directory of Open Access Journals (Sweden)
Olatubosun Olabode
2011-01-01
Full Text Available Problem statement: An effective national identification system is a necessity in any national government for the proper implementation and execution of its governmental policies and duties. Approach: Such data can be held in a database relation in a distributed database environment. Till date, The Nigerian government is yet to have an effective and efficient National Identification Management System despite the huge among of money expended on the project. Results: This article presents a Smart Card Identification Management System over a Distributed Database Model. The model was implemented using a client/server architecture between a server and multiple clients. The programmable smart card to store identification detail, including the biometric feature was proposed. Among many other variables stored in the smart card includes individual information on personal identification number, gender, date of birth, place of birth, place of residence, citizenship, continuously updated information on vital status and the identity of parents and spouses. Conclusion/Recommendations: A conceptualization of the database structures and architecture of the distributed database model is presented. The designed distributed database model was intended to solve the lingering problems associated with multiple identification in a society.
Intelligent Storage System Based on Automatic Identification
Directory of Open Access Journals (Sweden)
Kolarovszki Peter
2014-09-01
Full Text Available This article describes RFID technology in conjunction with warehouse management systems. Article also deals with automatic identification and data capture technologies and each processes, which are used in warehouse management system. It describes processes from entering goods into production to identification of goods and also palletizing, storing, bin transferring and removing goods from warehouse. Article focuses on utilizing AMP middleware in WMS processes in Nowadays, the identification of goods in most warehouses is carried through barcodes. In this article we want to specify, how can be processes described above identified through RFID technology. All results are verified by measurement in our AIDC laboratory, which is located at the University of Žilina, and also in Laboratory of Automatic Identification Goods and Services located in GS1 Slovakia. The results of our research bring the new point of view and indicate the ways using of RFID technology in warehouse management system.
The odontology victim identification skill assessment system.
Zohn, Harry K; Dashkow, Sheila; Aschheim, Kenneth W; Dobrin, Lawrence A; Glazer, Howard S; Kirschbaum, Mitchell; Levitt, Daniel; Feldman, Cecile A
2010-05-01
Mass fatality identification efforts involving forensic odontology can involve hundreds of dental volunteers. A literature review was conducted and forensic odontologists and dental educators consulted to identify lessons learned from past mass fatality identification efforts. As a result, the authors propose a skill assessment system, the Odontology Victim Identification Skill Assessment System (OVID-SAS), which details qualifications required to participate on the Antemortem, Postmortem, Ante/Postmortem Comparison, Field, and Shift Leader/Initial Response Teams. For each qualification, specific skills have been identified along with suggested educational pedagogy and skill assessment methods. Courses and assessments can be developed by dental schools, professional associations, or forensic organizations to teach and test for the skills required for dental volunteers to participate on each team. By implementing a system, such as OVID-SAS, forensic odontologists responsible for organizing and managing a forensic odontology mass fatality identification effort will be able to optimally utilize individuals presenting with proven skills.
Mirbagheri, Mehdi M; Kindig, Matthew; Niu, Xun; Varoqui, Deborah; Conaway, Petra
2013-06-01
In this study, the effect of the LOKOMAT, a robotic-assisted locomotor training system, on the reduction of neuromuscular abnormalities associated with spasticity was examined, for the first time in the spinal cord injury (SCI) population. Twenty-three individuals with chronic incomplete SCI received 1-hour training sessions in the LOKOMAT three times per week, with up to 45 minutes of training per session; matched control group received no intervention. The neuromuscular properties of the spastic ankle were then evaluated prior to training and after 1, 2, and 4 weeks of training. A parallel-cascade system identification technique was used to determine the reflex and intrinsic stiffness of the ankle joint as a function of ankle position at each time point. The slope of the stiffness vs. joint angle curve, i.e. the modulation of stiffness with joint position, was then calculated and tracked over the four-week period. Growth Mixture Modeling (GMM), an advanced statistical method, was then used to classify subjects into subgroups based on similar trends in recovery pattern of slope over time, and Random Coefficient Regression (RCR) was used to model the recovery patterns within each subgroup. All groups showed significant reductions in both reflex and intrinsic slope over time, but subjects in classes with higher baseline values of the slope showed larger improvements over the four weeks of training. These findings suggest that LOKOMAT training may also be useful for reducing the abnormal modulation of neuromuscular properties that arises as secondary effects after SCI. This can advise clinicians as to which patients can benefit the most from LOKOMAT training prior to beginning the training. Further, this study shows that system identification and GMM/RCR can serve as powerful tools to quantify and track spasticity over time in the SCI population.
Identification of System Parameters by the Random Decrement Technique
DEFF Research Database (Denmark)
Brincker, Rune; Kirkegaard, Poul Henning; Rytter, Anders
1991-01-01
The aim of this paper is to investigate and illustrate the possibilities of using correlation functions estimated by the Random Decrement Technique as a basis for parameter identification. A two-stage system identification system is used: first, the correlation functions are estimated by the Rand......-Walker equations and finally, least-square fitting of the theoretical correlation function. The results are compared to the results of fitting an Auto Regressive Moving Average (ARMA) model directly to the system output from a single-degree-of-freedom system loaded by white noise....
Nonlinear state space model identification of synchronous generators
Energy Technology Data Exchange (ETDEWEB)
Dehghani, M.; Nikravesh, S.K.Y. [Electrical Engineering Department, Amirkabir University of Technology, Tehran (Iran)
2008-05-15
A method for identification of a synchronous generator is suggested in this paper. The method uses the theoretical relations of machine parameters and the Prony method to find the state space model of the system. Such models are useful for controller design and stability tests. The proposed identification method is applied to a third order model of a synchronous generator. In this study, the field voltage is considered as the input and the active output power and the rotor angle are considered as the outputs of the synchronous generator. Simulation results show good accuracy of the identified model. (author)
Nuclear Materials Identification System Operational Manual
Energy Technology Data Exchange (ETDEWEB)
Chiang, L.G.
2001-04-10
This report describes the operation and setup of the Nuclear Materials Identification System (NMIS) with a {sup 252}Cf neutron source at the Oak Ridge Y-12 Plant. The components of the system are described with a description of the setup of the system along with an overview of the NMIS measurements for scanning, calibration, and confirmation of inventory items.
Identification of linear stochastic systems through projection filters
Chen, Chung-Wen; Huang, Jen-Kuang; Juang, Jer-Nan
1992-01-01
A novel method is presented for identifying a state-space model and a state estimator for linear stochastic systems from input and output data. The method is primarily based on the relationship between the state-space model and the finite-difference model of linear stochastic systems derived through projection filters. It is proved that least-squares identification of a finite difference model converges to the model derived from the projection filters. System pulse response samples are computed from the coefficients of the finite difference model.
Nonlinear system identification in offshore structural reliability
Energy Technology Data Exchange (ETDEWEB)
Spanos, P.D. [Rice Univ., Houston, TX (United States); Lu, R. [Hudson Engineering Corporation, Houston, TX (United States)
1995-08-01
Nonlinear forces acting on offshore structures are examined from a system identification perspective. The nonlinearities are induced by ocean waves and may become significant in many situations. They are not necessarily in the form of Morison`s equation. Various wave force models are examined. The force function is either decomposed into a set of base functions or it is expanded in terms of the wave and structural kinematics. The resulting nonlinear system is decomposed into a number of parallel no-memory nonlinear systems, each followed by a finite-memory linear system. A conditioning procedure is applied to decouple these linear sub-systems; a frequency domain technique involving autospectra and cross-spectra is employed to identify the linear transfer functions. The structural properties and those force transfer parameters are determine with the aid of the coherence functions. The method is verified using simulated data. It provides a versatile and noniterative approach for dealing with nonlinear interaction problems encountered in offshore structural analysis and design.
CEAI: CCM based Email Authorship Identification Model
DEFF Research Database (Denmark)
Nizamani, Sarwat; Memon, Nasrullah
2013-01-01
In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some...... more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based...... reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10...
System Identification of X-33 Neural Network
Aggarwal, Shiv
2003-01-01
Modern flight control research has improved spacecraft survivability as its goal. To this end we need to have a failure detection system on board. In case the spacecraft is performing imperfectly, reconfiguration of control is needed. For that purpose we need to have parameter identification of spacecraft dynamics. Parameter identification of a system is called system identification. We treat the system as a black box which receives some inputs that lead to some outputs. The question is: what kind of parameters for a particular black box can correlate the observed inputs and outputs? Can these parameters help us to predict the outputs for a new given set of inputs? This is the basic problem of system identification. The X33 was supposed to have the onboard capability of evaluating the current performance and if needed to take the corrective measures to adapt to desired performance. The X33 is comprised of both rocket and aircraft vehicle design characteristics and requires, in general, analytical methods for evaluating its flight performance. Its flight consists of four phases: ascent, transition, entry and TAEM (Terminal Area Energy Management). It spends about 200 seconds in ascent phase, reaching an altitude of about 180,000 feet and a speed of about 10 to 15 Mach. During the transition phase which lasts only about 30 seconds, its altitude may increase to about 190,000 feet but its speed is reduced to about 9 Mach. At the beginning of this phase, the Main Engine is Cut Off (MECO) and the control is reconfigured with the help of aerosurfaces (four elevons, two flaps and two rudders) and reaction control system (RCS). The entry phase brings down the altitude of X33 to about 90,000 feet and its speed to about Mach 3. It spends about 250 seconds in this phase. Main engine is still cut off and the vehicle is controlled by complex maneuvers of aerosurfaces. The last phase TAEM lasts for about 450 seconds and the altitude and speed, both are reduced to zero. The
Modeling and Model Identification of Autonomous Underwater Vehicles
2015-06-01
IDENTIFICATION OF AUTONOMOUS UNDERWATER VEHICLES by Jose Alberti June 2015 Thesis Advisor: Noel du Toit Second Reader: Douglas...Master’s Thesis 4. TITLE AND SUBTITLE MODELING AND MODEL IDENTIFICATION OF AUTONOMOUS UNDERWATER VEHICLES 5. FUNDING NUMBERS 6. AUTHOR(S...unlimited 12b. DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) As autonomous underwater vehicles (AUVs) are deployed in more complex
Theory analysis and system identification methods on thermal dynamics characteristics of ballscrews
Institute of Scientific and Technical Information of China (English)
Junyong XIA; Youmin HU; Bo WU; Tielin SHI
2008-01-01
Empirical model of machine tools on thermal error has been widely researched, which can compensate for thermal error to some extent but not suitable for ther-mal dynamic errors produced by dynamic heat sources. The thermoelastic phenomenon of unidimensional heat transfer of ballscrews influenced by changeable heat sources is analyzed based on the theory of heat transfer. Two methods for system identification (the least square system identification and BP artificial neural network (ANN) system identification) are put forward to establish a dynamic characteristic model of thermal deformation of ballscrews. The model of thermal error of the X axis in a feed system of DM4600 vertical miller is established with a fine identification effect. Comparing the results of the two identification methods, the BP ANN system identification is more precise than the least square system identification.
Directory of Open Access Journals (Sweden)
K. Asan Mohideen
2014-07-01
Full Text Available Improving the transient performance of the MRAC has been a point of research for a long time. The main objective of the paper is to design an MRAC with improved transient and steady state performance. This paper proposes a Fuzzy modified MRAC (FMRAC to control a coupled tank level process. The FMRAC uses a proportional control based Mamdani-type Fuzzy inference system (MFIS to improve the transient performance of a direct MRAC. In addition, it proposes the application of Differential Evolution (DE algorithm to tune the membership function parameters off-line of the FMRAC to improve its performance further. The proposed controller is called DE based Fuzzy Modified Model Reference Adaptive Controller (DEFMRAC. In this study, an MRAC, an FMRAC and the proposed DEFMRAC are designed for a coupled tank level process and their performances are compared. The coupled tank level process is modeled by using system identification procedure and the accuracy of the resultant model is further improved by parameter tuning using DE. The simulation results show that the FMRAC gives better transient performance than the direct MRAC. The results also show that the proposed DEFMRAC gives better transient performance than the direct MRAC or the FMRAC. It is concluded that the proposed controller can be used to obtain very good transient and steady state performance in the control of nonlinear processes.
Classification Models for Symmetric Key Cryptosystem Identification
Directory of Open Access Journals (Sweden)
Shri Kant
2012-01-01
Full Text Available The present paper deals with the basic principle and theory behind prevalent classification models and their judicious application for symmetric key cryptosystem identification. These techniques have been implemented and verified on varieties of known and simulated data sets. After establishing the techniques the problems of cryptosystem identification have been addressed.Defence Science Journal, 2012, 62(1, pp.38-45, DOI:http://dx.doi.org/10.14429/dsj.62.1440
Online identification of nonlinear spatiotemporal systems using kernel learning approach.
Ning, Hanwen; Jing, Xingjian; Cheng, Li
2011-09-01
The identification of nonlinear spatiotemporal systems is of significance to engineering practice, since it can always provide useful insight into the underlying nonlinear mechanism and physical characteristics under study. In this paper, nonlinear spatiotemporal system models are transformed into a class of multi-input-multi-output (MIMO) partially linear systems (PLSs), and an effective online identification algorithm is therefore proposed by using a pruning error minimization principle and least square support vector machines. It is shown that many benchmark physical and engineering systems can be transformed into MIMO-PLSs which keep some important physical spatiotemporal relationships and are very helpful in the identification and analysis of the underlying system. Compared with several existing methods, the advantages of the proposed method are that it can make full use of some prior structural information about system physical models, can realize online estimation of the system dynamics, and achieve accurate characterization of some important nonlinear physical characteristics of the system. This would provide an important basis for state estimation, control, optimal analysis, and design of nonlinear distributed parameter systems. The proposed algorithm can also be applied to identification problems of stochastic spatiotemporal dynamical systems. Numeral examples and comparisons are given to demonstrate our results.
A portable air jet actuator device for mechanical system identification.
Belden, Jesse; Staats, Wayne L; Mazumdar, Anirban; Hunter, Ian W
2011-03-01
System identification of limb mechanics can help diagnose ailments and can aid in the optimization of robotic limb control parameters and designs. An interesting fluid phenomenon--the Coandă effect--is utilized in a portable actuator to provide a stochastic binary force disturbance to a limb system. The design of the actuator is approached with the goal of creating a portable device which could be deployed on human or robotic limbs for in situ mechanical system identification. The viability of the device is demonstrated by identifying the parameters of an underdamped elastic beam system with fixed inertia and stiffness and variable damping. The nonparametric compliance impulse response yielded from the system identification is modeled as a second-order system and the resultant parameters are found to be in excellent agreement with those found using more traditional system identification techniques. The current design could be further miniaturized and developed as a portable, wireless, unrestrained mechanical system identification instrument for less intrusive and more widespread use.
Algorithms for Digital Micro-Wave Receivers and Optimal System Identification.
1994-02-28
estimation, Frequency estimation, Digital receiver design, Improved AR and ARMA modeling, Electronic Warfare (EW) signal detection, Optimal system identification from input/output and frequency domain data.
System identification.Part B:Basic models for system description%系统辨识(2):系统描述的基本模型
Institute of Scientific and Technical Information of China (English)
丁锋
2011-01-01
控制是一切科学问题的核心.数学模型是一切控制问题的基础.事物的运动规律用方程描述就是数学模型.不同学科的发展就是建立其数学模型的过程.本文首次把线性动态系统数学模型分为三类:时间序列模型,方程误差类模型,输出误差类模型;详细介绍了线性系统的一些基本数学模型,包括连续系统离散化和模型等价变换,单输入单输出随机系统模型,多变量系统模型,类多变量系统模型,多输入和多输出系统模型(传递函数阵主模型、子模型、子子模型,多输入单输出系统模型,单输入多输出系统模型等).%Control is the core of all scientific issues. Mathematical models are the basis of all control problems. The movement law of things described by equations is the mathematical model. The development of different disciplines is to establish the process of their mathematical models. This paper divides the mathematical models of the linear dynamic systems into three categories : the time-series models ,the equation error type models and the output error type models , and introduces basic mathematical models of linear systems in detail , including the discretization of continuous-time system models and model equivalence transform , single-input single-output stochastic system , multivariable systems,multivariable-like systems, multiple-input and multiple-output systems such as the main model, submodel and sub-submodel of the transfer function matrix, multiple-input single-output systems, and single-input multiple-output systems.
Directory of Open Access Journals (Sweden)
Yaneth Osorio
Full Text Available BACKGROUND: New drugs are needed to treat visceral leishmaniasis (VL because the current therapies are toxic, expensive, and parasite resistance may weaken drug efficacy. We established a novel ex vivo splenic explant culture system from hamsters infected with luciferase-transfected Leishmania donovani to screen chemical compounds for anti-leishmanial activity. METHODOLOGY/PRINCIPAL FINDINGS: THIS MODEL HAS ADVANTAGES OVER IN VITRO SYSTEMS IN THAT IT: 1 includes the whole cellular population involved in the host-parasite interaction; 2 is initiated at a stage of infection when the immunosuppressive mechanisms that lead to progressive VL are evident; 3 involves the intracellular form of Leishmania; 4 supports parasite replication that can be easily quantified by detection of parasite-expressed luciferase; 5 is adaptable to a high-throughput screening format; and 6 can be used to identify compounds that have both direct and indirect anti-parasitic activity. The assay showed excellent discrimination between positive (amphotericin B and negative (vehicle controls with a Z' Factor >0.8. A duplicate screen of 4 chemical libraries containing 4,035 compounds identified 202 hits (5.0% with a Z score of <-1.96 (p<0.05. Eighty-four (2.1% of the hits were classified as lead compounds based on the in vitro therapeutic index (ratio of the compound concentration causing 50% cytotoxicity in the HepG(2 cell line to the concentration that caused 50% reduction in the parasite load. Sixty-nine (82% of the lead compounds were previously unknown to have anti-leishmanial activity. The most frequently identified lead compounds were classified as quinoline-containing compounds (14%, alkaloids (10%, aromatics (11%, terpenes (8%, phenothiazines (7% and furans (5%. CONCLUSIONS/SIGNIFICANCE: The ex vivo splenic explant model provides a powerful approach to identify new compounds active against L. donovani within the pathophysiologic environment of the infected spleen
Dynamic system multivariate calibration by system identification methods
Directory of Open Access Journals (Sweden)
Rolf Ergon
1998-04-01
Full Text Available In the first part of the paper, the optimal estimator for normally nonmeasured primary outputs from a linear and time invariant dynamic system is developed. The estimator is based on an underlying Kalman filter, utilizing all available information in known inputs and measured secondary outputs. Assuming sufficient experimental data, the optimal estimator can be identified by specifying an output error model in a standard prediction error identification method. It is further shown that static estimators found by the ordinary least squares method or multivariate calibration by means of principal component regression (PCR or partial least squares regression (PLSR can be seen as special cases of the optimal dynamic estimator. Finally, it is shown that dynamic system PCR and PLSR solutions can be developed as special cases of the general estimator for dynamic systems.
System identification of the Arabidopsis plant circadian system
Foo, Mathias; Somers, David E.; Kim, Pan-Jun
2015-02-01
The circadian system generates an endogenous oscillatory rhythm that governs the daily activities of organisms in nature. It offers adaptive advantages to organisms through a coordination of their biological functions with the optimal time of day. In this paper, a model of the circadian system in the plant Arabidopsis (species thaliana) is built by using system identification techniques. Prior knowledge about the physical interactions of the genes and the proteins in the plant circadian system is incorporated in the model building exercise. The model is built by using primarily experimentally-verified direct interactions between the genes and the proteins with the available data on mRNA and protein abundances from the circadian system. Our analysis reveals a great performance of the model in predicting the dynamics of the plant circadian system through the effect of diverse internal and external perturbations (gene knockouts and day-length changes). Furthermore, we found that the circadian oscillatory rhythm is robust and does not vary much with the biochemical parameters except those of a light-sensitive protein P and a transcription factor TOC1. In other words, the circadian rhythmic profile is largely a consequence of the network's architecture rather than its particular parameters. Our work suggests that the current experimental knowledge of the gene-to-protein interactions in the plant Arabidopsis, without considering any additional hypothetical interactions, seems to suffice for system-level modeling of the circadian system of this plant and to present an exemplary platform for the control of network dynamics in complex living organisms.
Parameter identification in the logistic STAR model
DEFF Research Database (Denmark)
Ekner, Line Elvstrøm; Nejstgaard, Emil
We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that th......We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter...
Bounding approaches to system identification
Norton, John; Piet-Lahanier, Hélène; Walter, Éric
1996-01-01
In response to the growing interest in bounding error approaches, the editors of this volume offer the first collection of papers to describe advances in techniques and applications of bounding of the parameters, or state variables, of uncertain dynamical systems. Contributors explore the application of the bounding approach as an alternative to the probabilistic analysis of such systems, relating its importance to robust control-system design.
Parameter identification of stochastic diffusion systems with unknown boundary conditions
Aihara, Shin Ichi; Bagchi, Arunabha
2013-01-01
This paper treats the filtering and parameter identification for the stochastic diffusion systems with unknown boundary conditions. The physical situation of the unknown boundary conditions can be found in many industrial problems,i.g., the salt concentration model of the river Rhine is a typical ex
Time-Varying FOPDT System Identification with Unknown Disturbance Input
DEFF Research Database (Denmark)
Sun, Zhen; Yang, Zhenyu
2012-01-01
The Time-Varying First Order Plus Dead Time (TV-FOPDT) model is an extension of the conventional FOPDT by allowing the system parameters, which are primarily defined on the transfer function description, i.e., the DC-gain, time constant and time delay, to be time dependent. The TV-FOPDT identific...
Time-Delay System Identification Using Genetic Algorithm
DEFF Research Database (Denmark)
Yang, Zhenyu; Seested, Glen Thane
2013-01-01
problem through an identification approach using the real coded Genetic Algorithm (GA). The desired FOPDT/SOPDT model is directly identified based on the measured system's input and output data. In order to evaluate the quality and performance of this GA-based approach, the proposed method is compared...
Soft Sensor Model Derived from Wiener Model Structure:Modeling and Identification
Institute of Scientific and Technical Information of China (English)
曹鹏飞; 罗雄麟
2014-01-01
The processes of building dynamic and static relationships between secondary and primary variables are usually integrated in most of nonlinear dynamic soft sensor models. However, such integration limits the estimation accuracy of soft sensor models. Wiener model effectively describes dynamic and static characteristics of a system with the structure of dynamic and static submodels in cascade. We propose a soft sensor model derived from Wiener model structure, which is an extension of Wiener model. Dynamic and static relationships between secondary and primary variables are built respectively to describe the dynamic and static characteristics of system. The feasibility of this model is verified. Then the expression of discrete model is derived for soft sensor system. Conjugate gradi-ent algorithm is applied to identify the dynamic and static model parameters alternately. Corresponding update method for soft sensor system is also given. Case studies confirm the effectiveness of the proposed model, alternate identification algorithm, and update method.
System identification based approach to dynamic weighing revisited
Niedźwiecki, Maciej; Meller, Michał; Pietrzak, Przemysław
2016-12-01
Dynamic weighing, i.e., weighing of objects in motion, without stopping them on the weighing platform, allows one to increase the rate of operation of automatic weighing systems, used in industrial production processes, without compromising their accuracy. Since the classical identification-based approach to dynamic weighing, based on the second-order mass-spring-damper model of the weighing system, does not yield satisfactory results when applied to conveyor belt type checkweighers, several extensions of this technique are examined. Experiments confirm that when appropriately modified the identification-based approach becomes a reliable tool for dynamic mass measurement in checkweighers.
Identification of parameters of discrete-continuous models
Energy Technology Data Exchange (ETDEWEB)
Cekus, Dawid, E-mail: cekus@imipkm.pcz.pl; Warys, Pawel, E-mail: warys@imipkm.pcz.pl [Institute of Mechanics and Machine Design Foundations, Czestochowa University of Technology, Dabrowskiego 73, 42-201 Czestochowa (Poland)
2015-03-10
In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible.
Learning theory and system identification; Gakushu riron to system dotei
Energy Technology Data Exchange (ETDEWEB)
Adachi, S. [Utsunomiya Univ. (Japan). Faculty of Engineering
1998-04-10
The relationship between learning theory and system identification theory is described. The learning theory is mainly being studied by neural network community, while the system identification theory is mainly being discussed in the community of control system design and failure detection. The relation between the two theories has been studied. In this paper, the relation is explained by focusing on the following two points: (1) The relationship between learning method such as error reverse propagation method and on-line system identification is discussed from the viewpoint of robust estimation. (2) The relationship between PAC (probably approximately correct) learning which is recently attracting the attention among many learning theories and system identification theories is investigated. 33 refs.
Numerical studies of identification in nonlinear distributed parameter systems
Banks, H. T.; Lo, C. K.; Reich, Simeon; Rosen, I. G.
1989-01-01
An abstract approximation framework and convergence theory for the identification of first and second order nonlinear distributed parameter systems developed previously by the authors and reported on in detail elsewhere are summarized and discussed. The theory is based upon results for systems whose dynamics can be described by monotone operators in Hilbert space and an abstract approximation theorem for the resulting nonlinear evolution system. The application of the theory together with numerical evidence demonstrating the feasibility of the general approach are discussed in the context of the identification of a first order quasi-linear parabolic model for one dimensional heat conduction/mass transport and the identification of a nonlinear dissipation mechanism (i.e., damping) in a second order one dimensional wave equation. Computational and implementational considerations, in particular, with regard to supercomputing, are addressed.
Ultra-Wideband Radio Frequency Identification Systems
Nekoogar, Faranak
2012-01-01
Ultra-Wideband Radio Frequency Identification Systems describes the essentials of radio frequency identification systems as well as their target markets. The authors provide a study of commercially available RFID systems and characterizes their performance in terms of read range and reliability in the presence of conductive and dielectric materials. The capabilities and limitations of some commercial RFID systems are reported followed by comprehensive discussions of the advantages and challenges of using ultra-wideband technology for tag/reader communications. The book presents practical aspects of UWB RFID system such as: pulse generation, remote powering, tag and reader antenna design, as well as special applications of UWB RFIDs in a simple and easy-to-understand language.
Authentication Without Identification using Anonymous Credential System
Damodaram, A
2009-01-01
Privacy and security are often intertwined. For example, identity theft is rampant because we have become accustomed to authentication by identification. To obtain some service, we provide enough information about our identity for an unscrupulous person to steal it (for example, we give our credit card number to Amazon.com). One of the consequences is that many people avoid e-commerce entirely due to privacy and security concerns. The solution is to perform authentication without identification. In fact, all on-line actions should be as anonymous as possible, for this is the only way to guarantee security for the overall system. A credential system is a system in which users can obtain credentials from organizations and demonstrate possession of these credentials. Such a system is anonymous when transactions carried out by the same user cannot be linked. An anonymous credential system is of significant practical relevance because it is the best means of providing privacy for users.
Smart system for gesture identification
Pérez Obiols, Eduard
2014-01-01
A new interface system for control and input of data for automotive applications will be developed. The technology will be based on capacitive sensors. This thesis project is centered on developing and integrating a contactless system based on automatic recognition of gestures to allow interaction between car driver/passenger and some selected car functions in the automotive environment. Este proyecto se centra en el desarrollo y la integración de un sistema contactless (sin contacto) b...
System Identification and POD Method Applied to Unsteady Aerodynamics
Tang, Deman; Kholodar, Denis; Juang, Jer-Nan; Dowell, Earl H.
2001-01-01
The representation of unsteady aerodynamic flow fields in terms of global aerodynamic modes has proven to be a useful method for reducing the size of the aerodynamic model over those representations that use local variables at discrete grid points in the flow field. Eigenmodes and Proper Orthogonal Decomposition (POD) modes have been used for this purpose with good effect. This suggests that system identification models may also be used to represent the aerodynamic flow field. Implicit in the use of a systems identification technique is the notion that a relative small state space model can be useful in describing a dynamical system. The POD model is first used to show that indeed a reduced order model can be obtained from a much larger numerical aerodynamical model (the vortex lattice method is used for illustrative purposes) and the results from the POD and the system identification methods are then compared. For the example considered, the two methods are shown to give comparable results in terms of accuracy and reduced model size. The advantages and limitations of each approach are briefly discussed. Both appear promising and complementary in their characteristics.
Cost Optimal System Identification Experiment Design
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
the experiment design are not based on obtained experimental data. Instead the decisions are based on the expected experimental data assumed to be obtained from the measurements, estimated based on prior information and engineering judgement. The design method provides a system identification experiment design...
Information, Consistent Estimation and Dynamic System Identification.
1976-11-01
the chesis . The rest of Chapter 4 4 is believed to be of theoretical interest and also of practical value, I which is demonstrated in sections 6.1...in the mean of the identification procedures at a certain rate. The condition in (6.3) also involves the system’s coefficients and thus, the selected
Improved system identification with Renormalization Group.
Wang, Qing-Guo; Yu, Chao; Zhang, Yong
2014-09-01
This paper proposes an improved system identification method with Renormalization Group. Renormalization Group is applied to a fine data set to obtain a coarse data set. The least squares algorithm is performed on the coarse data set. The theoretical analysis under certain conditions shows that the parameter estimation error could be reduced. The proposed method is illustrated with examples.
DIRC, the Particle Identification System for BABAR
Schwiening, J; Aleksan, Roy; Aston, D; Benkebil, M; Bernard, D; Bonneaud, G R; Brochard, F; Brown, D N; Bourgeois, P; Chauveau, J; Cohen-Tanugi, J; Convery, M; De Domenico, G; de Lesquen, A; Emery, S; Ferrag, S; Gaidot, A; Geld, T L; Hamel de Monchenault, G; Hast, C; Höcker, A; Kadel, R W; Kadyk, J A; Lacker, H M; London, G W; Lu, A; Lutz, A M; Lynch, G; Mancinelli, G; Martínez-Vidal, F; Mayer, N; Meadows, B T; Müller, D; Plaszczynski, S; Pripstein, M; Ratcliff, B N; Roos, L; Roussot, E; Schune, M H; Shelkov, V; Sokoloff, M D; Spanier, S M; Stark, J; Telnov, A V; Thiebaux, C; Vasileiadis, G; Vasseur, G; Vavra, J; Verderi, M; Wenzel, W A; Wilson, R J; Wormser, G; Yéche, C; Yellin, S; Zito, M; Schwiening, Jochen
2001-01-01
The DIRC, a novel type of Cherenkov ring imaging device, is the primary hadronic particle identification system for the BABAR detector at the asymmetric B-factory, PEP-II at SLAC. BABAR began taking data with colliding beams mode in late spring 1999. This paper describes the performance of the DIRC during the first 16 months of operation.
雷达特定辐射源识别的直观系统模型%Intuitive systemic models for radar specific emitter identification
Institute of Scientific and Technical Information of China (English)
韩韬; 周一宇
2013-01-01
An intuitive systemic model based on the systemic Yoyos and stochastic differential geometry is provided for finding a meaningful geometric description of radar specific emitter identification (SEI) . The paper points out that there is a lower dimensional state manifold, which generates signals with intrinsic signatures in every emitter, and the geometric significances of the manifold go far towards solving SEI. According to this model, the intrinsic parameters of signals can be used to explain and find effective fingerprints features of a specific emitters. Experiments on actual intercepted radar signals with the same type verify the correctness and validity of the proposed model.%利用Yoyos系统与随机微分几何,对特定辐射源识别问题进行系统建模及数学分析,建立了一种有意义的几何学描述.通过上述模型及分析,指出辐射源个体所辐射信号的瞬时参数中包含具有内蕴性质的指纹特征信息,且由产生信号的辐射源个体的系统低维状态流形决定.提出了一种雷达辐射源指纹特征信息的有效性判据和信号内蕴指纹特征参数.最后通过外场实验数据验证了本文所提出模型及特征的正确性和有效性.
The System Identification and Model Design of Linear Power Amplifier%线性功率放大器模型设计及其系统辨识
Institute of Scientific and Technical Information of China (English)
谢珺耀; 李久芳
2011-01-01
The linear power amplifier can control an ideal state to output voltage and current,so it is widely used in industrial control area.Such as programming voltage source,programming current source,voice motor driver,linear motor driver,etc.The linear power amplifier is mainly based on the power operational amplifier,in performance close to the ideal operational amplifier model,with a high dynamic voltage and current range.It is easy to enhance driving current.In this paper,using the design of the amplifier model,the calculation of transfer function,system performance analysis,simulation and identification with MATLAB to fully verify system performance,and thus make the best system design.%线性功率放大器可对其输出电压和电流进行比较理想的控制,因此在工业控制场合得到了广泛的应用。如可编程电压源、可编程电流源、音圈电机驱动、直线电机驱动等。线性功率放大器主要以功率运算放大器为基础,在性能上接近理想的运算放大器的同时具有较高的动态电压和动态电流范围,如果要进行驱动电流提升,也很容易实现。从放大器的模型设计、传递函数计算、系统性能分析和MATLAB仿真辨识来全面验证系统设计的指标性能,从而做出最佳系统设计。
Heeg, Jennifer; Wieseman, Carol D.
2012-01-01
Orthogonal harmonic multisine excitations were utilized in a wind tunnel test and in simulation of the SemiSpan Supersonic Transport model to assess aeroservoelastic characteristics. Fundamental issues associated with analyzing sinusoidal signals were examined, including spectral leakage, excitation truncation, and uncertainties on frequency response functions and mean-square coherence. Simulation allowed for evaluation of these issues relative to a truth model, while wind tunnel data introduced real-world implementation issues.
Dynamic Model Identification for Ultrasonic Motor Frequency-Speed Control
Institute of Scientific and Technical Information of China (English)
Shi Jingzhuo; Song Le
2015-01-01
The mathematical model of ultrasonic motor (USM ) is the foundation of the motor high performance control .Considering the motor speed control requirements ,the USM control model identification is established with frequency as the independent variable .The frequency-speed control model of USM system is developed ,thus laying foundation for the motor high performance control .The least square method and the extended least square method are used to identify the model .By comparing the results of the identification and measurement ,and fitting the time-varying parameters of the model ,one can show that the model obtained by using the extended least square method is reasonable and possesses high accuracy .Finally ,the frequency-speed control model of USM contains the nonlinear information .
Model Identification of Integrated ARMA Processes
Stadnytska, Tetiana; Braun, Simone; Werner, Joachim
2008-01-01
This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…
Model Identification of Integrated ARMA Processes
Stadnytska, Tetiana; Braun, Simone; Werner, Joachim
2008-01-01
This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…
Identification of Metabolic Pathway Systems
Directory of Open Access Journals (Sweden)
Sepideh eDolatshahi
2016-02-01
Full Text Available The estimation of parameters in even moderately large biological systems is a significant challenge. This challenge is greatly exacerbated if the mathematical formats of appropriate process descriptions are unknown. To address this challenge, the method of dynamic flux estimation (DFE was proposed for the analysis of metabolic time series data. Under ideal conditions, the first phase of DFE yields numerical representations of all fluxes within a metabolic pathway system, either as values at each time point or as plots against their substrates and modulators. However, this numerical result does not reveal the mathematical format of each flux. Thus, the second phase of DFE selects functional formats that are consistent with the numerical trends obtained from the first phase. While greatly facilitating metabolic data analysis, DFE is only directly applicable if the pathway system contains as many dependent variables as fluxes. Because most actual systems contain more fluxes than metabolite pools, this requirement is seldom satisfied. Auxiliary methods have been proposed to alleviate this issue, but they are not general. Here we propose strategies that extend DFE toward general, slightly underdetermined pathway systems.
Identification of Metabolic Pathway Systems.
Dolatshahi, Sepideh; Voit, Eberhard O
2016-01-01
The estimation of parameters in even moderately large biological systems is a significant challenge. This challenge is greatly exacerbated if the mathematical formats of appropriate process descriptions are unknown. To address this challenge, the method of dynamic flux estimation (DFE) was proposed for the analysis of metabolic time series data. Under ideal conditions, the first phase of DFE yields numerical representations of all fluxes within a metabolic pathway system, either as values at each time point or as plots against their substrates and modulators. However, this numerical result does not reveal the mathematical format of each flux. Thus, the second phase of DFE selects functional formats that are consistent with the numerical trends obtained from the first phase. While greatly facilitating metabolic data analysis, DFE is only directly applicable if the pathway system contains as many dependent variables as fluxes. Because most actual systems contain more fluxes than metabolite pools, this requirement is seldom satisfied. Auxiliary methods have been proposed to alleviate this issue, but they are not general. Here we propose strategies that extend DFE toward general, slightly underdetermined pathway systems.
Improved System Identification Approach Using Wavelet Networks
Institute of Scientific and Technical Information of China (English)
石宏理; 蔡远利; 邱祖廉
2005-01-01
A new approach is proposed to improve the general identification algorithm of multidimensional systems using wavelet networks. The general algorithm involves mapping vector input into its norm to avoid problem of dimensionality in construction multidimensional wavelet basis functions. Thus, the basis functions are spherically symmetric without direction selectivity. In order to restore the direction selectivity, the improved approach weights the input variables before mapping it into a scalar form. The weights can be obtained using universal optimization algorithms. Generally, only local optimal weights are obtained. Even so, performance of identification can be improved.
Identification of uncertain nonlinear systems for robust fuzzy control.
Senthilkumar, D; Mahanta, Chitralekha
2010-01-01
In this paper, we consider fuzzy identification of uncertain nonlinear systems in Takagi-Sugeno (T-S) form for the purpose of robust fuzzy control design. The uncertain nonlinear system is represented using a fuzzy function having constant matrices and time varying uncertain matrices that describe the nominal model and the uncertainty in the nonlinear system respectively. The suggested method is based on linear programming approach and it comprises the identification of the nominal model and the bounds of the uncertain matrices and then expressing the uncertain matrices into uncertain norm bounded matrices accompanied by constant matrices. It has been observed that our method yields less conservative results than the other existing method proposed by Skrjanc et al. (2005). With the obtained fuzzy model, we showed the robust stability condition which provides a basis for different robust fuzzy control design. Finally, different simulation examples are presented for identification and control of uncertain nonlinear systems to illustrate the utility of our proposed identification method for robust fuzzy control.
Frameworks in Problems of Structural Identification Systems
Directory of Open Access Journals (Sweden)
Nikolay Karabutov
2017-01-01
Full Text Available The new approach to structural identification of nonlinear dynamic systems under uncertainty is proposed. It is based on the analysis of virtual frameworks (VF, reflecting a state of a nonlinear part system. Construction VF is based on obtaining special an informational set describing a steady state of a nonlinear dynamic system. Introduction VF demands an estimation of structural identifiability of a system. This concept is associated with nonlinearity of system and properties VF. The method of an estimation of structural identifiability is proposed. The appearance of the insignificant virtual frameworks, not satisfying to the condition of structural identifiability, is considered. Algorithms for an estimation of a nonlinearity class on the basis of the analysis of sector sets are proposed. Methods and procedures of the estimation of framework single-valued and multiple -valued nonlinearities are proposed. The method of the structurally-frequency analysis is proposed and applied to validate the obtained solutions. VF is proposed for identification of an order and a spectrum of eigenvalues of a linear dynamic system. The possibility of application VF for the problem solving of identification static systems is shown
A New Autom ated Fingerprint Identification System
Institute of Scientific and Technical Information of China (English)
沈学宁; 程民德; 等
1989-01-01
A new automated fingerpring identification system is proposed.In this system,based on some local properties of digital image,the shape and minutiae features of fingerprint can be extracted from the grey level image without binarizing and thinning.In query,a latent fingerprint can be matched with the filed fingerprints by shape and/or minutiae features.Matching by shape features is much faster than by minutiae.
Recent phylogenetic studies have used DNA as the target molecule for the development of environmental 16S rDNA clone libraries. As DNA may persist in the environment, DNA-based libraries cannot be used to identify metabolically active bacteria in water systems. In this study, a...
System with Distributed Lag: Adaptive Identification and Prediction
Directory of Open Access Journals (Sweden)
Nikolay Karabutov
2016-03-01
Full Text Available Adaptive algorithms of parametric identifica-tion of discrete systems with lag variables are proposed. Adaptive algorithms (AA in the presence of lag input variables are developed. The convergence of the AA and the boundedness of the trajectories the adaptive system is proved. Convergence domain АА depends on operating disturbance. Models with multiplicative parameters (MPM for the decrease of a number estimated parameters are offered. The process for selection of the vector of base parameters MPM was developed. The performance of adaptive system identification for this case is proved. It is shown that parameters of system estimation at the application of multiplicative identification must be chosen from a condition of minimization of the criterion of the prediction error. Transformation of interdependence be-tween the lagged variables is offered, allowing eliminating their effect on system work. In the second part of work, the method of synthesis АА identification of the systems containing lagged output variables is offered. We consider a case of linear correlation between an output of the system and operating disturbance. For a solution of a problem, we suggest fulfilling an estimation of operating disturbance. Corresponding procedures are described and proved their efficiency. Simulation results are presented that confirm the efficiency of the adaptive methods.
An Adaptive Nonlinear Filter for System Identification
Directory of Open Access Journals (Sweden)
Tokunbo Ogunfunmi
2009-01-01
Full Text Available The primary difficulty in the identification of Hammerstein nonlinear systems (a static memoryless nonlinear system in series with a dynamic linear system is that the output of the nonlinear system (input to the linear system is unknown. By employing the theory of affine projection, we propose a gradient-based adaptive Hammerstein algorithm with variable step-size which estimates the Hammerstein nonlinear system parameters. The adaptive Hammerstein nonlinear system parameter estimation algorithm proposed is accomplished without linearizing the systems nonlinearity. To reduce the effects of eigenvalue spread as a result of the Hammerstein system nonlinearity, a new criterion that provides a measure of how close the Hammerstein filter is to optimum performance was used to update the step-size. Experimental results are presented to validate our proposed variable step-size adaptive Hammerstein algorithm given a real life system and a hypothetical case.
Teslic, Luka; Hartmann, Benjamin; Nelles, Oliver; Skrjanc, Igor
2011-12-01
This paper deals with the problem of fuzzy nonlinear model identification in the framework of a local model network (LMN). A new iterative identification approach is proposed, where supervised and unsupervised learning are combined to optimize the structure of the LMN. For the purpose of fitting the cluster-centers to the process nonlinearity, the Gustafsson-Kessel (GK) fuzzy clustering, i.e., unsupervised learning, is applied. In combination with the LMN learning procedure, a new incremental method to define the number and the initial locations of the cluster centers for the GK clustering algorithm is proposed. Each data cluster corresponds to a local region of the process and is modeled with a local linear model. Since the validity functions are calculated from the fuzzy covariance matrices of the clusters, they are highly adaptable and thus the process can be described with a very sparse amount of local models, i.e., with a parsimonious LMN model. The proposed method for constructing the LMN is finally tested on a drug absorption spectral process and compared to two other methods, namely, Lolimot and Hilomot. The comparison between the experimental results when using each method shows the usefulness of the proposed identification algorithm.
System Identification and Robust Control
DEFF Research Database (Denmark)
Tøffner-Clausen, S.
etc. Will generally yield a set of coupled non-linear partial differential equations. These equations can then be linearized (in time and position) around a suitable working point and Laplace transformed for linear control design. The linearized differential equations will typically involve physical...... enter the nominal model in a linear fractional manner. This is, however, a very general perturbation set which includes a large variety of uncertainty such as unstructured and structured dynamic uncertainty (complex perturbations) and parameter variations (real perturbations). The uncertainty structures...
Shabanpoor, Fazel; Hammond, Suzan M; Abendroth, Frank; Hazell, Gareth; Wood, Matthew J A; Gait, Michael J
2017-06-01
Splice-switching antisense oligonucleotides are emerging treatments for neuromuscular diseases, with several splice-switching oligonucleotides (SSOs) currently undergoing clinical trials such as for Duchenne muscular dystrophy (DMD) and spinal muscular atrophy (SMA). However, the development of systemically delivered antisense therapeutics has been hampered by poor tissue penetration and cellular uptake, including crossing of the blood-brain barrier (BBB) to reach targets in the central nervous system (CNS). For SMA application, we have investigated the ability of various BBB-crossing peptides for CNS delivery of a splice-switching phosphorodiamidate morpholino oligonucleotide (PMO) targeting survival motor neuron 2 (SMN2) exon 7 inclusion. We identified a branched derivative of the well-known ApoE (141-150) peptide, which as a PMO conjugate was capable of exon inclusion in the CNS following systemic administration, leading to an increase in the level of full-length SMN2 transcript. Treatment of newborn SMA mice with this peptide-PMO (P-PMO) conjugate resulted in a significant increase in the average lifespan and gains in weight, muscle strength, and righting reflexes. Systemic treatment of adult SMA mice with this newly identified P-PMO also resulted in small but significant increases in the levels of SMN2 pre-messenger RNA (mRNA) exon inclusion in the CNS and peripheral tissues. This work provides proof of principle for the ability to select new peptide paradigms to enhance CNS delivery and activity of a PMO SSO through use of a peptide-based delivery platform for the treatment of SMA potentially extending to other neuromuscular and neurodegenerative diseases.
Modeling of Network Identification Capability.
1986-07-01
scalar moment is assumed to follow a Poisson distribution, as suggested by Lomnitz (1966). The A cumulative number of events occurring per year at or...Spectral Ratios from Point Sources in Plane-Layered Earth V Models," BSSA. 60, pp 1937-1987 Lomnitz . C. (1966). -Statistical Prediction of Earthquakes...Moment-Magritude Relations in Theory and Practice," J Geophy. Res., 89 (B7). pp. 6229-6235. Lomnitz , C. (1966), Statistical Prediction of Earthquakes
Identification for a class of distributed parameter systems
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
This paper discusses the identification for the distributed parameter system of gas reservoirs with het erogeneous carbonate matrices. Based on the relationship between the ad hoc function and the geological feature, we set up a general model with double porous structure, clarify its effect and significance, and present a series of results of sys tem identifications, including the basic content and methods, stabilizing functional and parameter identifiability etc. Us ing the perturbation of spectra of self-adjoint operators, the identifiability of the porosities and the ad hoc coefficient is demonstrated for the general structure model. This project indicates that the identification of a distributed parameter sys tem involves parameters, boundary position and structure.
Dragos, Kosmas; Smarsly, Kay
2016-04-01
System identification has been employed in numerous structural health monitoring (SHM) applications. Traditional system identification methods usually rely on centralized processing of structural response data to extract information on structural parameters. However, in wireless SHM systems the centralized processing of structural response data introduces a significant communication bottleneck. Exploiting the merits of decentralization and on-board processing power of wireless SHM systems, many system identification methods have been successfully implemented in wireless sensor networks. While several system identification approaches for wireless SHM systems have been proposed, little attention has been paid to obtaining information on the physical parameters (e.g. stiffness, damping) of the monitored structure. This paper presents a hybrid system identification methodology suitable for wireless sensor networks based on the principles of component mode synthesis (dynamic substructuring). A numerical model of the monitored structure is embedded into the wireless sensor nodes in a distributed manner, i.e. the entire model is segmented into sub-models, each embedded into one sensor node corresponding to the substructure the sensor node is assigned to. The parameters of each sub-model are estimated by extracting local mode shapes and by applying the equations of the Craig-Bampton method on dynamic substructuring. The proposed methodology is validated in a laboratory test conducted on a four-story frame structure to demonstrate the ability of the methodology to yield accurate estimates of stiffness parameters. Finally, the test results are discussed and an outlook on future research directions is provided.
ROCK ENINGEERING SYSTEM BASED IDENTIFICATION MODEL FOR POTENTIAL LANDSLIDE%基于RE S理论的潜在滑坡识别
Institute of Scientific and Technical Information of China (English)
陈筠; 郭果
2014-01-01
The stability of a potential landslide involves the interaction and inter-coupling of multi-factors.So it is very difficult to evaluate the interaction relationship among the influencing factors quantitatively and determine weight considering multi-factors.Therefore according to other scholars’ achievement and the principle of rock engineering system theory’s matrix construction and decoding,a new potential landslide identification model is suggested based on improved BP network.Then,the arithmetic flow of coding interaction matrix and considering multi-factors determine weight are deduced.At last,the model is applied to identify potential soil landslides.The research results show that the parameters analysis of artificial neural network can be used not only to potential landslide identification but also to determine weight considering multi-factors.%潜在滑坡的识别涉及到多种因素间的相互作用和相互耦合，常规方法难以准确描述影响因素间相互耦合作用对斜坡稳定性的影响。结合前人的研究成果，引入岩石工程系统（RES）理论的交互作用矩阵构造和编码原理，提出了基于BP网络潜在滑坡识别模型，推导了在该识别模型下实现交互作用矩阵的编码及考虑多因素相互作用权重确定的流程，并将其运用于潜在土质滑坡判别中。研究结果表明：运用此识别模型，不仅能够实现对潜在滑坡识别，同时能够实现基于多因素交互作用影响的各识别指标权重的确定。
供应链风险的系统识别与评价模型研究%Study on Supply Chain Risk System Identification and Evaluation Model
Institute of Scientific and Technical Information of China (English)
耿殿明; 刘佳翔
2011-01-01
In this paper, we systematically identify and classify supply chain risks, as well as its controllability and harmfnl influence. A supply chain risk evaluation index system is formulated for the identification and classification of supply chain risks, and then through improved set-valued statistical accelerated iteration method, the weight of each index is determined. On the basis of expert risk probability interval estimation, a comprehensive evaluation model for supply chain risk is constructed and an empirical analysis is carried out at the last.%对供应链风险进行了系统识别和分类,分析了供应链风险的可控性和危害性.构建了供应链风险评估指标体系,利用改进的集值统计加速迭代法确定各指标权重,建立了基于专家风险概率区间估计的供应链风险综合评价模型,并进行了实证分析.
Abo-Elmagd, M; Sadek, A M
2014-12-01
Can and Bare method is a widely used passive method for measuring the equilibrium factor F through the determination of the track density ratio between bare (D) and filtered (Do) detectors. The dimensions of the used diffusion chamber are altering the deposition ratios of Po-isotopes on the chamber walls as well as the ratios of the existing alpha emitters in air. Then the measured filtered track density and therefore the resultant equilibrium factor is changed according to the diffusion chamber dimensions. For this reason, high uncertainty was expected in the measured F using different diffusion chambers. In the present work, F is derived as a function of both track density ratio (D/Do) and the dimensions of the used diffusion chambers (its volume to the total internal surface area; V/A). The accuracy of the derived formula was verified using the black-box modeling technique via the MATLAB System identification toolbox. The results show that the uncertainty of the calculated F by using the derived formula of F (D/Do, V/A) is only 5%. The obtained uncertainty ensures the quality of the derived function to calculate F using diffusion chambers with wide range of dimensions.
Directory of Open Access Journals (Sweden)
Soojin Cho
2015-04-01
Full Text Available Wireless sensor networks (WSNs facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI technique is selected for system identification, and SSI-based decentralized system identification (SDSI is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model.
Real-time Algorithms for Sparse Neuronal System Identification.
Sheikhattar, Alireza; Babadi, Behtash
2016-08-01
We consider the problem of sparse adaptive neuronal system identification, where the goal is to estimate the sparse time-varying neuronal model parameters in an online fashion from neural spiking observations. We develop two adaptive filters based on greedy estimation techniques and regularized log-likelihood maximization. We apply the proposed algorithms to simulated spiking data as well as experimentally recorded data from the ferret's primary auditory cortex during performance of auditory tasks. Our results reveal significant performance gains achieved by the proposed algorithms in terms of sparse identification and trackability, compared to existing algorithms.
Architecture control and model identification of a Omni-Directional Mobile Robot
António Paulo Gomes Mendes Moreira; Paulo José Cerqueira Gomes da Costa; André Gustavo Scolari Conceição
2005-01-01
This paper presents a architecture control and model identification of a onmi-Directional Mobile Robot It is divided into the three stages. Stage one proposes a procedure for dynamic model identification and control of the "motor + reduction + encoder" process of the Robotapos;s Motors. Second, proposes the identification of a dynamic model for the whole mobile robot considering it as a multi-variable system. Third, presents a algorithm for perfect trajectory tracking of Omni-Directional Mobi...
Energy Technology Data Exchange (ETDEWEB)
Hansen, E.J.; Frisch, C.F.; McDade, R.L. Jr.; Johnston, K.H.
1981-06-01
Outer membrane proteins of Haemophilus influenzae type b which are immunogenic in infant rats were identified by a radioimmunoprecipitation method. Intact cells of H. influenzae type b were radioiodinated by a lactoperoxidase-catalyzed procedure, and an outer membrane-containing fraction was prepared from these cells. These radioiodinated outer membranes were mixed with sera obtained from rats convalescing from systemic H. influenzae type b disease induced at 6 days of age, and the resultant (antibody-outer membrane protein antigen) complexes were extracted from these membranes by treatment with nonionic detergent and ethylenediaminetetraacetic acid. These soluble antibody-antigen complexes were isolated by means of adsorption to protein A-bearing staphylococci, and the radioiodinated protein antigens were identified by gel electrophoresis followed by autoradiography. Infant rats were shown to mount a readily detectable antibody response to several different proteins present in the outer membrane of H. influenzae type b. Individual infant rats were found to vary both qualitatively and quantitatively in their immune response to these immunogenic outer membrane proteins.
Nonlinear vibrating system identification via Hilbert decomposition
Feldman, Michael; Braun, Simon
2017-02-01
This paper deals with the identification of nonlinear vibration systems, based on measured signals for free and forced vibration regimes. Two categories of time domain signal are analyzed, one of a fast inter-modulation signal and a second as composed of several mono-components. To some extent, this attempts to imitate analytic studies of such systems, with its two major analysis groups - the perturbation and the harmonic balance methods. Two appropriate signal processing methods are then investigated, one based on demodulation and the other on signal decomposition. The Hilbert Transform (HT) has been shown to enable effective and simple methods of analysis. We show that precise identification of the nonlinear parameters can be obtained, contrary to other average HT based methods where only approximation parameters are obtained. The effectiveness of the proposed methods is demonstrated for the precise nonlinear system identification, using both the signal demodulation and the signal decomposition methods. Following the exposition of the tools used, both the signal demodulation as well as decomposition are applied to classical examples of nonlinear systems. Cases of nonlinear stiffness and damping forces are analyzed. These include, among other, an asymmetric Helmholtz oscillator, a backlash with nonlinear turbulent square friction, and a Duffing oscillator with dry friction.
Nonlinear System Identification for Aeroelastic Systems with Application to Experimental Data
Kukreja, Sunil L.
2008-01-01
Representation and identification of a nonlinear aeroelastic pitch-plunge system as a model of the Nonlinear AutoRegressive, Moving Average eXogenous (NARMAX) class is considered. A nonlinear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properties demonstrated via continuous-time simulations of experimental conditions. Simulation results show that (1) the outputs of the NARMAX model closely match those generated using continuous-time methods, and (2) NARMAX identification methods applied to aeroelastic dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental pitch-plunge dynamics data gives a high percent fit for cross-validated data.
Non-Linear System Identification for Aeroelastic Systems with Application to Experimental Data
Kukreja, Sunil L.
2008-01-01
Representation and identification of a non-linear aeroelastic pitch-plunge system as a model of the NARMAX class is considered. A non-linear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properties demonstrated via continuous-time simulations of experimental conditions. Simulation results show that (i) the outputs of the NARMAX model match closely those generated using continuous-time methods and (ii) NARMAX identification methods applied to aeroelastic dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental pitch-plunge dynamics data gives a high percent fit for cross-validated data.
Continuous-Time System Identification of a Smoking Cessation Intervention.
Timms, Kevin P; Rivera, Daniel E; Collins, Linda M; Piper, Megan E
2014-01-01
Cigarette smoking is a major global public health issue and the leading cause of preventable death in the United States. Toward a goal of designing better smoking cessation treatments, system identification techniques are applied to intervention data to describe smoking cessation as a process of behavior change. System identification problems that draw from two modeling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems. A continuous-time dynamic modeling approach is employed to describe the response of craving and smoking rates during a quit attempt, as captured in data from a smoking cessation clinical trial. The use of continuous-time models provide benefits of parsimony, ease of interpretation, and the opportunity to work with uneven or missing data.
Continuous-time system identification of a smoking cessation intervention
Timms, Kevin P.; Rivera, Daniel E.; Collins, Linda M.; Piper, Megan E.
2014-07-01
Cigarette smoking is a major global public health issue and the leading cause of preventable death in the United States. Toward a goal of designing better smoking cessation treatments, system identification techniques are applied to intervention data to describe smoking cessation as a process of behaviour change. System identification problems that draw from two modelling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems. A continuous-time dynamic modelling approach is employed to describe the response of craving and smoking rates during a quit attempt, as captured in data from a smoking cessation clinical trial. The use of continuous-time models provide benefits of parsimony, ease of interpretation, and the opportunity to work with uneven or missing data.
Spatiotemporal System Identification With Continuous Spatial Maps and Sparse Estimation.
Aram, Parham; Kadirkamanathan, Visakan; Anderson, Sean R
2015-11-01
We present a framework for the identification of spatiotemporal linear dynamical systems. We use a state-space model representation that has the following attributes: 1) the number of spatial observation locations are decoupled from the model order; 2) the model allows for spatial heterogeneity; 3) the model representation is continuous over space; and 4) the model parameters can be identified in a simple and sparse estimation procedure. The model identification procedure we propose has four steps: 1) decomposition of the continuous spatial field using a finite set of basis functions where spatial frequency analysis is used to determine basis function width and spacing, such that the main spatial frequency contents of the underlying field can be captured; 2) initialization of states in closed form; 3) initialization of state-transition and input matrix model parameters using sparse regression-the least absolute shrinkage and selection operator method; and 4) joint state and parameter estimation using an iterative Kalman-filter/sparse-regression algorithm. To investigate the performance of the proposed algorithm we use data generated by the Kuramoto model of spatiotemporal cortical dynamics. The identification algorithm performs successfully, predicting the spatiotemporal field with high accuracy, whilst the sparse regression leads to a compact model.
ADAPTIVE CONTROL AND IDENTIFICATION OF CHAOTIC SYSTEMS
Institute of Scientific and Technical Information of China (English)
LI ZHI; HAN CHONG-ZHAO
2001-01-01
A novel adaptive control and identification on-line method is proposed for a class of chaotic system with uncertain parameters. We prove that, using the presented method, a controller and identifier is developed which can remove chaos in nonlinear systems and make the system asymptotically stabilizing to an arbitrarily desired smooth orbit. And at the same time, estimates to uncertain parameters converge to their true values. The advantage of our method over the existing result is that the controller and identifier is directly constructed by analytic formula without knowing unknown bounds about uncertain parameters in advance. A computer simulation example is given to validate the proposed approach.
Unique device identification system. Final rule.
2013-09-24
The Food and Drug Administration (FDA) is issuing a final rule to establish a system to adequately identify devices through distribution and use. This rule requires the label of medical devices to include a unique device identifier (UDI), except where the rule provides for an exception or alternative placement. The labeler must submit product information concerning devices to FDA's Global Unique Device Identification Database (GUDID), unless subject to an exception or alternative. The system established by this rule requires the label and device package of each medical device to include a UDI and requires that each UDI be provided in a plain-text version and in a form that uses automatic identification and data capture (AIDC) technology. The UDI will be required to be directly marked on the device itself if the device is intended to be used more than once and intended to be reprocessed before each use.
Identification of integrating and critically damped systems with time delay
Institute of Scientific and Technical Information of China (English)
BAJARANGBALI; Somanath MAJHI
2015-01-01
This paper presents identification of second order plus dead time (SOPDT) integrating and critically damped systems based on relay feedback testing. Relay with hysteresis is applied to the unknown system to get the sustained oscillations also called as limit cycle. The limit cycle parameters are utilized in mathematical expressions which are derived using state space technique so that exact process model parameters are estimated. As the relay with hysteresis helps in generating sustained oscillations and also reduces effect of measurement noise which is an important issue in system identification. Different types of processes in the form of transfer function models are considered to show the efficacy of the proposed method and results are compared with available methods in the literature with and without noise effect.
Experimental Modeling of Dynamic Systems
DEFF Research Database (Denmark)
Knudsen, Morten Haack
2006-01-01
An engineering course, Simulation and Experimental Modeling, has been developed that is based on a method for direct estimation of physical parameters in dynamic systems. Compared with classical system identification, the method appears to be easier to understand, apply, and combine with physical...
IDENTIFICATION FOR WIENER SYSTEMS WITH INTERNAL NOISE
Institute of Scientific and Technical Information of China (English)
Qijiang SONG; Hanfu CHEN
2008-01-01
This paper considers identification of Wiener systems for which the internal variables and output are corrupted by noises. When the internal noise is a sequence of independent and identically distributed (iid) Gaussian random variables, by the Weierstrass transformation (WT) the system under consideration turns to be a Wiener system without internal noise. The nonlinear part of the latter is nothing else than the WT of the nonlinear function of the original system, while the linear subsystem is the same for both systems before and after WT. Under reasonable conditions, the recursive identification algorithms are proposed for the transformed Wiener system, and strong consistency for the estimates is established. By using the inverse WT the nonparametric estimates for the nonlinearity of the original system are derived, and they are strongly consistent if the nonlinearity in the original system is a polynomial. Similar results also hold in the case where the internal noise is non-Gaussian. Simulation results are fully consistent with the theoretical analysis.
Online Palmprint Identification System for Civil Applications
Institute of Scientific and Technical Information of China (English)
David Zhang; Guang-Ming Lu; Adams Wai-Kin Kong; Michael Wong
2005-01-01
In this paper, a novel biontetric identification system is presented to identify a person's identity by his/her palmprint. In contrast to existing palmprint systems for criminal applications, the proposed system targets at the civil applications, which require identifying a person in a large database with high accuracy in real-time. The system is constituted by four major components: User Interface Module, Acquisition Module, Recognition Module and External Module. More than 7,000 palmprint images have been collected to test the performance of the system. The system can identify 400 palms with a low false acceptance rate, 0.02%, and a high genuine acceptance rate, 98.83%. For verification, the system can operate at a false acceptance rate, 0.017% and a false rejection rate, 0.86%. The execution time for the whole process including image collection, preprocessing, feature extraction and matching is less than 1 second.
Identification of dynamic systems, theory and formulation
Maine, R. E.; Iliff, K. W.
1985-01-01
The problem of estimating parameters of dynamic systems is addressed in order to present the theoretical basis of system identification and parameter estimation in a manner that is complete and rigorous, yet understandable with minimal prerequisites. Maximum likelihood and related estimators are highlighted. The approach used requires familiarity with calculus, linear algebra, and probability, but does not require knowledge of stochastic processes or functional analysis. The treatment emphasizes unification of the various areas in estimation in dynamic systems is treated as a direct outgrowth of the static system theory. Topics covered include basic concepts and definitions; numerical optimization methods; probability; statistical estimators; estimation in static systems; stochastic processes; state estimation in dynamic systems; output error, filter error, and equation error methods of parameter estimation in dynamic systems, and the accuracy of the estimates.
Identification du modele mathematique d'un helicoptere reduit
Honvo, Japhet
The remote-controlled helicopter remains an interesting topic for research in flight control. This kind of machine, easy to deploy due to their small size, is an ideal candidate to test multiple flight control algorithms. To better understand the dynamics of flight of this vehicle, it is important to have a mathematical model. This thesis follows the logic of obtaining a mathematical model for a stationary hovering helicopter. This thesis aims to provide a testbench for the identification of a mathematical model of a small helicopter and for the application of different flight control laws. First, a review on the identification theory is introduced. The methods presented are applicable to multivariable systems. A particular focus is on the identification of state models. The theory concludes with the presentation of algorithms used in the Matlab/Simulink software. Second, a mathematical model of the helicopter is developed. As part of our research, hypotheses to reduce the model are presented. This model is the basis for determining the right identification methods. The mathematical model provides a guideline for specifying the various components of the test bench. The thesis continues with the presentation of the avionics used in the project. The instrumentation is presented in two parts: the hardware and the software. The acquisition of real-time flight parameters is also presented. Finally, the use of the test bench is detailed for the ground tests and for the flight tests. These tests are designed to collect the data necessary for the deployment of various identification techniques. The thesis concludes with comments on significants results and suggestions of prospects for improving the test bench.
Using Pareto points for model identification in predictive toxicology.
Palczewska, Anna; Neagu, Daniel; Ridley, Mick
2013-03-22
: Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology.
Yu, Minli; Hahn, Eric J.; Liu, Jike; Lu, Zhongrong
2016-11-01
This paper introduced a modal parameter based identification procedure to identify the equivalent system of structures under harmonic excitations. The developed identification technique assumed non-proportional hysteretic damping in the equivalent system, which would be applicable in identifying more general structures. By introducing quasi-modal parameter, modal analysis equation was decoupled under physical coordinate; hence, the modal parameters of each vibration mode are identified independently. Double iteration algorithm was developed to solve the derived non-linear identification equation with complex unknowns. The developed identification procedure was applied to identify the equivalent system of a numerical model in order to evaluate the feasibility of the technique in practice. The identification procedure was also applied to identify an experimental mass and bar rig for validation purpose. Identification results showed that the identification procedure could identify accurately and robustly the equivalent system with non-proportional hysteretic damping assumption; hence, it is likely to be applicable in the field.
Hekler, Eric B.; Buman, Matthew P.; Poothakandiyil, Nikhil; Rivera, Daniel E.; Dzierzewski, Joseph M.; Aiken Morgan, Adrienne; McCrae, Christina S.; Roberts, Beverly L.; Marsiske, Michael; Giacobbi, Peter R., Jr.
2013-01-01
Efficacious interventions to promote long-term maintenance of physical activity are not well understood. Engineers have developed methods to create dynamical system models for modeling idiographic (i.e., within-person) relationships within systems. In behavioral research, dynamical systems modeling may assist in decomposing intervention effects…
Parameter Identification of Weakly Nonlinear Vibration System in Frequency Domain
Directory of Open Access Journals (Sweden)
Jiehua Peng
2004-01-01
Full Text Available A new method of identifying parameters of nonlinearly vibrating system in frequency domain is presented in this paper. The problems of parameter identification of the nonlinear dynamic system with nonlinear elastic force or nonlinear damping force are discussed. In the method, the mathematic model of parameter identification is frequency response function. Firstly, by means of perturbation method the frequency response function of weakly nonlinear vibration system is derived. Next, a parameter transformation is made and the frequency response function becomes a linear function of the new parameters. Then, based on this function and with the least square method, physical parameters of the system are identified. Finally, the applicability of the proposed technique is confirmed by numerical simulation.
Realization-Based System Identification with Applications
Miller, Daniel N.
The identification of dynamic system behavior from experimentally measured or computationally simulated data is fundamental to the fields of control system design, modal analysis, and defect detection. In this dissertation, methods for system identification are developed based on classical linear system realization theory. The common methods of state-space realization from a measured, discrete-time impulse response are generalized to the following additional types of experiments: measured step responses, arbitrary sets of input-output data, and estimated cross-covariance functions of input-output data. The methods are particularly well suited to systems with large input and/or output dimension, for which classical system identification methods based on maximum likelihood estimation may fail due to their reliance on non-convex optimizations. The realization-based methods by themselves require a finite number of linear algebraic operations. Because these methods implicitly optimize cost functions that are linear in state-space parameters, they may be augmented with convex constraints to form convex optimization problems. Several common behavioral constraints are translated into eigenvalue constraints stated as linear matrix inequalities, and the realization-based methods are converted into semidefinite programming problems. Some additional constraints on transient and steady-state behavior are derived and incorporated into a quadratic program, which is solved following the semidefinite program. The newly developed realization-based methods are applied to two experiments: the aeroelastic response of a fighter aircraft and the transient thermal behavior of a light-emitting diode. The algorithms for each experiment are implemented in two freely available software packages.
Institute of Scientific and Technical Information of China (English)
韩中; 赵升吨; 张贵成; 阮卫平; 李建平; 沈红立
2014-01-01
模型能够解决系统工程中的许多问题，为此，提出了一种新的流程制造系统辨识的自动结构性建模方法。通过对系统的结构组成和单元关系进行辨识，提炼出模型的结构性数据，并以此自动地形成系统仿真模型。建模采用了图论作为工业系统的数学表达形式。研究对系统单元进行规则性编码，并根据系统结构所具有的特性定义了建模的辨识函数。实例证明了提出的方法是可行的，并能够满足系统建模的有用性、高效性、准确性的要求。%Models can solve many problems in systems engineering, so a new automatic structural modeling technology based on process manufacturing system identifications is presented. Through carrying out identifications to system compo-sitions and unit relationships, model structure data is refined, and a system simulation model is automatically generated. The graph theory is adapted and regarded as a math expression format of the industrial system in the modeling. In addi-tion, the rule codes are implemented for system units in the research, the identification functions are defined according to system composition properties. The auto-modeling processes are achieved through iterative computation. Finally, the example is given to verify that the presented method is feasible, and can satisfy these requirements of the availability, efficiency and accuracy.
微波加热过程中的一种系统辨识建模方法%A modeling method based on system identification in microwave heating process
Institute of Scientific and Technical Information of China (English)
曾诚; 袁宇鹏; 王智慧
2014-01-01
微波加热技术广泛的应用于生物、化学等各个工业领域。本文首先阐述了基于最小二乘法的系统辨识方法理论；其次针对微波感应加热过程数据，利用Matlab系统辨识工具箱进行离线数据系统辨识，建立了微波感应加热系统的一阶滞后和二阶滞后系统模型，通过仿真对比，二阶滞后模型同原始数据的相关性优于一阶滞后系统，为微波加热过程温度控制方法的设计奠定了基础。%Microwave heating technology is widely used in many industrial applications,such as biopharmaceutical,organic synthesis and so on. Firstly,the article presents the theory of the system identification based on the least squares estimate method. Then,a set of data of the microwave heating process is engaged in the Matlab System Identification Tool to get the estimate model,two models are achieved that are the 1-order system model with delay and the 2-order system model with delay. The simulations show that the 2-order model is better than the 1-order model in terms of the correlation between the estimated model and the source data. This modelling method based on system identification will contribute in the microwave heating temperature control field.
Subspace System Identification of the Kalman Filter
Directory of Open Access Journals (Sweden)
David Di Ruscio
2003-07-01
Full Text Available Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati equation. Furthermore, it is in general and for colored inputs, proved that the subspace identification of the states only is possible if the deterministic part of the system is known or identified beforehand. However, if the inputs are white, then, it is proved that the states can be identified directly. Some alternative projection matrices which can be used to compute the extended observability matrix directly from the data are presented. Furthermore, an efficient method for computing the deterministic part of the system is presented. The closed loop subspace identification problem is also addressed and it is shown that this problem is solved and unbiased estimates are obtained by simply including a filter in the feedback. Furthermore, an algorithm for consistent closed loop subspace estimation is presented. This algorithm is using the controller parameters in order to overcome the bias problem.
Identification of Hammerstein Model Based on Quantum Genetic Algorithm
Zhang Hai Li
2013-01-01
Nonlinear system identification is a main topic of modern identification. A new method for nonlinear system identification is presented by using Quantum Genetic Algorithm(QGA).The problems of nonlinear system identification are cast as function optimization overprameter space，and the Quantum Genetic Algorithm is adopted to solve the optimization problem. Simulation experiments show that: compared with the genetic algorithm, quantum genetic algorithm is an effective swarm intelligence algorith...
On closed loop transient response system identification
Directory of Open Access Journals (Sweden)
Christer Dalen
2016-10-01
Full Text Available Some methods for transient closed loop step response system identification presented in the literature are reviewed. Interestingly some errors in a method published in the early 80's where propagated into a recently published method. These methods are reviewed and some improved methods are suggested and presented. The methods are compared against each other on some closed loop system examples, e.g. a well pipeline-riser severe-slugging flow regime example, using Monte Carlo simulations for comparison of the methods.
Decentralized system identification for electric power system; Denryoku keito no bunsan system dotei
Energy Technology Data Exchange (ETDEWEB)
Kawamoto, S.; Kanetaka, I. [University of Osaka Prefecture, Osaka (Japan)
1995-12-20
The research on decentralized control of electric power system, which has become more complex, is an important subject for tile stabilizing control. In particular since electric power system is a large scale nonlinear control ones, decentralized (not divided) systems with cooperation should be constructed. The purpose of this paper is to present an approach for constructing decentralized systems of electric ponder system. In Chapter 2, swing datas of a three-machine model system are obtained, and in Chapter 3, coefficient parameters of tile model equation based on one-machine infinite bus system with AVR and GOV are estimated by tile least square method. In Chapter 4, the equivalence calculated by using tile estimated values is discussed and also the effect of conditions for the fault is considered. Finally Chapter 5 is devoted to summarizing tile result for the decentralized system identification. 14 refs., 5 figs., 2 tabs.
Ling-Yuan Hsu; Tsung-Lin Chen
2012-01-01
This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficie...
Time domain system identification of unknown initial conditions
Institute of Scientific and Technical Information of China (English)
SUNGWen-pei; MATZENVernonC.; SHIHMing-hsiang
2004-01-01
System identification is a method for using measured data to create or improve a mathematical model of the object being tested. From the measured data however, noise is noticed at the beginning of the response. One solution to avoid this noise problem is to skip the noisy data and then use the initial conditions as active parameters, to be found by using the system identification process. This paper describes the development of the equations for setting up the initial conditions as active parameters. The simulated data and response data from actual shear buildings were used to prove the accuracy of both the algorithm and the computer program, which include the initial conditions as active parameters. The numerical and experimental model analysis showed that the value of mass, stiffness and frequency were very reasonable and that the computed acceleration and measured acceleration matched very well.
INFORMATION CHARACTERIZATION OF COMMUNICATION CHANNELS FOR SYSTEM IDENTIFICATION
Institute of Scientific and Technical Information of China (English)
Le Yi WANG; G. George YIN
2007-01-01
This paper studies identification of systems in which the system output is quantized,transmitted through a digital communication channel, and observed afterwards. The concept of the CR Ratio is introduced to characterize impact of communication channels on identification. The relationship between the CR Ratio and Shannon channel capacity is discussed. Identification algorithms are further developed when the channel error probability is unknown.
A computerised system for the identification of lactic acid bacteria.
Wijtzes, T.; Bruggeman, M.R.; Nout, M.J.R.; Zwietering, M.H.
1997-01-01
A generic computerised system for the identification of bacteria was developed. The system is equipped with a key to the identification of lactic acid bacteria. The identification is carried out in two steps. The first step distinguishes groups of bacteria by following a decision tree with general i
Persistent excitation in adaptive parameter identification of uncertain chaotic system
Institute of Scientific and Technical Information of China (English)
Zhao Jun-chan; Zhang Qun-Jiao; Lu Jun-An
2011-01-01
This paper studies the parameter identification problem of chaotic systems. Adaptive identification laws are proposed to estimate the parameters of uncertain chaotic systems. It proves that the asymptotical identification is ensured by a persistently exciting condition. Additionally, the method can be applied to identify the uncertain parameters with any number. Numerical simulations are given to validate the theoretical analysis.
Output-Only Identification of System Parameters from Noisy Measurements by Multiwavelet Denoising
2014-01-01
In this paper we estimate the parameters of a multidimensional system from a record of noisy output measurements by using a multiwavelet denoising technique. In this output-only identification scheme, we extend wavelet denoising methods to the multiwavelet case. After the noise has been removed from the output records by wavelet methods, either full model identification or deterministic subspace identification can be performed. In the former case, full information on the system such as modal ...
1989-10-30
In this Phase I SBIR study, new methods are developed for the system identification and stochastic filtering of nonlinear controlled Markov processes...state space Markov process models and canonical variate analysis (CVA) for obtaining optimal nonlinear procedures for system identification and stochastic
Joint Dynamics Modeling and Parameter Identification for Space Robot Applications
Directory of Open Access Journals (Sweden)
Adenilson R. da Silva
2007-01-01
Full Text Available Long-term mission identification and model validation for in-flight manipulator control system in almost zero gravity with hostile space environment are extremely important for robotic applications. In this paper, a robot joint mathematical model is developed where several nonlinearities have been taken into account. In order to identify all the required system parameters, an integrated identification strategy is derived. This strategy makes use of a robust version of least-squares procedure (LS for getting the initial conditions and a general nonlinear optimization method (MCS—multilevel coordinate search—algorithm to estimate the nonlinear parameters. The approach is applied to the intelligent robot joint (IRJ experiment that was developed at DLR for utilization opportunity on the International Space Station (ISS. The results using real and simulated measurements have shown that the developed algorithm and strategy have remarkable features in identifying all the parameters with good accuracy.
System Identification using Measurements Subject to Stochastic Time Jitter
2004-01-01
When the sensors readings are perturbed by an unknown stochastic time jitter, classical system identification algorithms based on additive amplitude perturbations will give biased estimates. We here outline the maximum likelihood procedure, for the case of both time and amplitude noise, in the frequency domain, based on the measurement DFT. The method directly applies to output error continuous time models, while a simple sinusoid in noise example is used to illustrate the bias removal of the...
Support Vector Machine for Behavior-Based Driver Identification System
Huihuan Qian; Yongsheng Ou; Xinyu Wu; Xiaoning Meng; Yangsheng Xu
2010-01-01
We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security applicat...
Identification of Civil Engineering Structures using Multivariate ARMAV and RARMAV Models
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune
This paper presents how to make system identification of civil engineering structures using multivariate auto-regressive moving-average vector (ARMAV) models. Further, the ARMAV technique is extended to a recursive technique (RARMAV). The ARMAV model is used to identify measured stationary data....... The results show the usefulness of the approaches for identification of civil engineering structures excited by natural excitation...
Comparison of System Identification Methods using Ambient Bridge Test Data
DEFF Research Database (Denmark)
Andersen, P.; Peeters, B.; Hermans, L.
In this paper the performance of four different system identification methods is compared using operational data obtained from an ambient vibration test of the Swiss Z24 highway bridge. The four methods are the frequency domain based peak-picking methods, the polyreference LSCE method, the stocha......In this paper the performance of four different system identification methods is compared using operational data obtained from an ambient vibration test of the Swiss Z24 highway bridge. The four methods are the frequency domain based peak-picking methods, the polyreference LSCE method......, the stochastic subspace method for estimation of state space systems and the prediction error method for estimation of Auto-Regressive Moving Average Vector models. It is not the entention to elect a winner among the four methods, but more to emphasize the different advantages of each of the methods....
Asymptotic inference in system identification for the atom maser.
Catana, Catalin; van Horssen, Merlijn; Guta, Madalin
2012-11-28
System identification is closely related to control theory and plays an increasing role in quantum engineering. In the quantum set-up, system identification is usually equated to process tomography, i.e. estimating a channel by probing it repeatedly with different input states. However, for quantum dynamical systems such as quantum Markov processes, it is more natural to consider the estimation based on continuous measurements of the output, with a given input that may be stationary. We address this problem using asymptotic statistics tools, for the specific example of estimating the Rabi frequency of an atom maser. We compute the Fisher information of different measurement processes as well as the quantum Fisher information of the atom maser, and establish the local asymptotic normality of these statistical models. The statistical notions can be expressed in terms of spectral properties of certain deformed Markov generators, and the connection to large deviations is briefly discussed.
Asymptotic inference in system identification for the atom maser
Catana, Catalin; Guta, Madalin
2011-01-01
System identification is an integrant part of control theory and plays an increasing role in quantum engineering. In the quantum set-up, system identification is usually equated to process tomography, i.e. estimating a channel by probing it repeatedly with different input states. However for quantum dynamical systems like quantum Markov processes, it is more natural to consider the estimation based on continuous measurements of the output, with a given input which may be stationary. We address this problem using asymptotic statistics tools, for the specific example of estimating the Rabi frequency of an atom maser. We compute the Fisher information of different measurement processes as well as the quantum Fisher information of the atom maser, and establish the local asymptotic normality of these statistical models. The statistical notions can be expressed in terms of spectral properties of certain deformed Markov generators and the connection to large deviations is briefly discussed.
Nonlinear system identification and control using state transition algorithm
Yang, Chunhua; Gui, Weihua
2012-01-01
This paper presents a novel optimization method named state transition algorithm (STA) to solve the problem of identification and control for nonlinear system. In the proposed algorithm, a solution to optimization problem is considered as a state, and the updating of a solution equates to the process of state transition, which makes the STA easy to understand and convenient to be implemented. First, the STA is applied to identify the optimal parameters of the estimated system with previously known structure. With the accurate estimated model, an off-line PID controller is then designed optimally by using the STA as well. Experimental results demonstrate the validity of the methodology, and comparison to STA with other optimization algorithms confirms that STA is a promising alternative method for system identification and control due to its stronger search ability, faster convergence speed and more stable performance.
Linear System Identification via Atomic Norm Regularization
Shah, Parikshit; Tang, Gongguo; Recht, Benjamin
2012-01-01
This paper proposes a new algorithm for linear system identification from noisy measurements. The proposed algorithm balances a data fidelity term with a norm induced by the set of single pole filters. We pose a convex optimization problem that approximately solves the atomic norm minimization problem and identifies the unknown system from noisy linear measurements. This problem can be solved efficiently with standard, freely available software. We provide rigorous statistical guarantees that explicitly bound the estimation error (in the H_2-norm) in terms of the stability radius, the Hankel singular values of the true system and the number of measurements. These results in turn yield complexity bounds and asymptotic consistency. We provide numerical experiments demonstrating the efficacy of our method for estimating linear systems from a variety of linear measurements.
Evaluation of fungichrom 1: A new yeast identification system
Umabala P; Satheeshkumar T; Lakshmi V
2002-01-01
Advances in anti-fungal therapy necessitate the need for accurate identification of fungi especially yeasts to their species level for more effective management. Unlike the time consuming conventional methods of yeast identification using fermentation and assimilation patterns of various carbohydrates, the new commercialized yeast identification systems are simpler, rapid and are particularly easy to interpret. In our study, a new colorimetric yeast identification system-Fungichrom 1(Internat...
Continuous-time model identification and state estimation using non-uniformly sampled data
2009-01-01
This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input-output sequences. The algorithms developed are autoregressive methods, methods of subspace-based model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output model and a stochastic innovations model. Using the Riccati equation, we have designed a procedure to ...
Application of a new method of nonlinear dynamical system identification to biochemical problems.
Karnaukhov, A V; Karnaukhova, E V
2003-03-01
The system identification method for a variety of nonlinear dynamic models is elaborated. The problem of identification of an original nonlinear model presented as a system of ordinary differential equations in the Cauchy explicit form with a polynomial right part reduces to the solution of the system of linear equations for the constants of the dynamical model. In other words, to construct an integral model of the complex system (phenomenon), it is enough to collect some data array characterizing the time-course of dynamical parameters of the system. Collection of such a data array has always been a problem. However difficulties emerging are, as a rule, not principal and may be overcome almost without exception. The potentialities of the method under discussion are demonstrated by the example of the test problem of multiparametric nonlinear oscillator identification. The identification method proposed may be applied to the study of different biological systems and in particular the enzyme kinetics of complex biochemical reactions.
A secure identification system using coherent states
Institute of Scientific and Technical Information of China (English)
He Guang-Qiang; Zeng Gui-Hua
2006-01-01
A quantum identification system based on the transformation of polarization of a mesoscopic coherent state is proposed. Physically, an initial polarization state which carries the identity information is transformed into an arbitrary elliptical polarization state. To verify the identity of a communicator, a reverse procedure is performed by the receiver. For simply describing the transformation procedure, the analytical methods of Poincare sphere and quaternion are adopted. Since quantum noise provides such a measurement uncertainty for the eavesdropping that the identity information cannot be retrieved from the elliptical polarization state, the proposed scheme is secure.
BLIND IDENTIFICATION OF A CLASS OF NONLINEAR SYSTEMS WITH CYCLOSTATIONARY INPUT
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
This letter deals with blind identification of nonlinear discrete Hammerstein system under the input signal that is cyclostationary.The first-order moment of the specific input as well as the inverse nonlinear mapping of the Hammerstein model are combined to establish a relationship between the system output and the system parameters,which implies an approach to identifying the system blindly.Simulation results demonstrate the effectiveness of this approach to blind identification of a class of nonUnear systems.
A Reliable Identification System for Red Palm Weevil
Directory of Open Access Journals (Sweden)
Saleh Mufleh Al-Saqer
2012-01-01
Full Text Available Problem statement: Red Palm Weevil (RPW is a widely found pest among palm trees and is known to cause significant losses every year to palm growers. Existing identification techniques for RPW comprise of using traps with pheromones to detect these pests. However, these traditional methods are labor-intensive, expensive to implement and unreliable for early detection of RPW infestation. Early detection of these pests would provide the best opportunity to eradicate them and minimize the potential losses of palm trees. Approach: In this study, a reliable identification system is developed to identify RPW by using only a small number of image descriptors in combination with neural network models. The neural networks were developed by using between three to nine image descriptors as inputs and a large database of insectsâ images was used for training. Three different training ratios ranging from 25-75% were used and the network was trained by two different algorithms. Further, several scenarios were formulated to test the efficacy and reliability of the newly developed identification system. Results: The results indicate that the identification system developed in this study is capable of 100% recognition of RPW and 93% recognition of other insects in the database by taking as input only three easily-calculable image descriptors. Further, the average training times for these networks was 13 sec and the testing time for a single image was only 0.015 sec. Conclusion: The new system developed in this study provided reliable identification for RPW and was found to be up to 14 times faster in training and three times faster in testing of insectsâ images.
Modelling on fuzzy control systems
Institute of Scientific and Technical Information of China (English)
LI; Hongxing(李洪兴); WANG; Jiayin(王加银); MIAO; Zhihong(苗志宏)
2002-01-01
A kind of modelling method for fuzzy control systems is first proposed here, which is calledmodelling method based on fuzzy inference (MMFI). It should be regarded as the third modelling method thatis different from two well-known modelling methods, that is, the first modelling method, mechanism modellingmethod (MMM), and the second modelling method, system identification modelling method (SlMM). Thismethod can, based on the interpolation mechanism on fuzzy logic system, transfer a group of fuzzy inferencerules describing a practice system into a kind of nonlinear differential equation with variable coefficients, calledHX equations, so that the mathematical model of the system can be obtained. This means that we solve thedifficult problem of how to get a model represented as differential equations on a complicated or fuzzy controlsystem.
Dynamic Model Identification for Industrial Robots
Directory of Open Access Journals (Sweden)
Ngoc Dung Vuong
2009-12-01
Full Text Available In this paper, a systematic procedure for identifying the dynamics of industrialrobots is presented. Since joint friction can be highly nonlinearwith time varyingcharacteristics in the low speed region,a simple and yet effective scheme has been used toidentify the boundary velocity that separates this “dynamic” friction region from its staticregion. The robot’s dynamic model is then identified in this static region, where thenonlinnear friction model is reduced to the linear-in-parameter form. To overcome thedrawbacks of the least squares estimator, which does not take in any constraints, anonlinear optimization problem is formulated to guarantee the physical feasibility of theidentified parameters. The proposed procedure has been demonstrated on the first fourlinks of the Mitsubishi PA10 manipulator, an improved dynamic model was obtained andthe the effectiveness of the proposed identification procedure is demonstrated.
A Comparison of Evolutionary Computation Techniques for IIR Model Identification
Directory of Open Access Journals (Sweden)
Erik Cuevas
2014-01-01
Full Text Available System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR models for identification is preferred over their equivalent FIR (finite impulse response models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated.
Institute of Scientific and Technical Information of China (English)
丁锋; 陈慧波
2016-01-01
针对具有已知基的输入非线性输出误差系统，提出了基于过参数化模型的辅助模型递推辨识方法和辅助模型递阶辨识方法，提出了基于关键项分离的辅助模型递推辨识方法、基于关键项分离的辅助模型两阶段辨识方法和辅助模型三阶段辨识方法，提出了基于双线性参数模型分解的辅助模型随机梯度算法和基于双线性参数模型分解的辅助模型递推最小二乘算法，并给出了几个典型辨识算法的计算量、计算步骤。这些算法的收敛性分析都是需要研究的辨识课题。%For input nonlinear output⁃error systems with known bases,this paper presents the over⁃parameterization model based auxiliary model ( AM) recursive identification methods,the over⁃parameterization model based AM hi⁃erarchical identification methods,the key term separation based AM recursive identification methods,the key term separation based AM two⁃stage recursive identification methods,the key term separation based AM three⁃stage re⁃cursive identification methods,the bilinear⁃in⁃parameter model decomposition based AM stochastic gradient identifi⁃cation methods and the bilinear⁃in⁃parameter model decomposition based AM recursive least squares identification methods.Finally,the computational efficiency and the computational steps of several typical identification algorithms are discussed.The convergence of the proposed algorithms needs further study.
Direction Identification System of Garlic Clove Based on Machine Vision
Directory of Open Access Journals (Sweden)
Gao Chi
2013-05-01
Full Text Available In order to fulfill the requirements of seeding direction of garlic cloves, the paper proposed a research method of garlic clove direction identification based on machine vision, it expounded the theory of garlic clove direction identification, stated the arithmetic of it, designed the direction identification device of it, then developed the control system of garlic clove direction identification based on machine vision, at last tested the garlic clove direction identification, and the result of the experiment certificated that the rate of garlic clove direction identification could reach to more than 97%, and it demonstrated that the research is of high feasibility and technological values.
Systematic approach for the identification of process reference models
CSIR Research Space (South Africa)
Van Der Merwe, A
2009-02-01
Full Text Available Process models are used in different application domains to capture knowledge on the process flow. Process reference models (PRM) are used to capture reusable process models, which should simplify the identification process of process models...
SSNN toolbox for non-linear system identification
Luzar, Marcel; Czajkowski, Andrzej
2015-11-01
The aim of this paper is to develop and design a State Space Neural Network toolbox for a non-linear system identification with an artificial state-space neural networks, which can be used in a model-based robust fault diagnosis and control. Such toolbox is implemented in the MATLAB environment and it uses some of its predefined functions. It is designed in the way that any non-linear multi-input multi-output system is identified and represented in the classical state-space form. The novelty of the proposed approach is that the final result of the identification process is the state, input and output matrices, not only the neural network parameters. Moreover, the toolbox is equipped with the graphical user interface, which makes it useful for the users not familiar with the neural networks theory.
Stability Analysis of Neural Networks-Based System Identification
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Talel Korkobi
2008-01-01
Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.
Application of dynamic recurrent neural networks in nonlinear system identification
Du, Yun; Wu, Xueli; Sun, Huiqin; Zhang, Suying; Tian, Qiang
2006-11-01
An adaptive identification method of simple dynamic recurrent neural network (SRNN) for nonlinear dynamic systems is presented in this paper. This method based on the theory that by using the inner-states feed-back of dynamic network to describe the nonlinear kinetic characteristics of system can reflect the dynamic characteristics more directly, deduces the recursive prediction error (RPE) learning algorithm of SRNN, and improves the algorithm by studying topological structure on recursion layer without the weight values. The simulation results indicate that this kind of neural network can be used in real-time control, due to its less weight values, simpler learning algorithm, higher identification speed, and higher precision of model. It solves the problems of intricate in training algorithm and slow rate in convergence caused by the complicate topological structure in usual dynamic recurrent neural network.
Multi-Output System Identification Using Evolutionary Programming
1991-11-04
Evolutionary programming (EP) has been demonstrated to be an effective method of system identification of single-input-single-output (SISO) systems...This paper investigates the use of EP in system identification of single-input-multioutput (SIMO) systems. EP is used to identify parameters of a
Identifiability and identification of a Synthesis Load Model
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
A Synthesis Load Model (SLM) including both the power load and the distribution network has been proposed in the references. The identifiability of SLM is analyzed at first, it is concluded that the model parameters are identifiable if one of the resistance, reactance and the ratio of them is known. The conclusion is validated through a simulation example. A strategy for parameter identification of SLM is proposed with the combination of the component based approach and the measurement based approach. During parameter identification, only the key parameters playing very important roles in the dynamics of the load and the system are estimated, while the other parameters playing limited role are set as the default values. The proposed strategy is verified by the field measurements.
Subspace-based identification of discrete time-delay system
Institute of Scientific and Technical Information of China (English)
Qiang LIU; Jia-chen MA
2016-01-01
We investigate the identification problems of a class of linear stochastic time-delay systems with unknown delayed states in this study. A time-delay system is expressed as a delay differential equation with a single delay in the state vector. We first derive an equivalent linear time-invariant (LTI) system for the time-delay system using a state augmentation technique. Then a conventional subspace identification method is used to estimate augmented system matrices and Kalman state sequences up to a similarity transformation. To obtain a state-space model for the time-delay system, an alternate convex search (ACS) algorithm is presented to find a similarity transformation that takes the identified augmented system back to a form so that the time-delay system can be recovered. Finally, we reconstruct the Kalman state sequences based on the similarity transformation. The time-delay system matrices under the same state-space basis can be recovered from the Kalman state sequences and input-output data by solving two least squares problems. Numerical examples are to show the effectiveness of the proposed method.
Time Synchronization Module for Automatic Identification System
Institute of Scientific and Technical Information of China (English)
Choi Il-heung; Oh Sang-heon; Choi Dae-soo; Park Chan-sik; Hwang Dong-hwan; Lee Sang-jeong
2003-01-01
This paper proposed a design and implementation procedure of the Time Synchronization Module (TSM) for the Automatic Identification System (AIS). The proposed TSM module uses a Temperature Compensated Crystal Oscillator (TCXO) as a local reference clock, and consists of a Digitally Controlled Oscillator (DCO), a divider, a phase discriminator, and register blocks. The TSM measures time difference between the 1 PPS from the Global Navigation Satellite System (GNSS) receiver and the generated transmitter clock. The measured time difference is compensated by controlling the DCO and the transmit clock is synchronized to the Universal Time Coordinated (UTC). The designed TSM can also be synchronized to the reference time derived from the received message. The proposed module is tested using the experimental AIS transponder set. The experimental results show that the proposed module satisfies the functional and timing specification of the AIS technical standard, ITU-R M.1371.
Experimental Identification of Concentrated Nonlinearity in Aeroelastic System
Directory of Open Access Journals (Sweden)
Nayfeh Ali H
2012-07-01
Full Text Available Identification of concentrated nonlinearity in the torsional spring of an aeroelastic system is performed. This system consists of a rigid airfoil that is supported by a linear spring in the plunge motion and a nonlinear spring in the pitch motion. Quadratic and cubic nonlinearities in the pitch moment are introduced to model the concentrated nonlinearity. The representation of the aerodynamic loads by the Duhamel formulation yielded accurate values for the flutter speed and frequency. The results show that the use of the Duhamel formulation to represent the aerodynamic loads yields excellent agreement between the experimental data and the numerical predictions.
Compressive System Identification in the Linear Time-Invariant framework
Toth, Roland
2011-12-01
Selection of an efficient model parametrization (model order, delay, etc.) has crucial importance in parametric system identification. It navigates a trade-off between representation capabilities of the model (structural bias) and effects of over-parametrization (variance increase of the estimates). There exists many approaches to this widely studied problem in terms of statistical regularization methods and information criteria. In this paper, an alternative ℓ 1 regularization scheme is proposed for estimation of sparse linear-regression models based on recent results in compressive sensing. It is shown that the proposed scheme provides consistent estimation of sparse models in terms of the so-called oracle property, it is computationally attractive for large-scale over-parameterized models and it is applicable in case of small data sets, i.e., underdetermined estimation problems. The performance of the approach w.r.t. other regularization schemes is demonstrated in an extensive Monte Carlo study. © 2011 IEEE.
Identification of Multimodel LPV Models with Asymmetric Gaussian Weighting Function
Directory of Open Access Journals (Sweden)
Jie You
2013-01-01
Full Text Available This paper is concerned with the identification of linear parameter varying (LPV systems by utilizing a multimodel structure. To improve the approximation capability of the LPV model, asymmetric Gaussian weighting functions are introduced and compared with commonly used symmetric Gaussian functions. By this mean, locations of operating points can be selected freely. It has been demonstrated through simulations with a high purity distillation column that the identified models provide more satisfactory approximation. Moreover, an experiment is performed on real HVAC (heating, ventilation, and air-conditioning to further validate the effectiveness of the proposed approach.
[Groundwater organic pollution source identification technology system research and application].
Wang, Xiao-Hong; Wei, Jia-Hua; Cheng, Zhi-Neng; Liu, Pei-Bin; Ji, Yi-Qun; Zhang, Gan
2013-02-01
Groundwater organic pollutions are found in large amount of locations, and the pollutions are widely spread once onset; which is hard to identify and control. The key process to control and govern groundwater pollution is how to control the sources of pollution and reduce the danger to groundwater. This paper introduced typical contaminated sites as an example; then carried out the source identification studies and established groundwater organic pollution source identification system, finally applied the system to the identification of typical contaminated sites. First, grasp the basis of the contaminated sites of geological and hydrogeological conditions; determine the contaminated sites characteristics of pollutants as carbon tetrachloride, from the large numbers of groundwater analysis and test data; then find the solute transport model of contaminated sites and compound-specific isotope techniques. At last, through groundwater solute transport model and compound-specific isotope technology, determine the distribution of the typical site of organic sources of pollution and pollution status; invest identified potential sources of pollution and sample the soil to analysis. It turns out that the results of two identified historical pollution sources and pollutant concentration distribution are reliable. The results provided the basis for treatment of groundwater pollution.
Wiener-Hammerstein system identification - an evolutionary approach
Naitali, Abdessamad; Giri, Fouad
2016-01-01
The problem of identifying parametric Wiener-Hammerstein (WH) systems is addressed within the evolutionary optimisation context. Specifically, a hybrid culture identification method is developed that involves model structure adaptation using genetic recombination and model parameter learning using particle swarm optimisation. The method enjoys three interesting features: (1) the risk of premature convergence of model parameter estimates to local optima is significantly reduced, due to the constantly maintained diversity of model candidates; (2) no prior knowledge is needed except for upper bounds on the system structure indices; (3) the method is fully autonomous as no interaction is needed with the user during the optimum search process. The performances of the proposed method will be illustrated and compared to alternative methods using a well-established WH benchmark.
MOVING PERSON IDENTIFICATION IN VIDEO SURVEILLANCE SYSTEMS
Directory of Open Access Journals (Sweden)
A. Y. Solomatin
2014-07-01
Full Text Available The paper deals with an approach for a moving person identifying in video surveillance systems. The proposed solution consists of two successive stages. Selecting of a moving human from all other moving objects in a video stream takes place at the first stage. Human identification based on facial image takes place at the second stage. Detection of a human’s movement is performed via representation of the original video stream in a form of time series. Mathematical apparatus of a singular spectrum is applied for that purpose. The presence of motion is determined by analyzing the periodic components of time series constructed from color and brightness data of the original components of initial video stream. Identification of a person based on his facial image is done through representation of a facial image via two-dimensional matrix with the subsequent application of immune computing mathematical apparatus. Then the binding energy is calculated which shows similarity between the input facial image and faces stored in the training set. The proposed solution for a problem of a moving person’s identifying gives the opportunity to work with low quality video stream having a high level of noise or compression artifacts after encoding. The advantage of the method is implementation simplicity. Unlike traditional methods of computer vision, the proposed method does not require significant computational burden due to simple numerical operations. This method does not require pre-filtering of video images, therefore its performance speed is significantly increased.
Identification and Analysis of National Airspace System Resource Constraints
Smith, Jeremy C.; Marien, Ty V.; Viken, Jeffery K.; Neitzke, Kurt W.; Kwa, Tech-Seng; Dollyhigh, Samuel M.; Fenbert, James W.; Hinze, Nicolas K.
2015-01-01
This analysis is the deliverable for the Airspace Systems Program, Systems Analysis Integration and Evaluation Project Milestone for the Systems and Portfolio Analysis (SPA) focus area SPA.4.06 Identification and Analysis of National Airspace System (NAS) Resource Constraints and Mitigation Strategies. "Identify choke points in the current and future NAS. Choke points refer to any areas in the en route, terminal, oceanic, airport, and surface operations that constrain actual demand in current and projected future operations. Use the Common Scenarios based on Transportation Systems Analysis Model (TSAM) projections of future demand developed under SPA.4.04 Tools, Methods and Scenarios Development. Analyze causes, including operational and physical constraints." The NASA analysis is complementary to a NASA Research Announcement (NRA) "Development of Tools and Analysis to Evaluate Choke Points in the National Airspace System" Contract # NNA3AB95C awarded to Logistics Management Institute, Sept 2013.
Nonlinear stochastic system identification of skin using volterra kernels.
Chen, Yi; Hunter, Ian W
2013-04-01
Volterra kernel stochastic system identification is a technique that can be used to capture and model nonlinear dynamics in biological systems, including the nonlinear properties of skin during indentation. A high bandwidth and high stroke Lorentz force linear actuator system was developed and used to test the mechanical properties of bulk skin and underlying tissue in vivo using a non-white input force and measuring an output position. These short tests (5 s) were conducted in an indentation configuration normal to the skin surface and in an extension configuration tangent to the skin surface. Volterra kernel solution methods were used including a fast least squares procedure and an orthogonalization solution method. The practical modifications, such as frequency domain filtering, necessary for working with low-pass filtered inputs are also described. A simple linear stochastic system identification technique had a variance accounted for (VAF) of less than 75%. Representations using the first and second Volterra kernels had a much higher VAF (90-97%) as well as a lower Akaike information criteria (AICc) indicating that the Volterra kernel models were more efficient. The experimental second Volterra kernel matches well with results from a dynamic-parameter nonlinearity model with fixed mass as a function of depth as well as stiffness and damping that increase with depth into the skin. A study with 16 subjects showed that the kernel peak values have mean coefficients of variation (CV) that ranged from 3 to 8% and showed that the kernel principal components were correlated with location on the body, subject mass, body mass index (BMI), and gender. These fast and robust methods for Volterra kernel stochastic system identification can be applied to the characterization of biological tissues, diagnosis of skin diseases, and determination of consumer product efficacy.
Probing Signal Design for Power System Identification
Energy Technology Data Exchange (ETDEWEB)
Pierre, John W.; Zhou, Ning; Tuffner, Francis K.; Hauer, John F.; Trudnowski, Daniel J.; Mittelstadt, William
2010-05-31
This paper investigates the design of effective input signals for low-level probing of power systems. In 2005, 2006, and 2008 the Western Electricity Coordinating Council (WECC) conducted four large-scale system wide tests of the western interconnected power system where probing signals were injected by modulating the control signal at the Celilo end of the Pacific DC intertie. A major objective of these tests is the accurate estimation of the inter-area electromechanical modes. A key aspect of any such test is the design of an effective probing signal that leads to measured outputs rich in information about the modes. This paper specifically studies low-level probing signal design for power-system identification. The paper describes the design methodology and the advantages of this new probing signal which was successfully applied during these tests. This probing input is a multi-sine signal with its frequency content focused in the range of the inter-area modes. The period of the signal is over two minutes providing high-frequency resolution. Up to 15 cycles of the signal are injected resulting in a processing gain of 15. The resulting system response is studied in the time and frequency domains. Because of the new probing signal characteristics, these results show significant improvement in the output SNR compared to previous tests.
System Identification of Wind Turbines for Structural Health Monitoring
DEFF Research Database (Denmark)
Perisic, Nevena
. Thanks to the advanced system identification methods, the majority of these signals can be indirectly measured by assuming a realistic sensor scenario. This thesis addresses the problem of using system identification techniques on monitoring time-varying signals that direct measuring is prevented due...... techniques for time-varying system identification. The test case chosen hereto concerns blade bearing friction estimation. Different nonlinear system identification algorithms are considered and their performances are benchmarked on problems of time-varying parameter estimation in a blade bearing friction...
Bayesian robot system identification with input and output noise.
Ting, Jo-Anne; D'Souza, Aaron; Schaal, Stefan
2011-01-01
For complex robots such as humanoids, model-based control is highly beneficial for accurate tracking while keeping negative feedback gains low for compliance. However, in such multi degree-of-freedom lightweight systems, conventional identification of rigid body dynamics models using CAD data and actuator models is inaccurate due to unknown nonlinear robot dynamic effects. An alternative method is data-driven parameter estimation, but significant noise in measured and inferred variables affects it adversely. Moreover, standard estimation procedures may give physically inconsistent results due to unmodeled nonlinearities or insufficiently rich data. This paper addresses these problems, proposing a Bayesian system identification technique for linear or piecewise linear systems. Inspired by Factor Analysis regression, we develop a computationally efficient variational Bayesian regression algorithm that is robust to ill-conditioned data, automatically detects relevant features, and identifies input and output noise. We evaluate our approach on rigid body parameter estimation for various robotic systems, achieving an error of up to three times lower than other state-of-the-art machine learning methods.
Fractional System Identification: An Approach Using Continuous Order-Distributions
Hartley, Tom T.; Lorenzo, Carl F.
1999-01-01
This paper discusses the identification of fractional- and integer-order systems using the concept of continuous order-distribution. Based on the ability to define systems using continuous order-distributions, it is shown that frequency domain system identification can be performed using least squares techniques after discretizing the order-distribution.
Bayesian system identification of dynamical systems using highly informative training data
Green, P. L.; Cross, E. J.; Worden, K.
2015-05-01
This paper is concerned with the Bayesian system identification of structural dynamical systems using experimentally obtained training data. It is motivated by situations where, from a large quantity of training data, one must select a subset to infer probabilistic models. To that end, using concepts from information theory, expressions are derived which allow one to approximate the effect that a set of training data will have on parameter uncertainty as well as the plausibility of candidate model structures. The usefulness of this concept is then demonstrated through the system identification of several dynamical systems using both physics-based and emulator models. The result is a rigorous scientific framework which can be used to select 'highly informative' subsets from large quantities of training data.
Directory of Open Access Journals (Sweden)
Kazuhiko Hiramoto
2012-01-01
Full Text Available An LMI-based method for the integrated system identification and controller design is proposed in the paper. We use the fact that a class of a system identification problem results in an LMI optimization problem. By combining LMIs for the system identification and those to obtain a discrete time controller we propose a framework to integrate two steps for the model-based control system design, that is, the system identification and the controller synthesis. The framework enables us to obtain a good model for control and a model-based feedback controller simultaneously in the sense of the closed-loop performance. An iterative design algorithm similar to so-called Windsurfer Approach is presented.
System IDentification Programs for AirCraft (SIDPAC)
Morelli, Eugene A.
2002-01-01
A collection of computer programs for aircraft system identification is described and demonstrated. The programs, collectively called System IDentification Programs for AirCraft, or SIDPAC, were developed in MATLAB as m-file functions. SIDPAC has been used successfully at NASA Langley Research Center with data from many different flight test programs and wind tunnel experiments. SIDPAC includes routines for experiment design, data conditioning, data compatibility analysis, model structure determination, equation-error and output-error parameter estimation in both the time and frequency domains, real-time and recursive parameter estimation, low order equivalent system identification, estimated parameter error calculation, linear and nonlinear simulation, plotting, and 3-D visualization. An overview of SIDPAC capabilities is provided, along with a demonstration of the use of SIDPAC with real flight test data from the NASA Glenn Twin Otter aircraft. The SIDPAC software is available without charge to U.S. citizens by request to the author, contingent on the requestor completing a NASA software usage agreement.
Computational requirements for on-orbit identification of space systems
Hadaegh, Fred Y.
1988-01-01
For the future space systems, on-orbit identification (ID) capability will be required to complement on-orbit control, due to the fact that the dynamics of large space structures, spacecrafts, and antennas will not be known sufficiently from ground modeling and testing. The computational requirements for ID of flexible structures such as the space station (SS) or the large deployable reflectors (LDR) are however, extensive due to the large number of modes, sensors, and actuators. For these systems the ID algorithm operations need not be computed in real-time, only in near real-time, or an appropriate mission time. Consequently the space systems will need advanced processors and efficient parallel processing algorithm design and architectures to implement the identification algorithms in near real-time. The MAX computer currently being developed may handle such computational requirements. The purpose is to specify the on-board computational requirements for dynamic and static identification for large space structures. The computational requirements for six ID algorithms are presented in the context of three examples: the JPL/AFAL ground antenna facility, the space station (SS), and the large deployable reflector (LDR).
System identification of physiological systems using short data segments.
Ludvig, Daniel; Perreault, Eric J
2012-12-01
System identification of physiological systems poses unique challenges, especially when the structure of the system under study is uncertain. Nonparametric techniques can be useful for identifying system structure, but these typically assume stationarity and require large amounts of data. Both of these requirements are often not easily obtained in the study of physiological systems. Ensemble methods for time-varying nonparametric estimation have been developed to address the issue of stationarity, but these require an amount of data that can be prohibitive for many experimental systems. To address this issue, we developed a novel algorithm that uses multiple short data segments. Using simulation studies, we showed that this algorithm produces system estimates with lower variability than previous methods when limited data are present. Furthermore, we showed that the new algorithm generates time-varying system estimates with lower total error than an ensemble method. Thus, this algorithm is well suited for the identification of physiological systems that vary with time or from which only short segments of stationary data can be collected.
Identification of Nonlinear Rational Systems Using A Prediction-Error Estimation Algorithm
1987-01-01
Identification of discrete-time noninear stochastic systems which can be represented by a rational input-output model is considered. A prediction-error parameter estimation algorithm is developed and a criterion is derived using results from the theory of hypothesis testing to determine the correct model structure. The identification of a simulated system and a heat exchanger are included to illustrate the algorithms.
Directory of Open Access Journals (Sweden)
Francisco Rodríguez-Trelles
1998-12-01
Full Text Available Current efforts to study the biological effects of global change have focused on ecological responses, particularly shifts in species ranges. Mostly ignored are microevolutionary changes. Genetic changes may be at least as important as ecological ones in determining species' responses. In addition, such changes may be a sensitive indicator of global changes that will provide different information than that provided by range shifts. We discuss potential candidate systems to use in such monitoring programs. Studies of Drosophila subobscura suggest that its chromosomal inversion polymorphisms are responding to global warming. Drosophila inversion polymorphisms can be useful indicators of the effects of climate change on populations and ecosystems. Other species also hold the potential to become important indicators of global change. Such studies might significantly influence ecosystem conservation policies and research priorities.
Task Characterisation and Cross-Platform Programming Through System Identification
Directory of Open Access Journals (Sweden)
Roberto Iglesias
2008-11-01
Full Text Available Developing robust and reliable control code for autonomous mobile robots is difficult, because the interaction between a physical robot and the environment is highly complex, it is subject to noise and variation, and therefore partly unpredictable. This means that to date it is not possible to predict robot behaviour, based on theoretical models. Instead, current methods to develop robot control code still require a substantial trial-and-error component to the software design process. Such iterative refinement could be reduced, we argue, if a more profound theoretical understanding of robot-environment interaction existed. In this paper, we therefore present a modelling method that generates a faithful model of a robot's interaction with its environment, based on data logged while observing a physical robot's behaviour. Because this modelling method - nonlinear modelling using polynomials - is commonly used in the engineering discipline of system identification, we refer to it here as "robot identification". We show in this paper that using robot identification to obtain a computer model of robot environment interaction offers several distinct advantages:
1. Very compact representations (one-line programs of the robot control program are generated
2.The model can be analysed, for example through sensitivity analysis, leading to a better understanding of the essential parameters underlying the robot's behaviour, and
3. The generated, compact robot code can be used for cross-platform robot programming, allowing fast transfer of robot code from one type of robot to another.
We demonstrate these points through experiments with a Magellan Pro and a Nomad 200 mobile robot.
Directory of Open Access Journals (Sweden)
Laila Khalilzadeh Ganjali-khani
2013-01-01
Full Text Available One of the most effective strategies for steam generator efficiency enhancement is to improve the control system. For such an improvement, it is essential to have an accurate model for the steam generator of power plant. In this paper, an industrial steam generator is considered as a nonlinear multivariable system for identification. An important step in nonlinear system identification is the development of a nonlinear model. In recent years, artificial neural networks have been successfully used for identification of nonlinear systems in many researches. Wavelet neural networks (WNNs also are used as a powerful tool for nonlinear system identification. In this paper we present a time delay neural network model and a WNN model in order to identify an industrial steam generator. Simulation results show the effectiveness of the proposed models in the system identification and demonstrate that the WNN model is more precise to estimate the plant outputs.
Cardiovascular system identification: Simulation study using arterial and central venous pressures.
Karamolegkos, Nikolaos; Vicario, Francesco; Chbat, Nicolas W
2015-08-01
The paper presents a study of the identifiability of a lumped model of the cardiovascular system. The significance of this work from the existing literature is in the potential advantage of using both arterial and central venous (CVP) pressures, two signals that are frequently monitored in the critical care unit. The analysis is done on the system's state-space representation via control theory and system identification techniques. Non-parametric state-space identification is preferred over other identification techniques as it optimally assesses the order of a model, which best describes the input-output data, without any prior knowledge about the system. In particular, a recent system identification algorithm, namely Observer Kalman Filter Identification with Deterministic Projection, is used to identify a simplified version of an existing cardiopulmonary model. The outcome of the study highlights the following two facts. In the deterministic (noiseless) case, the theoretical indicators report that the model is fully identifiable, whereas the stochastic case reveals the difficulty in determining the complete system's dynamics. This suggests that even with the use of CVP as an additional pressure signal, the identification of a more detailed (high order) model of the circulatory system remains a challenging task.
Damage detection in structures through nonlinear excitation and system identification
Hajj, Muhammad R.; Bordonaro, Giancarlo G.; Nayfeh, Ali H.; Duke, John C., Jr.
2008-03-01
Variations in parameters representing natural frequency, damping and effective nonlinearities before and after damage initiation in a beam carrying a lumped mass are assessed. The identification of these parameters is performed by exploiting and modeling nonlinear behavior of the beam-mass system and matching an approximate solution of the representative model with quantities obtained from spectral analysis of measured vibrations. The representative model and identified coefficients are validated through comparison of measured and predicted responses. Percentage variations of the identified parameters before and after damage initiation are determined to establish their sensitivities to the state of damage of the beam. The results show that damping and effective nonlinearity parameters are more sensitive to damage initiation than the system's natural frequency. Moreover, the sensitivity of nonlinear parameters to damage is better established using a physically-derived parameter rather than spectral amplitudes of harmonic components.
IBCIS:Intelligent blood cell identification system
Institute of Scientific and Technical Information of China (English)
Adnan Khashman
2008-01-01
The analysis of blood cells in microscope images can provide useful information concerning the health of patients.There are three major blood cell types,namely,erythrocytes (red),leukocytes (white),and platelets.Manual classification is time consuming and susceptible to error due to the different morphological features of the cells.This paper presents an intelligent system that simulates a human visual inspection and classification of the three blood cell types.The proposed system comprises two phases:The image preprocessing phase where blood cell features are extracted via global pattern averaging,and the neural network arbitration phase where training is the first and then classification is carried out.Experimental results suggest that the proposed method performs well in identifying blood cell types regardless of their irregular shapes,sizes and orientation,thus providing a fast,simple and efficient rotational and scale invariant blood cell identification system which can be used in automating laboratory reporting.
Lightweight autonomous chemical identification system (LACIS)
Lozos, George; Lin, Hai; Burch, Timothy
2012-06-01
Smiths Detection and Intelligent Optical Systems have developed prototypes for the Lightweight Autonomous Chemical Identification System (LACIS) for the US Department of Homeland Security. LACIS is to be a handheld detection system for Chemical Warfare Agents (CWAs) and Toxic Industrial Chemicals (TICs). LACIS is designed to have a low limit of detection and rapid response time for use by emergency responders and could allow determination of areas having dangerous concentration levels and if protective garments will be required. Procedures for protection of responders from hazardous materials incidents require the use of protective equipment until such time as the hazard can be assessed. Such accurate analysis can accelerate operations and increase effectiveness. LACIS is to be an improved point detector employing novel CBRNE detection modalities that includes a militaryproven ruggedized ion mobility spectrometer (IMS) with an array of electro-resistive sensors to extend the range of chemical threats detected in a single device. It uses a novel sensor data fusion and threat classification architecture to interpret the independent sensor responses and provide robust detection at low levels in complex backgrounds with minimal false alarms. The performance of LACIS prototypes have been characterized in independent third party laboratory tests at the Battelle Memorial Institute (BMI, Columbus, OH) and indoor and outdoor field tests at the Nevada National Security Site (NNSS). LACIS prototypes will be entering operational assessment by key government emergency response groups to determine its capabilities versus requirements.
Identification of linear continuous-time system using wavelet modulating filters
Institute of Scientific and Technical Information of China (English)
贺尚红; 钟掘
2004-01-01
An approach to identification of linear continuous-time system is studied with modulating functions. Based on wavelet analysis theory, the multi-resolution modulating functions are designed, and the corresponding filters have been analyzed. Using linear modulating filters, we can obtain an identification model that is parameterized directly in continuous-time model parameters. By applying the results from discrete-time model identification to the obtained identification model, a continuous-time estimation method is developed. Considering the accuracy of parameter estimates, an instrumental variable(V) method is proposed, and the design of modulating integral filter is discussed. The relationship between the accuracy of identification and the parameter of modulating filter is investigated, and some points about designing Gaussian wavelet modulating function are outlined. Finally, a simulation study is also included to verify the theoretical results.
System identification advances and case studies
Mehra, Raman K
1976-01-01
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank mat
Identification of Hammerstein Model Based on Quantum Genetic Algorithm
Directory of Open Access Journals (Sweden)
Zhang Hai Li
2013-07-01
Full Text Available Nonlinear system identification is a main topic of modern identification. A new method for nonlinear system identification is presented by using Quantum Genetic Algorithm(QGA.The problems of nonlinear system identification are cast as function optimization overprameter space，and the Quantum Genetic Algorithm is adopted to solve the optimization problem. Simulation experiments show that: compared with the genetic algorithm, quantum genetic algorithm is an effective swarm intelligence algorithm, its salient features of the algorithm parameters, small population size, and the use of Quantum gate update populations, greatly improving the recognition in the optimization of speed and accuracy. Simulation results show the effectiveness of the proposed method.
Mathematics of tsunami: modelling and identification
Krivorotko, Olga; Kabanikhin, Sergey
2015-04-01
Tsunami (long waves in the deep water) motion caused by underwater earthquakes is described by shallow water equations ( { ηtt = div (gH (x,y)-gradη), (x,y) ∈ Ω, t ∈ (0,T ); η|t=0 = q(x,y), ηt|t=0 = 0, (x,y) ∈ Ω. ( (1) Bottom relief H(x,y) characteristics and the initial perturbation data (a tsunami source q(x,y)) are required for the direct simulation of tsunamis. The main difficulty problem of tsunami modelling is a very big size of the computational domain (Ω = 500 × 1000 kilometres in space and about one hour computational time T for one meter of initial perturbation amplitude max|q|). The calculation of the function η(x,y,t) of three variables in Ω × (0,T) requires large computing resources. We construct a new algorithm to solve numerically the problem of determining the moving tsunami wave height S(x,y) which is based on kinematic-type approach and analytical representation of fundamental solution. Proposed algorithm of determining the function of two variables S(x,y) reduces the number of operations in 1.5 times than solving problem (1). If all functions does not depend on the variable y (one dimensional case), then the moving tsunami wave height satisfies of the well-known Airy-Green formula: S(x) = S(0)° --- 4H (0)/H (x). The problem of identification parameters of a tsunami source using additional measurements of a passing wave is called inverse tsunami problem. We investigate two different inverse problems of determining a tsunami source q(x,y) using two different additional data: Deep-ocean Assessment and Reporting of Tsunamis (DART) measurements and satellite altimeters wave-form images. These problems are severely ill-posed. The main idea consists of combination of two measured data to reconstruct the source parameters. We apply regularization techniques to control the degree of ill-posedness such as Fourier expansion, truncated singular value decomposition, numerical regularization. The algorithm of selecting the truncated number of
Advanced 3D Object Identification System Project
National Aeronautics and Space Administration — During the Phase I effort, OPTRA developed object detection, tracking, and identification algorithms and successfully tested these algorithms on computer-generated...
A Design of Generalized Predictive Control Systems Using a Memory-Based System Identification
Takao, Kenji; Yamamoto, Toru; Hinamoto, Takao
In this paper, a new system identification scheme is proposed based on a memory-based modeling (MBM) method. According to the MBM method, some local models are automatically generated using input/output data pairs of the controlled object stored in the data-base. Especially, it is well known that the MBM method works suitably on nonlinear systems. Therefore, even if the nonlinearities are contained in the controlled object, accuracy identification can be performed by the proposed method. Moreover, since the parameter estimates are easily applied to many existing controllers, the good control result can be obtained for nonlinear systems. In this paper, the generalized predictive control (GPC) is used as the one of existing controllers, because the GPC is designed based on multi-step prediction, and is effective for systems with large, ambiguous and/or time-variant time-delays. Finally, the effectiveness of the newly proposed control scheme is numerically evaluated on some simulation examples.
Time-Delay System Identification Using Genetic Algorithm
DEFF Research Database (Denmark)
Yang, Zhenyu; Seested, Glen Thane
2013-01-01
Due to the unknown dead-time coefficient, the time-delay system identification turns to be a non-convex optimization problem. This paper investigates the identification of a simple time-delay system, named First-Order-Plus-Dead-Time (FOPDT), by using the Genetic Algorithm (GA) technique...
Directory of Open Access Journals (Sweden)
Wei Huang
2013-01-01
Full Text Available We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA. The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.
Blind adaptive identification of FIR channel in chaotic communication systems
Institute of Scientific and Technical Information of China (English)
Wang Bao-Yun; Tommy W.S.Chow; K.T.Ng
2004-01-01
In this paper we study the problem of blind channel identification in chaotic communications. An adaptive algorithm is proposed, which exploits the boundness property of chaotic signals. Compared with the EKF-based approach, the proposed algorithm achieves a great complexity gain but at the expense of a slight accuracy degradation.However, our approach enjoys the important advantage that it does not require the a priori information such as nonlinearity of chaotic dynamics and the variances of measurement noise and the coefficient model noise. In addition,our approach is applicable to the ARMA system.
Energy Technology Data Exchange (ETDEWEB)
Alves, Antonio Carlos Pinto Dias
2000-09-01
A nuclear power plant has a myriad of complex system and sub-systems that, working cooperatively, make the control of the whole plant. Nevertheless their operation be automatic most of the time, the integral understanding of their internal- logic can be away of the comprehension of even experienced operators because of the poor interpretability those controls offer. This difficulty does not happens only in nuclear power plants but in almost every a little more complex control system. Neuro-fuzzy models have been used for the last years in a attempt of suppress these difficulties because of their ability of modelling in linguist form even a system which behavior is extremely complex. This is a very intuitive human form of interpretation and neuro-fuzzy model are gathering increasing acceptance. Unfortunately, neuro-fuzzy models can grow up to become of hard interpretation because of the complexity of the systems under modelling. In general, that growing occurs in function of redundant rules or rules that cover a very little domain of the problem. This work presents an identification method for neuro-fuzzy models that not only allows models grow in function of the existent complexity but that beforehand they try to self-adapt to avoid the inclusion of new rules. This form of construction allowed to arrive to highly interpretative neuro-fuzzy models even of very complex systems. The use of this kind of technique in modelling the control of the pressurizer of a PWR nuclear power plant allowed verify its validity and how neuro-fuzzy models so built can be useful in understanding the automatic operation of a nuclear power plant. (author)
Indirect Identification of Linear Stochastic Systems with Known Feedback Dynamics
Huang, Jen-Kuang; Hsiao, Min-Hung; Cox, David E.
1996-01-01
An algorithm is presented for identifying a state-space model of linear stochastic systems operating under known feedback controller. In this algorithm, only the reference input and output of closed-loop data are required. No feedback signal needs to be recorded. The overall closed-loop system dynamics is first identified. Then a recursive formulation is derived to compute the open-loop plant dynamics from the identified closed-loop system dynamics and known feedback controller dynamics. The controller can be a dynamic or constant-gain full-state feedback controller. Numerical simulations and test data of a highly unstable large-gap magnetic suspension system are presented to demonstrate the feasibility of this indirect identification method.
Identification of linear systems by an asymptotically stable observer
Phan, Minh Q.; Horta, Lucas G.; Juang, Jer-Nan; Longman, Richard W.
1992-01-01
A formulation is presented for the identification of a linear multivariable system from single or multiple sets of input-output data. The system input-output relationship is expressed in terms of an observer, which is made asymptotically stable by an embedded eigenvalue assignment procedure. The prescribed eigenvalues for the observer may be real, complex, mixed real and complex, or zero. In this formulation, the Markov parameters of the observer are identified from input-output data. The Markov parameters of the actual system are then recovered from those of the observer and used to obtain a state space model of the system by standard realization techniques. The basic mathematical formulation is derived, and extensive numerical examples using simulated noise-free data are presented to illustrate the proposed method.
Particle Identification with the LHCb RICH System
Harnew, Neville
2005-01-01
The LHCb experiment uses a Ring Imaging Cherenkov (RICH) system to provide particle identification over the momentum range 2-100 GeV/c. Two RICH detectors are employed. The upstream detector, RICH1, utilizes both aerogel and C$_4$F$_{10}$ gas radiators whilst the downstream RICH2 uses a CF$_4$ gas radiator. The RICH2 detector has been fabricated and is installed in the LHCb interaction region; RICH1 has a programme of phased design and construction. Novel Hybrid Photon Detectors (HPDs) have been developed in collaboration with industry to detect the Cherenkov photons in the wavelength range 200-600 nm. The HPDs are enclosed in iron shielding and Mumetal cylinders to allow operation in magnetic fields up to 50mT. The performance of pre-series HPDs and the results obtained from a particle test beam using the full LHCb readout chain is presented. The production of a total of 484 HPDs required for the two RICH detectors has recently commenced. The expected performance of the LHCb RICH system, obtained from real...
A Survey of Modelling and Identification of Quadrotor Robot
Directory of Open Access Journals (Sweden)
Xiaodong Zhang
2014-01-01
Full Text Available A quadrotor is a rotorcraft capable of hover, forward flight, and VTOL and is emerging as a fundamental research and application platform at present with flexibility, adaptability, and ease of construction. Since a quadrotor is basically considered an unstable system with the characteristics of dynamics such as being intensively nonlinear, multivariable, strongly coupled, and underactuated, a precise and practical model is critical to control the vehicle which seems to be simple to operate. As a rotorcraft, the dynamics of a quadrotor is mainly dominated by the complicated aerodynamic effects of the rotors. This paper gives a tutorial of the platform configuration, methodology of modeling, comprehensive nonlinear model, the aerodynamic effects, and model identification for a quadrotor.
Dynamic model of production enterprises based on accounting registers and its identification
Sirazetdinov, R. T.; Samodurov, A. V.; Yenikeev, I. A.; Markov, D. S.
2016-06-01
The report focuses on the mathematical modeling of economic entities based on accounting registers. Developed the dynamic model of financial and economic activity of the enterprise as a system of differential equations. Created algorithms for identification of parameters of the dynamic model. Constructed and identified the model of Russian machine-building enterprises.
Identification of a Manipulator Model Using the Input Error Method in the Mathematica Program
Directory of Open Access Journals (Sweden)
Leszek CEDRO
2009-06-01
Full Text Available The problem of parameter identification for a four-degree-of-freedom robot was solved using the Mathematica program. The identification was performed by means of specially developed differential filters [1]. Using the example of a manipulator, we analyze the capabilities of the Mathematica program that can be applied to solve problems related to the modeling, control, simulation and identification of a system [2]. The responses of the identification process for the variables and the values of the quality function are included.
NNSYSID and NNCTRL Tools for system identification and control with neural networks
DEFF Research Database (Denmark)
Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad
2001-01-01
Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...
Identification study on base isolation systems by full-scale buildings
Energy Technology Data Exchange (ETDEWEB)
Loh, C.; Lee, C. (National Taiwan University, Taipei (Taiwan, Province of China))
1992-10-15
In this paper, seismic response characteristics of two types of base-isolated buildings are investigated by using identification techniques. Equivalent linear system was first assumed in connection with the dynamic behavior of the structural system, then the nonlinear Bouc-Wen's model was assumed as second stage to model the isolation system and evaluate the hysteretic damping of the system. For structural identification, both ' model minimization method ' and ' extended Kalman filtering techniques ' were used. It was illustrated that the natural frequency of the isolation system of ' Law and Justice Center Building ' in California, the USA was designed greater than the natural frequency of the building while the natural frequency of the isolation system of the test building of Tohoku University was designed smaller than the natural frequency of the building. These results could be obtained by the system identification techniques. 20 refs., 12 figs., 2 tabs.
Evaluation of fungichrom 1: A new yeast identification system
Directory of Open Access Journals (Sweden)
Umabala P
2002-01-01
Full Text Available Advances in anti-fungal therapy necessitate the need for accurate identification of fungi especially yeasts to their species level for more effective management. Unlike the time consuming conventional methods of yeast identification using fermentation and assimilation patterns of various carbohydrates, the new commercialized yeast identification systems are simpler, rapid and are particularly easy to interpret. In our study, a new colorimetric yeast identification system-Fungichrom 1(International microbio, Signes, France was evaluated against the conventional method to identify 50 clinical isolates of yeasts belonging to the genera -Candida, Cryptococcus, Geotrichum. 96% agreement was found between the two methods.
Evaluation of Fungichrom 1: a new yeast identification system.
Umabala, P; Satheeshkumar, T; Lakshmi, V
2002-01-01
Advances in anti-fungal therapy necessitate the need for accurate identification of fungi especially yeasts to their species level for more effective management. Unlike the time consuming conventional methods of yeast identification using fermentation and assimilation patterns of various carbohydrates, the new commercialized yeast identification systems are simpler, rapid and are particularly easy to interpret. In our study, a new colorimetric yeast identification system-Fungichrom 1(International microbio, Signes, France) was evaluated against the conventional method to identify 50 clinical isolates of yeasts belonging to the genera -Candida, Cryptococcus, Geotrichum. 96% agreement was found between the two methods.
SVM Model for Identification of human GPCRs
Shrivastava, Sonal; Malik, M M
2010-01-01
G-protein coupled receptors (GPCRs) constitute a broad class of cell-surface receptors in eukaryotes and they possess seven transmembrane a-helical domains. GPCRs are usually classified into several functionally distinct families that play a key role in cellular signalling and regulation of basic physiological processes. We can develop statistical models based on these common features that can be used to classify proteins, to predict new members, and to study the sequence-function relationship of this protein function group. In this study, SVM based classification model has been developed for the identification of human gpcr sequences. Sequences of Level 1 subfamilies of Class A rhodopsin is considered as case study. In the present study, an attempt has been made to classify GPCRs on the basis of species. The present study classifies human gpcr sequences with rest of the species available in GPCRDB. Classification is based on specific information derived from the n-terminal and extracellular loops of the sequ...
49 CFR 1542.211 - Identification systems.
2010-10-01
... secured area or SIDA continuously displays the identification medium issued to that individual on the... individual who has authorized unescorted access to secured areas and SIDA's to ascertain the authority of any... approved identification media. The procedure must— (1) Apply uniformly in secured areas, SIDAs, and...
2011-03-06
The PIs current research and development, funded by AFOSR, aims to develop novel means of vibration control for aerospace systems, system ... identification procedures for strongly nonlinear dynamical systems, and a fully passive limit cycle oscillation (LCO) suppression system for a model generic
System Identification of a Non-Uniformly Sampled Multi-Rate System in Aluminium Electrolysis Cells
Directory of Open Access Journals (Sweden)
Håkon Viumdal
2014-07-01
Full Text Available Standard system identification algorithms are usually designed to generate mathematical models with equidistant sampling instants, that are equal for both input variables and output variables. Unfortunately, real industrial data sets are often disrupted by missing samples, variations of sampling rates in the different variables (also known as multi-rate systems, and intermittent measurements. In industries with varying events based maintenance or manual operational measures, intermittent measurements are performed leading to uneven sampling rates. Such is the case with aluminium smelters, where in addition the materials fed into the cell create even more irregularity in sampling. Both measurements and feeding are mostly manually controlled. A simplified simulation of the metal level in an aluminium electrolysis cell is performed based on mass balance considerations. System identification methods based on Prediction Error Methods (PEM such as Ordinary Least Squares (OLS, and the sub-space method combined Deterministic and Stochastic system identification and Realization (DSR, and its variants are applied to the model of a single electrolysis cell as found in the aluminium smelters. Aliasing phenomena due to large sampling intervals can be crucial in avoiding unsuitable models, but with knowledge about the system dynamics, it is easier to optimize the sampling performance, and hence achieve successful models. The results based on the simulation studies of molten aluminium height in the cells using the various algorithms give results which tally well with the synthetic data sets used. System identification on a smaller data set from a real plant is also implemented in this work. Finally, some concrete suggestions are made for using these models in the smelters.
Nonlinear systems identification and control via dynamic multitime scales neural networks.
Fu, Zhi-Jun; Xie, Wen-Fang; Han, Xuan; Luo, Wei-Dong
2013-11-01
This paper deals with the adaptive nonlinear identification and trajectory tracking via dynamic multilayer neural network (NN) with different timescales. Two NN identifiers are proposed for nonlinear systems identification via dynamic NNs with different timescales including both fast and slow phenomenon. The first NN identifier uses the output signals from the actual system for the system identification. In the second NN identifier, all the output signals from nonlinear system are replaced with the state variables of the NNs. The online identification algorithms for both NN identifier parameters are proposed using Lyapunov function and singularly perturbed techniques. With the identified NN models, two indirect adaptive NN controllers for the nonlinear systems containing slow and fast dynamic processes are developed. For both developed adaptive NN controllers, the trajectory errors are analyzed and the stability of the systems is proved. Simulation results show that the controller based on the second identifier has better performance than that of the first identifier.
Neural-Fuzzy Approach for System Identification.
Tien, B.T.
1997-01-01
Most real-world processes have nonlinear and complex dynamics. Conventional methods of constructing nonlinear models from first principles are time consuming and require a level of knowledge about the internal functioning of the system that is often not available. Consequently, in such cases a nonli
System identification of Wiener systems with B-spline functions using De Boor recursion
Hong, X.; Mitchell, R. J.; Chen, S.
2013-09-01
In this article a simple and effective algorithm is introduced for the system identification of the Wiener system using observational input/output data. The nonlinear static function in the Wiener system is modelled using a B-spline neural network. The Gauss-Newton algorithm is combined with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialisation scheme. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.
Nonlinear system identification based on internal recurrent neural networks.
Puscasu, Gheorghe; Codres, Bogdan; Stancu, Alexandru; Murariu, Gabriel
2009-04-01
A novel approach for nonlinear complex system identification based on internal recurrent neural networks (IRNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This approach employs internal state estimation when no measurements coming from the sensors are available for the system states. A modified backpropagation algorithm is introduced in order to train the IRNN for nonlinear system identification. The performance of the proposed design approach is proven on a car simulator case study.
Closed Loop System Identification with Genetic Algorithms
Whorton, Mark S.
2004-01-01
High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.
Toward Mending Two Nation-Scale Brokered Identification Systems
Directory of Open Access Journals (Sweden)
Brandão Luís T. A. N.
2015-06-01
Full Text Available Available online public/governmental services requiring authentication by citizens have considerably expanded in recent years. This has hindered the usability and security associated with credential management by users and service providers. To address the problem, some countries have proposed nation-scale identification/authentication systems that intend to greatly reduce the burden of credential management, while seemingly offering desirable privacy benefits. In this paper we analyze two such systems: the Federal Cloud Credential Exchange (FCCX in the United States and GOV.UK Verify in the United Kingdom, which altogether aim at serving more than a hundred million citizens. Both systems propose a brokered identification architecture, where an online central hub mediates user authentications between identity providers and service providers. We show that both FCCX and GOV.UK Verify suffer from serious privacy and security shortcomings, fail to comply with privacy-preserving guidelines they are meant to follow, and may actually degrade user privacy. Notably, the hub can link interactions of the same user across different service providers and has visibility over private identifiable information of citizens. In case of malicious compromise it is also able to undetectably impersonate users. Within the structural design constraints placed on these nation-scale brokered identification systems, we propose feasible technical solutions to the privacy and security issues we identified. We conclude with a strong recommendation that FCCX and GOV.UK Verify be subject to a more in-depth technical and public review, based on a defined and comprehensive threat model, and adopt adequate structural adjustments.
Identification of coefficients in platform drift error model
Institute of Scientific and Technical Information of China (English)
邓正隆; 徐松艳; 付振宪
2002-01-01
The identification of the coefficients in the drift error model of a floated gyro inertial nawgation plat-form was investigated by following the principle of the inertial navigation platform and using gyro and accelerom-eter output models, and a complete platform drift error model was established, with parameters as state varia-bles, thereby establishing the system state equation and observation equation. Since these two equations areboth nonlinear, the Extended Kalman Filter (EKF) was adopted. Then the problem of parameter identificationwas converted into a problem of state estimation. During the simulation, multi-position testing schemes were de-signed to motivated the parameters by gravity acceleration. Using these schemes, twenty-four error coefficientsof three gyros and six error coefficients of three accelerometers were identified, which showed the feasibility ofthis method.
Identification of slow molecular order parameters for Markov model construction
Perez-Hernandez, Guillermo; Giorgino, Toni; de Fabritiis, Gianni; Noé, Frank
2013-01-01
A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes, involving (i) identification of the structural changes involved in these processes, and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, Master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the "slow subspace" of a large set of prior order parameters - either g...
Identification and robust control of linear parameter-varying systems
Lee, Lawton Hubert
This dissertation deals with linear parameter-varying (LPV) systems: linear dynamic systems that depend on time-varying parameters. These systems appear in gain scheduling problems, and much recent research has been devoted to their prospective usefulness for systematic gain scheduling. We primarily focus on robust control of uncertain LPV systems and identification of LPV systems that are modelable as linear-fractional transformations (LFTs). Using parameter-dependent quadratic Lyapunov functions, linear matrix inequalities (LMIs), and scaled small-gain arguments, we define notions of stability and induced-{cal L}sb2 performance for uncertain LPV systems whose parameters and rates of parameter variation satisfy given bounds. The performance criterion involves integral quadratic constraints and implies naturally parameter-dependent induced-{cal L}sb2 norm bounds. We formulate and solve an {cal H}sb{infty}-like control problem for an LPV plant with measurable parameters and an "Output/State Feedback" structure: the feedback outputs include some noiselessly measured states. Necessary and sufficient solvability conditions reduce to LMIs that can be solved approximately using finite-dimensional convex programming. Reduced-order LPV controllers are constructed from the LMI solutions. A D-K iteration-like procedure provides robustness to structured, time-varying, parametric uncertainty. The design method is applied to a motivating example: flight control for the F-16 VISTA throughout its subsonic flight envelope. Parameter-dependent weights and {cal H}sb{infty} design principles describe the performance objectives. Closed-loop responses exhibited by nonlinear simulations indicate satisfactory flying qualities. Identification of linear-fractional LPV systems is treated using maximum-likelihood parameter estimation. Computing the gradient and Hessian of a maximum-likelihood cost function reduces to simulating one LPV filter per identified parameter. We use nonlinear
Offline synchronization of data acquisition systems using system identification
Maes, K.; Reynders, E.; Rezayat, A.; Roeck, G. De; Lombaert, G.
2016-10-01
This paper presents a technique for offline time synchronization of data acquisition systems. The technique can be applied when real-time synchronization of data acquisition systems is impossible or not sufficiently accurate. It allows for accurate synchronization based on the acquired dynamic response of the structure only, without requiring a common response or the use of a trigger signal. The synchronization is performed using the results obtained from system identification, and assumes linear dynamic behavior of the structure and proportional damping of the structural modes. A demonstration for a laboratory experiment on a cantilever steel beam shows that the proposed methodology can be used for accurate time synchronization, resulting in a significant improvement of the accuracy of the identified mode shapes.
System identification and robust control of a portable proton exchange membrane full-cell system
Energy Technology Data Exchange (ETDEWEB)
Wang, Fu-Cheng; Yang, Yee-Pien; Huang, Chi-Wei; Chen, Hsuan-Tsung [Department of Mechanical Engineering, National Taiwan University, Taipei (Taiwan); Chang, Hsin-Ping [Chung Shan Institute of Science and Technology (CSIST), Armaments Bureau, M.N.D (Taiwan)
2007-02-10
This paper will discuss the application of system identification techniques and robust control strategies to a proton exchange membrane fuel-cell system. The fuel-cell system's dynamic behaviour is influenced by many factors, such as the reaction mechanism, pressure, flow-rate, composition and temperature change, and is inherently non-linear and time varying. From a system point of view, however, the system can be modelled as a two-input, two-output linear time-invariant system whose inputs are hydrogen and air flow rates, and whose outputs are cell voltage and current. On the other hand, the system's non-linearities and time-varying characteristics can be regarded as system uncertainties and disturbances that are treated by the designed robust controllers. This paper is comprised of three parts. First, system identification techniques were adopted to model the system's transfer functions. Second, the H{sub {infinity}} robust control strategies were applied to stabilise the system. Finally, the system's stability and performance were compromised by introducing weighting functions to the controller's design. From the experimental results, the designed H{sub {infinity}} robust controllers were deemed effective. (author)
Institute of Scientific and Technical Information of China (English)
张庆舟
2015-01-01
By using internal expert approach,key performance indicators and N level risk classification,a kind of identification index system and forewarning model of personal insurance fraud were initially con-structed.Insurance fraud had seriously harmed to the healthy development of the insurance industry.In re-cent years the personal insurance fraud was becoming more and more serious.Therefore,establishment of personal insurance fraud identification index system and forewarning model was particularly urgent.%运用内部专家法、关键指标法和N级风险分类法，初步构建了人身保险欺诈识别指标体系和人身保险欺诈的预警模型。保险欺诈已经严重危害到保险行业的健康发展，近年来人身保险欺诈越来越严重，因此建立人身保险欺诈的识别指标体系和预警模型显得尤其迫切。
Non-linear system identification in flow-induced vibration
Energy Technology Data Exchange (ETDEWEB)
Spanos, P.D.; Zeldin, B.A. [Rice Univ., Houston, TX (United States); Lu, R. [Hudson Engineering Corp., Houston, TX (United States)
1996-12-31
The paper introduces a method of identification of non-linear systems encountered in marine engineering applications. The non-linearity is accounted for by a combination of linear subsystems and known zero-memory non-linear transformations; an equivalent linear multi-input-single-output (MISO) system is developed for the identification problem. The unknown transfer functions of the MISO system are identified by assembling a system of linear equations in the frequency domain. This system is solved by performing the Cholesky decomposition of a related matrix. It is shown that the proposed identification method can be interpreted as a {open_quotes}Gram-Schmidt{close_quotes} type of orthogonal decomposition of the input-output quantities of the equivalent MISO system. A numerical example involving the identification of unknown parameters of flow (ocean wave) induced forces on offshore structures elucidates the applicability of the proposed method.
Energy Technology Data Exchange (ETDEWEB)
Hadjiloucas, S; Walker, G C; Bowen, J W [Cybernetics, School of Systems Engineering, The University of Reading, RG6 6AY (United Kingdom); Galvao, R K H, E-mail: s.hadjiloucas@reading.ac.uk [Divisao de Engenharia Eletronica, Instituto Tecnologico de Aeronautica, Sao Jose dos Campos, SP, 12228-900 Brazil (Brazil)
2011-08-12
We discuss the modelling of dielectric responses for an electromagnetically excited network of capacitors and resistors using a systems identification framework. Standard models that assume integral order dynamics are augmented to incorporate fractional order dynamics. This enables us to relate more faithfully the modelled responses to those reported in the Dielectrics literature.
Institute of Scientific and Technical Information of China (English)
李汝宁; 何勇灵
2012-01-01
The changing of pressure in the diesel injection system results in the growth and collapse of gas bubbles,which affects the diesel flowing and pressure.In order to predict the initial void fraction and changes in the diesel injection system,the method using improved genetic algorithms in parameter identification for the model of diesel injection system was presented,including the initial void fraction of plunger chamber,delivery valve chamber,high-pressure oil pipe and needle valve chamber.The model of diesel injection system under the condition of gas-liquid was established based on the gas model,fitness function was built by comparing with the simulation results and the experimental data,so the parameter optimization of the model of diesel injection system was realized,and the model of diesel injection system of parameter identification was carried out.Comparisons between simulation results and experimental data show that the improved genetic algorithms are capable of estimating unknown parameters in the model of diesel injection system.%柴油机喷油系统中压力的变化会引起气泡的产生和溃灭,而气泡的变化会对燃油的流动和压力波的传播产生影响.为了更准确预测喷油系统中的初始含气率以及变化情况,提出了应用改进遗传算法对柴油机喷油系统模型中的柱塞腔、出油阀紧帽腔、高压油管和针阀腔初始含气率进行辨识的新方法.基于气泡模型,建立了气-液两相条件下的柴油机喷油系统模型,通过仿真数据和试验数据对比,构造了适应度函数,实现了对柴油机喷油系统模型的参数辨识,并得到了参数优化后的柴油机喷油系统模型.仿真结果与试验数据的比较验证了采用改进遗传算法对气-液两相条件下的柴油机喷油系统模型进行参数辨识的可行性.
A model study for tardigrade identification
Bertolani, Roberto; Lorena REBECCHI; Cesari, Michele
2010-01-01
Using tardigrades from a single moss sample as a case study, we propose a new method for tardigrade species identification, which is often problematic, due to the low number of morphological characters. Identification at generic level was carried out on adults, while morphological analyses were performed on animals (LM) and eggs (LM and SEM), including hologenophores, vouchers used also for molecular analysis of COI mtDNA. This multi-approach method revealed the presence of ...
Target identification and navigation performance modeling of a passive millimeter wave imager.
Jacobs, Eddie L; Furxhi, Orges
2010-07-01
Human task performance using a passive interferometric millimeter wave imaging sensor is modeled using a task performance modeling approach developed by the U.S. Army Night Vision and Electronic Sensors Directorate. The techniques used are illustrated for an imaging system composed of an interferometric antenna array, optical upconversion, and image formation using a shortwave infrared focal plane array. Two tasks, target identification and pilotage, are modeled. The effects of sparse antenna arrays on task performance are considered. Applications of this model include system trade studies for concealed weapon identification, navigation in fog, and brownout conditions.
Energy Technology Data Exchange (ETDEWEB)
Jalashgar, A.
1997-05-01
The main subject of this thesis is to identify hidden failures in process control systems by developing and using a function-oriented system analysis method. Qualitative failure analysis and the characteristics of the classical failure analysis methods and function-oriented modelling methods are covered. The general limitations of the methods in connection with the identification and representation of hidden failures are discussed. The discussion has led to the justification of developing and using a function-oriented system analysis method to identify and represent the capabilities of the system components, which realize different sets of functions in connection with different sets of goals that the system must achieve. A terminology is introduced to define the basic aspects of technical systems including goals, functions, capabilities and physical structure. A function-oriented system analysis method using this terminology and a tailored combination of the two function-oriented modelling approaches, is also introduced. It is then explained how the method can be applied in the identification and representation of hidden failures. The building blocks of a knowledge-oriented system to perform the diagnosis on the basis of the developed method are equally described. A prototype of the knowledge-based system is developed to demonstrate the applicability of the function-oriented system analysis method and the knowledge-based system. The prototype is implemented within the object-oriented software environment G2. (au) 65 ills., 32 refs.
A Decision Support System for Identification and Evaluation of High-tech Products
Institute of Scientific and Technical Information of China (English)
GAO Chang-yuan; LIANG Jing-guo; CAO Xiu-ying
2002-01-01
According to high-tech product features and decision support system theory, a decision support system (DSS) for identification and evaluation of high-tech products has been designed which consists of the user interface subsystem, the data management subsystem, the model management subsystem and the knowledge management subsystem. This paper describes the function and the framework of the system.
A constraint-based search algorithm for parameter identification of environmental models
Gharari, S.; Shafiei, M.; Hrachowitz, M.; Kumar, R.; Fenicia, F.; Gupta, H.V.; Savenije, H.H.G.
2014-01-01
Many environmental systems models, such as conceptual rainfall-runoff models, rely on model calibration for parameter identification. For this, an observed output time series (such as runoff) is needed, but frequently not available (e.g., when making predictions in ungauged basins). In this study, w
Reliability of system identification technique in super high-rise building
Directory of Open Access Journals (Sweden)
Ayumi eIkeda
2015-07-01
Full Text Available A smart physical-parameter based system identification method has been proposed in the previous paper. This method deals with time-variant nonparametric identification of natural frequencies and modal damping ratios using ARX (Auto-Regressive eXogenous models and has been applied to high-rise buildings during the 2011 off the Pacific coast of Tohoku earthquake. In this perspective article, the current state of knowledge in this class of system identification methods is explained briefly and the reliability of this smart method is discussed through the comparison with the result by a more confident technique.
Autonomous system for pathogen detection and identification
Energy Technology Data Exchange (ETDEWEB)
Belgrader, P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Benett, W. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Bergman, W. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Langlois, R. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Mariella, R. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Milanovich, F. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Miles, R. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Venkateswaran, K. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Long, G. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Nelson, W. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
1998-09-24
This purpose of this project is to build a prototype instrument that will, running unattended, detect, identify, and quantify BW agents. In order to accomplish this, we have chosen to start with the world' s leading, proven, assays for pathogens: surface-molecular recognition assays, such as antibody-based assays, implemented on a high-performance, identification (ID)-capable flow cytometer, and the polymerase chain reaction (PCR) for nucleic-acid based assays. With these assays, we must integrate the capability to: l collect samples from aerosols, water, or surfaces; l perform sample preparation prior to the assays; l incubate the prepared samples, if necessary, for a period of time; l transport the prepared, incubated samples to the assays; l perform the assays; l interpret and report the results of the assays. Issues such as reliability, sensitivity and accuracy, quantity of consumables, maintenance schedule, etc. must be addressed satisfactorily to the end user. The highest possible sensitivity and specificity of the assay must be combined with no false alarms. Today, we have assays that can, in under 30 minutes, detect and identify simulants for BW agents at concentrations of a few hundred colony-forming units per ml of solution. If the bio-aerosol sampler of this system collects 1000 Ymin and concentrates the respirable particles into 1 ml of solution with 70% processing efficiency over a period of 5 minutes, then this translates to a detection/ID capability of under 0.1 agent-containing particle/liter of air.
System Identification of MEMS Vibratory Gyroscope Sensor
Juntao Fei; Yuzheng Yang
2011-01-01
Fabrication defects and perturbations affect the behavior of a vibratory MEMS gyroscope sensor, which makes it difficult to measure the rotation angular rate. This paper presents a novel adaptive approach that can identify, in an online fashion, angular rate and other system parameters. The proposed approach develops an online identifier scheme, by rewriting the dynamic model of MEMS gyroscope sensor, that can update the estimator of angular rate adaptively and converge to its true value asy...
Analysis of Offshore Knuckle Boom Crane - Part One: Modeling and Parameter Identification
Directory of Open Access Journals (Sweden)
Morten K. Bak
2013-10-01
Full Text Available This paper presents an extensive model of a knuckle boom crane used for pipe handling on offshore drilling rigs. The mechanical system is modeled as a multi-body system and includes the structural flexibility and damping. The motion control system model includes the main components of the crane's electro-hydraulic actuation system. For this a novel black-box model for counterbalance valves is presented, which uses two different pressure ratios to compute the flow through the valve. Experimental data and parameter identification, based on both numerical optimization and manual tuning, are used to verify the crane model. The demonstrated modeling and parameter identification techniques target the system engineer and takes into account the limited access to component data normally encountered by engineers working with design of hydraulic systems.
Identification of systems containing nonlinear stiffnesses using backbone curves
Londoño, Julián M.; Cooper, Jonathan E.; Neild, Simon A.
2017-02-01
This paper presents a method for the dynamic identification of structures containing discrete nonlinear stiffnesses. The approach requires the structure to be excited at a single resonant frequency, enabling measurements to be made in regimes of large displacements where nonlinearities are more likely to be significant. Measured resonant decay data is used to estimate the system backbone curves. Linear natural frequencies and nonlinear parameters are identified using these backbone curves assuming a form for the nonlinear behaviour. Numerical and experimental examples, inspired by an aerospace industry test case study, are considered to illustrate how the method can be applied. Results from these models demonstrate that the method can successfully deliver nonlinear models able to predict the response of the test structure nonlinear dynamics.
MINLIP for the Identification of Monotone Wiener Systems
Pelckmans, Kristiaan
2010-01-01
This paper studies the MINLIP estimator for the identification of Wiener systems consisting of a sequence of a linear FIR dynamical model, and a monotonically increasing (or decreasing) static function. Given $T$ observations, this algorithm boils down to solving a convex quadratic program with $O(T)$ variables and inequality constraints, implementing an inference technique which is based entirely on model complexity control. The resulting estimates of the linear submodel are found to be almost consistent when no noise is present in the data, under a condition of smoothness of the true nonlinearity and local Persistency of Excitation (local PE) of the data. This result is novel as it does not rely on classical tools as a 'linearization' using a Taylor decomposition, nor exploits stochastic properties of the data. It is indicated how to extend the method to cope with noisy data, and empirical evidence contrasts performance of the estimator against other recently proposed techniques.
Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems
Patan, Maciej
2012-01-01
Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...
A Markov Chain Monte Carlo Based Method for System Identification
Energy Technology Data Exchange (ETDEWEB)
Glaser, R E; Lee, C L; Nitao, J J; Hanley, W G
2002-10-22
This paper describes a novel methodology for the identification of mechanical systems and structures from vibration response measurements. It combines prior information, observational data and predictive finite element models to produce configurations and system parameter values that are most consistent with the available data and model. Bayesian inference and a Metropolis simulation algorithm form the basis for this approach. The resulting process enables the estimation of distributions of both individual parameters and system-wide states. Attractive features of this approach include its ability to: (1) provide quantitative measures of the uncertainty of a generated estimate; (2) function effectively when exposed to degraded conditions including: noisy data, incomplete data sets and model misspecification; (3) allow alternative estimates to be produced and compared, and (4) incrementally update initial estimates and analysis as more data becomes available. A series of test cases based on a simple fixed-free cantilever beam is presented. These results demonstrate that the algorithm is able to identify the system, based on the stiffness matrix, given applied force and resultant nodal displacements. Moreover, it effectively identifies locations on the beam where damage (represented by a change in elastic modulus) was specified.
Cellier, Francois E.
1991-01-01
A comprehensive and systematic introduction is presented for the concepts associated with 'modeling', involving the transition from a physical system down to an abstract description of that system in the form of a set of differential and/or difference equations, and basing its treatment of modeling on the mathematics of dynamical systems. Attention is given to the principles of passive electrical circuit modeling, planar mechanical systems modeling, hierarchical modular modeling of continuous systems, and bond-graph modeling. Also discussed are modeling in equilibrium thermodynamics, population dynamics, and system dynamics, inductive reasoning, artificial neural networks, and automated model synthesis.
Modelling and control of dynamic systems using gaussian process models
Kocijan, Juš
2016-01-01
This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior know...
Device identification of mechanical properties of electromagnetic systems
Directory of Open Access Journals (Sweden)
Ю.Т. Гуз
2008-01-01
Full Text Available It’s developed a method that structural the scheme of the identification device the mechanical characteristic of the relay of a direct current with no saturated magnetic system.
Systematic identification of crystallization kinetics within a generic modelling framework
DEFF Research Database (Denmark)
Abdul Samad, Noor Asma Fazli Bin; Meisler, Kresten Troelstrup; Gernaey, Krist
2012-01-01
A systematic development of constitutive models within a generic modelling framework has been developed for use in design, analysis and simulation of crystallization operations. The framework contains a tool for model identification connected with a generic crystallizer modelling tool-box, a tool...
Chang, Seongmin; Baek, Sungmin; Kim, Ki-Ook; Cho, Maenghyo
2015-06-01
A system identification method has been proposed to validate finite element models of complex structures using measured modal data. Finite element method is used for the system identification as well as the structural analysis. In perturbation methods, the perturbed system is expressed as a combination of the baseline structure and the related perturbations. The changes in dynamic responses are applied to determine the structural modifications so that the equilibrium may be satisfied in the perturbed system. In practical applications, the dynamic measurements are carried out on a limited number of accessible nodes and associated degrees of freedom. The equilibrium equation is, in principle, expressed in terms of the measured (master, primary) and unmeasured (slave, secondary) degrees of freedom. Only the specified degrees of freedom are included in the equation formulation for identification and the unspecified degrees of freedom are eliminated through the iterative improved reduction scheme. A large number of system parameters are included as the unknown variables in the system identification of large-scaled structures. The identification problem with large number of system parameters requires a large amount of computation time and resources. In the present study, a hierarchical clustering algorithm is applied to reduce the number of system parameters effectively. Numerical examples demonstrate that the proposed method greatly improves the accuracy and efficiency in the inverse problem of identification.
Institute of Scientific and Technical Information of China (English)
聂资; 陈铭; 李仁府
2012-01-01
Using perturbation approach, a linearized flight dynamics model was formulated for the non-linear coaxial helicopter flight control problem. Based on mathematical parametric model of the rotorcraft, the system identification model was obtained by utilizing the input and output data of longitudinal and lateral channel in flight test. The analysis and validation of the above two models in the time domain was given by computer simulation. The stability derivatives, control derivatives and the system eigenvalues were computed for the stability analysis of the helicopter. Results show that the mathematical model can reflect the dynamic characteristics of the longitudinal and lateral channels, based on which the system identification model can be a mathematical model for autonomous flight control system design.%运用经典的叶素法和一阶谐波理论，建立了悬停状态下某小型共轴式直升机纵横向通道的理论计算模型；同时根据飞行试验中采集的直升机输入输出数据，以参数化理论模型为基础，运用系统辨识的方法得到了该机纵横向通道模型．通过计算机仿真，分别对理论计算模型和系统辨识模型进行了时域验证和分析，并比较了2种模型的稳定性导数、操纵导数及特征根，对该直升机的稳定性进行了分析．研究表明：所建立的小型共轴式直升机纵横向通道的理论计算模型能够反映该机悬停状态下纵横向通道的动态特性，以此为基础建立的系统辨识模型可以作为飞行控制系统纵横向通道控制的数学模型．
System identification with belief calculus; Systemidentifikation mittels Glaubenskalkuel
Energy Technology Data Exchange (ETDEWEB)
Duerrbaum, A.; Sommer, H. [Kassel Univ. (Germany). Fachgebiet Mess- und Regelungstechnik; Scherm, W. [Kassel Univ. (Germany). Inst. fuer Produktionstechnik und Logistik
2008-07-01
Belief theory provides a very powerful tool for system identification. In this paper the principal method is presented and compared with probability-theory based identification methods. The new method allows to transfer all user requests into optimisable parameters and makes a priori assumptions superfluous. The efficiency of the method is demonstrated in a practical application: the design of a systems which forecasts the damage of drill-bits. (orig.)
Segmental Dynamics of Forward Fall Arrests: System Identification Approach
Kim, Kyu-Jung; Ashton-Miller, James A.
2009-01-01
Background Fall-related injuries are multifaceted problems, necessitating thorough biodynamic simulation to identify critical biomechanical factors. Methods A 2-degree-of-freedom discrete impact model was constructed through system identification and validation processes using the experimental data to understand dynamic interactions of various biomechanical parameters in bimanual forward fall arrests. Findings The bimodal reaction force response from the identified models had small identification errors for the first and second force peaks less than 3.5% and high coherence between the measured and identified model responses (R2=0.95). Model validation with separate experimental data also demonstrated excellent validation accuracy and coherence, less than 7% errors and R2=0.87, respectively. The first force peak was usually greater than the second force peak and strongly correlated with the impact velocity of the upper extremity, while the second force peak was associated with the impact velocity of the body. The impact velocity of the upper extremity relative to the body could be a major risk factor to fall-related injuries as observed from model simulations that a 75% faster arm movement relative to the falling speed of the body alone could double the first force peak from soft landing, thereby readily exceeding the fracture strength of the distal radius. Interpretation Considering that the time-critical nature of falling often calls for a fast arm movement, the use of the upper extremity in forward fall arrests is not biomechanically justified unless sufficient reaction time and coordinated protective motion of the upper extremity are available. PMID:19250726
Gervais, Brian; D'Arcy, Deirdre M
2014-01-01
Pharmaceutical quality systems use various inputs to ensure product quality and prevent failures that might have patient consequences. These inputs are generally data from failures that have already occurred, for example process deviations or customer complaints. Risk analysis techniques are well-established in certain other industries and have become of interest to pharmaceutical manufacturers because they allow potential quality failures to be predicted and mitigating action taken in advance of their occurring. Failure mode and effects analysis (FMEA) is one such technique, and in this study it was applied to implement a computerized manufacturing execution system in a pharmaceutical manufacturing environment. After introduction, the system was monitored to detect failures that did occur and these were analyzed to determine why the risk analysis method failed to predict them. Application of FMEA in other industries has identified weaknesses in predicting certain error types, specifically its dependence on other techniques to model risk situations and its poor analysis of non-hardware risks, such as human error, and this was confirmed in this study. Hierarchical holographic modeling (HHM), a technique for identifying risk scenarios in wide-scope analyses, was applied subsequently and identified additional potential failure modes. The technique for human error rate prediction (THERP) has previously been used for the quantitative analysis of human error risk and the event tree from this technique was adapted and identified further human error scenarios. These were input to the FMEA for prioritization and mitigation, thereby strengthening the risk analysis in terms of failure modes considered.
Online contact impedance identification for robotic systems
Haddadi, Amir; Hashtrudi-Zaad, Keyvan
2008-01-01
In this paper, we study the performance of various algorithms for fast online identification of environment impedance during robotic contact tasks. In particular, we evaluate and compare algorithms with regard to their convergence rate, computational complexity and sensitivity to noise for different