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System Identification (system + identification)

Distribution by Scientific Domains

Engineering	71%
5 Other Domains	29%

Selected Abstracts

A MAS IMPLEMENTATION FOR SYSTEM IDENTIFICATION AND PROCESS CONTROL

ASIAN JOURNAL OF CONTROL, Issue 4 2006
E. J. González
ABSTRACT In this paper, a MAS for system identification and process control is presented. In particular, this MAS implements a self-tuning regulator (STR) scheme. It has adopted FIPA specifications because they have become a stronger standard in MAS development and they involve not only agent language specifications but also agent management and conversations. In this work, an Ontology Agent (OA) is included, using DAML + OIL as ontology language. The obtained results validate this approach in the implementation of well-known algorithms for control process. [source]

Wavelet Transforms for System Identification in Civil Engineering

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2003
T. Kijewski
Although challenges did not surface in prior applications concerned with mechanical systems, which are characterized by higher frequency and broader-band signals, the transition to the time-frequency domain for the analysis of civil engineering structures highlighted the need to understand more fully various processing concerns, particularly for the popular Morlet wavelet. In particular, as these systems may possess longer period motions and thus require finer frequency resolutions, the particular impacts of end effects become increasingly apparent. This study discusses these considerations in the context of the wavelet's multi-resolution character and includes guidelines for selection of wavelet central frequencies, highlights their role in complete modal separation, and quantifies their contributions to end-effect errors, which may be minimized through a simple padding scheme. [source]

System identification applied to long-span cable-supported bridges using seismic records

EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 3 2008
Dionysius M. Siringoringo
Abstract This paper presents the application of system identification (SI) to long-span cable-supported bridges using seismic records. The SI method is based on the System Realization using Information Matrix (SRIM) that utilizes correlations between base motions and bridge accelerations to identify coefficient matrices of a state-space model. Numerical simulations using a benchmark cable-stayed bridge demonstrate the advantages of this method in dealing with multiple-input multiple-output (MIMO) data from relatively short seismic records. Important issues related to the effects of sensor arrangement, measurement noise, input inclusion, and the types of input with respect to identification results are also investigated. The method is applied to identify modal parameters of the Yokohama Bay Bridge, Rainbow Bridge, and Tsurumi Fairway Bridge using the records from the 2004 Chuetsu-Niigata earthquake. Comparison of modal parameters with the results of ambient vibration tests, forced vibration tests, and analytical models are presented together with discussions regarding the effects of earthquake excitation amplitude on global and local structural modes. Copyright © 2007 John Wiley & Sons, Ltd. [source]

System identification of linear structures based on Hilbert,Huang spectral analysis.

EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 10 2003
Part 2: Complex modes
Abstract A method, based on the Hilbert,Huang spectral analysis, has been proposed by the authors to identify linear structures in which normal modes exist (i.e., real eigenvalues and eigenvectors). Frequently, all the eigenvalues and eigenvectors of linear structures are complex. In this paper, the method is extended further to identify general linear structures with complex modes using the free vibration response data polluted by noise. Measured response signals are first decomposed into modal responses using the method of Empirical Mode Decomposition with intermittency criteria. Each modal response contains the contribution of a complex conjugate pair of modes with a unique frequency and a damping ratio. Then, each modal response is decomposed in the frequency,time domain to yield instantaneous phase angle and amplitude using the Hilbert transform. Based on a single measurement of the impulse response time history at one appropriate location, the complex eigenvalues of the linear structure can be identified using a simple analysis procedure. When the response time histories are measured at all locations, the proposed methodology is capable of identifying the complex mode shapes as well as the mass, damping and stiffness matrices of the structure. The effectiveness and accuracy of the method presented are illustrated through numerical simulations. It is demonstrated that dynamic characteristics of linear structures with complex modes can be identified effectively using the proposed method. Copyright © 2003 John Wiley & Sons, Ltd. [source]

System identification of instrumented bridge systems

EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 7 2003
Yalin Arici
Abstract Several recorded motions for seven bridge systems in California during recent earthquakes were analysed using parametric and non-parametric system identification (SI) methods. The bridges were selected considering the availability of an adequate array of accelerometers and accounting for different structural systems, materials, geometry and soil types. The results of the application of SI methods included identification of modal frequencies and damping ratios. Excellent fits of the recorded motion in the time domain were obtained using parametric methods. The multi-input/single-output SI method was a suitable approach considering the instrumentation layout for these bridges. Use of the constructed linear filters for prediction purposes was also demonstrated for three bridge systems. Reasonable prediction results were obtained considering the various limitations of the procedure. Finally, the study was concluded by identifying the change of the modal frequencies and damping of a particular bridge system in time using recursive filters. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Consistency of dynamic site response at Port Island

EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 6 2001
Laurie G. Baise
Abstract System identification (SI) methods are used to determine empirical Green's functions (EGF) for soil intervals at the Port Island Site in Kobe, Japan and in shake table model tests performed by the Port and Harbor Research Institute (PHRI) to emulate the site during the 17 January 1995 Hyogo-ken Nanbu earthquake. The model form for the EGFs is a parametric auto-regressive moving average (ARMA) model mapping the ground motions recorded at the base of a soil interval to the top of that interval, hence capturing the effect of the soil on the through-passing wave. The consistency of site response at Port Island before, during, and after the mainshock is examined by application of small motion foreshock EGFs to incoming ground motions over these time intervals. The prediction errors (or misfits) for the foreshocks, the mainshock, and the aftershocks, are assessed to determine the extent of altered soil response as a result of liquefaction of the ground during the mainshock. In addition, the consistency of soil response between field and model test is verified by application of EGFs calculated from the shake table test to the 17 January input data. The prediction error is then used to assess the consistency of behaviour between the two cases. By using EGFs developed for small-amplitude foreshock ground motions, ground motions were predicted for all intervals of the vertical array except those that liquefied with small error. Analysis of the post-liquefied ground conditions implies that the site response gradually returns to a pre-earthquake state. Site behaviour is found to be consistent between foreshocks and the mainshock for the native ground (below 16 m in the field) with a normalized mean square error (NMSE) of 0.080 and a peak ground acceleration (PGA) of 0.5g. When the soil actually liquefies (change of state), recursive models are needed to track the variable soil behaviour for the remainder of the shaking. The recursive models are shown to demonstrate consistency between the shake table tests and the field with a NMSE of 0.102 for the 16 m to surface interval that liquefied. The aftershock ground response was not modelled well with the foreshock EGF immediately after the mainshock (NMSE ranging from 0.37 to 0.92). One month after the mainshock, the prediction error from the foreshock modeled was back to the foreshock error level. Copyright © 2001 John Wiley Sons, Ltd. [source]

Dynamic Wavelet Neural Network for Nonlinear Identification of Highrise Buildings

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2005
Xiaomo Jiang
Compared with conventional neural networks, training of a dynamic neural network for system identification of large-scale structures is substantially more complicated and time consuming because both input and output of the network are not single valued but involve thousands of time steps. In this article, an adaptive Levenberg,Marquardt least-squares algorithm with a backtracking inexact linear search scheme is presented for training of the dynamic fuzzy WNN model. The approach avoids the second-order differentiation required in the Gauss,Newton algorithm and overcomes the numerical instabilities encountered in the steepest descent algorithm with improved learning convergence rate and high computational efficiency. The model is applied to two highrise moment-resisting building structures, taking into account their geometric nonlinearities. Validation results demonstrate that the new methodology provides an efficient and accurate tool for nonlinear system identification of high-rising buildings. [source]

System identification applied to long-span cable-supported bridges using seismic records

Algorithms for time synchronization of wireless structural monitoring sensors

EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 6 2005
Ying Lei
Abstract Dense networks of wireless structural health monitoring systems can effectively remove the disadvantages associated with current wire-based sparse sensing systems. However, recorded data sets may have relative time-delays due to interference in radio transmission or inherent internal sensor clock errors. For structural system identification and damage detection purposes, sensor data require that they are time synchronized. The need for time synchronization of sensor data is illustrated through a series of tests on asynchronous data sets. Results from the identification of structural modal parameters show that frequencies and damping ratios are not influenced by the asynchronous data; however, the error in identifying structural mode shapes can be significant. The results from these tests are summarized in Appendix A. The objective of this paper is to present algorithms for measurement data synchronization. Two algorithms are proposed for this purpose. The first algorithm is applicable when the input signal to a structure can be measured. The time-delay between an output measurement and the input is identified based on an ARX (auto-regressive model with exogenous input) model for the input,output pair recordings. The second algorithm can be used for a structure subject to ambient excitation, where the excitation cannot be measured. An ARMAV (auto-regressive moving average vector) model is constructed from two output signals and the time-delay between them is evaluated. The proposed algorithms are verified with simulation data and recorded seismic response data from multi-story buildings. The influence of noise on the time-delay estimates is also assessed. Copyright © 2004 John Wiley & Sons, Ltd. [source]

System identification of instrumented bridge systems

Structural damage detection using the optimal weights of the approximating artificial neural networks

EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 2 2002
Shih-Lin Hung
Abstract This work presents a novel neural network-based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Iterative adaptive robust control of multivariable CD processes

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 8 2010
Fazel Farahmand
Abstract In this paper we present a novel adaptive robust control approach to the multivariable cross-directional (CD) process of continuous web manufacturing. The common assumption of spatial frequency decomposition (SFD) is used to allow the process analysis in terms of a family of single-input single-output (SISO) transfer functions across the spatial frequencies. We then apply discretized Windsurfing adaptive robust control to each individual separated spatial frequency, starting with a stable initial model and a robust stabilizing controller at each spatial frequency. This approach allows the 2D bandwidth of the closed-loop system to be increased progressively at each spatial frequency through an iterative relevant system identification and control design procedure. The method deals with both model uncertainty and measurement noise issues. Simulation results are given to illustrate the performance of the applied method. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Hybrid kernel learning via genetic optimization for TS fuzzy system identification

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 1 2010
Wei Li
Abstract This paper presents a new TS fuzzy system identification approach based on hybrid kernel learning and an improved genetic algorithm (GA). Structure identification is achieved by using support vector regression (SVR), in which a hybrid kernel function is adopted to improve regression performance. For multiple-parameter selection of SVR, the proposed GA is adopted to speed up the search process and guarantee the least number of support vectors. As a result, a concise model structure can be determined by these obtained support vectors. Then, the premise parameters of fuzzy rules can be extracted from results of SVR, and the consequent parameters can be optimized by the least-square method. Simulation results show that the resulting fuzzy model not only achieves satisfactory accuracy, but also takes on good generalization capability. Copyright © 2008 John Wiley & Sons, Ltd. [source]

A statistical downscaling method for monthly total precipitation over Turkey

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 2 2004
Hasan Tatli
Abstract Researchers are aware of certain types of problems that arise when modelling interconnections between general circulation and regional processes, such as prediction of regional, local-scale climate variables from large-scale processes, e.g. by means of general circulation model (GCM) outputs. The problem solution is called downscaling. In this paper, a statistical downscaling approach to monthly total precipitation over Turkey, which is an integral part of system identification for analysis of local-scale climate variables, is investigated. Based on perfect prognosis, a new computationally effective working method is introduced by the proper predictors selected from the National Centers for Environmental Prediction,National Center for Atmospheric Research reanalysis data sets, which are simulated as perfectly as possible by GCMs during the period of 1961,98. The Sampson correlation ratio is used to determine the relationships between the monthly total precipitation series and the set of large-scale processes (namely 500 hPa geopotential heights, 700 hPa geopotential heights, sea-level pressures, 500 hPa vertical pressure velocities and 500,1000 hPa geopotential thicknesses). In the study, statistical preprocessing is implemented by independent component analysis rather than principal component analysis or principal factor analysis. The proposed downscaling method originates from a recurrent neural network model of Jordan that uses not only large-scale predictors, but also the previous states of the relevant local-scale variables. Finally, some possible improvements and suggestions for further study are mentioned. Copyright © 2004 Royal Meteorological Society [source]

Robust identification of piecewise/switching autoregressive exogenous process

AICHE JOURNAL, Issue 7 2010
Xing Jin
Abstract A robust identification approach for a class of switching processes named PWARX (piecewise autoregressive exogenous) processes is developed in this article. It is proposed that the identification problem can be formulated and solved within the EM (expectation-maximization) algorithm framework. However, unlike the regular EM algorithm in which the objective function of the maximization step is built upon the assumption that the noise comes from a single distribution, contaminated Gaussian distribution is utilized in the process of constructing the objective function, which effectively makes the revised EM algorithm robust to the latent outliers. Issues associated with the EM algorithm in the PWARX system identification such as sensitivity to its starting point as well as inability to accurately classify "un-decidable" data points are examined and a solution strategy is proposed. Data sets with/without outliers are both considered and the performance is compared between the robust EM algorithm and regular EM algorithm in terms of their parameter estimation performance. Finally, a modified version of MRLP (multi-category robust linear programming) region partition method is proposed by assigning different weights to different data points. In this way, negative influence caused by outliers could be minimized in region partitioning of PWARX systems. Simulation as well as application on a pilot-scale switched process control system are used to verify the efficiency of the proposed identification algorithm. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source]

Wavelet-based adaptive robust M-estimator for nonlinear system identification

AICHE JOURNAL, Issue 8 2000
D. Wang
A wavelet-based robust M-estimation method for the identification of nonlinear systems is proposed. Because it is not based on the assumption that there is the class of error distribution, it takes a flexible, nonparametric approach and has the advantage of directly estimating the error distribution from the data. This M-estimator is optimal over any error distribution in the sense of maximum likelihood estimation. A Monte-Carlo study on a nonlinear chemical engineering example was used to compare the results with various previously utilized methods. [source]

Design of a slim optical image stabilization actuator for mobile phone cameras

PHYSICA STATUS SOLIDI (C) - CURRENT TOPICS IN SOLID STATE PHYSICS, Issue 12 2007
Hsing-Cheng Yu
Abstract Mechanical optical image stabilization actuator (OISA) will quickly become a standard feature in a high resolution mobile phone camera (MPC) and the quantity of MPC will exceed that of digital still cameras in several years. Whenever jitter arises from shaky hands or environment in taking photos, optical images projected upon an image sensor blur. Designing a slim OISA in MPC is an effective solution that addresses image quality. Therefore, this work presents a slim OISA utilized in MPC to compensate jitter form camera shake in taking photos. Two proportional-integral-derivative controllers based on transfer functions for dual axes of the slim OISA system in MPC obtained from system identification have been designed. The settling time of dual axes are less than 0.03 sec. Furthermore, a thrust ball bearing in this study has reduced the friction force between the movable and the stationary parts, so as to minimize the driving current to be less than 5 mA. Hence, the slim OISA has satisfied low power consumption requirement, and is also possible to reduce dimension in the MPC application. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

Physiological Control of a Rotary Blood Pump With Selectable Therapeutic Options: Control of Pulsatility Gradient

ARTIFICIAL ORGANS, Issue 10 2008
Andreas Arndt
Abstract A control strategy for rotary blood pumps meeting different user-selectable control objectives is proposed: maximum support with the highest feasible flow rate versus medium support with maximum ventricular washout and controlled opening of the aortic valve (AoV). A pulsatility index (PI) is calculated from the pressure difference, which is deduced from the axial thrust measured by the magnetic bearing of the pump. The gradient of PI with respect to pump speed (GPI) is estimated via online system identification. The outer loop of a cascaded controller regulates GPI to a reference value satisfying the selected control objective. The inner loop controls the PI to a reference value set by the outer loop. Adverse pumping states such as suction and regurgitation can be detected on the basis of the GPI estimates and corrected by the controller. A lumped-parameter computer model of the assisted circulation was used to simulate variations of ventricular contractility, pulmonary venous pressure, and aortic pressure. The performance of the outer control loop was demonstrated by transitions between the two control modes. Fast reaction of the inner loop was tested by stepwise reduction of venous return. For maximum support, a low PI was maintained without inducing ventricular collapse. For maximum washout, the pump worked at a high PI in the transition region between the opening and the permanently closed AoV. The cascaded control of GPI and PI is able to meet different control objectives and is worth testing in vitro and in vivo. [source]

A MAS IMPLEMENTATION FOR SYSTEM IDENTIFICATION AND PROCESS CONTROL

A levenberg,marquardt learning applied for recurrent neural identification and control of a wastewater treatment bioprocess

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2009
Ieroham S. Baruch
The paper proposed a new recurrent neural network (RNN) model for systems identification and states estimation of nonlinear plants. The proposed RNN identifier is implemented in direct and indirect adaptive control schemes, incorporating a noise rejecting plant output filter and recurrent neural or linear-sliding mode controllers. For sake of comparison, the RNN model is learned both by the backpropagation and by the recursive Levenberg,Marquardt (L,M) learning algorithm. The estimated states and parameters of the RNN model are used for direct and indirect adaptive trajectory tracking control. The proposed direct and indirect schemes are applied for real-time control of wastewater treatment bioprocess, where a good, convergence, noise filtering, and low mean squared error of reference tracking is achieved for both learning algorithms, with priority of the L,M one. © 2009 Wiley Periodicals, Inc. [source]