			Home About us Contact

Identification Approach (identification + approach)

Distribution by Scientific Domains

Engineering	63%

Selected Abstracts

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]

Optimal and self-tuning fusion Kalman filters for discrete-time stochastic singular systems

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2008
Shu-Li Sun
Abstract Based on the optimal fusion estimation algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion Kalman filter weighted by scalars is presented for discrete-time stochastic singular systems with multiple sensors and correlated noises. A cross-covariance matrix of filtering errors between any two sensors is derived. When the noise statistical information is unknown, a distributed identification approach is presented based on correlation functions and the weighted average method. Further, a distributed self-tuning fusion filter is given, which includes two stage fusions where the first-stage fusion is used to identify the noise covariance and the second-stage fusion is used to obtain the fusion state filter. A simulation verifies the effectiveness of the proposed algorithm. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Fault diagnosis of a simulated industrial gas turbine via identification approach

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2007
S. Simani
Abstract In this paper, a model-based procedure exploiting the analytical redundancy principle for the detection and isolation of faults on a simulated process is presented. The main point of the work consists of using an identification scheme in connection with dynamic observer and Kalman filter designs for diagnostic purpose. The errors-in-variables identification technique and output estimation approach for residual generation are in particular advantageous in terms of solution complexity and performance achievement. The proposed tools are analysed and tested on a single-shaft industrial gas turbine MATLAB/SIMULINK® simulator in the presence of disturbances, i.e. measurement errors and modelling mismatch. Selected performance criteria are used together with Monte-Carlo simulations for robustness and performance evaluation. The suggested technique can constitute the design methodology realising a reliable approach for real application of industrial process FDI. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Reduction and identification methods for Markovian control systems, with application to thin film deposition

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 2 2004
Martha A. Gallivan
Abstract Dynamic models of nanometer-scale phenomena often require an explicit consideration of interactions among a large number of atoms or molecules. The corresponding mathematical representation may thus be high dimensional, nonlinear, and stochastic, incompatible with tools in nonlinear control theory that are designed for low-dimensional deterministic equations. We consider here a general class of probabilistic systems that are linear in the state, but whose input enters as a function multiplying the state vector. Model reduction is accomplished by grouping probabilities that evolve together, and truncating states that are unlikely to be accessed. An error bound for this reduction is also derived. A system identification approach that exploits the inherent linearity is then developed, which generates all coefficients in either a full or reduced model. These concepts are then extended to extremely high-dimensional systems, in which kinetic Monte Carlo (KMC) simulations provide the input,output data. This work was motivated by our interest in thin film deposition. We demonstrate the approaches developed in the paper on a KMC simulation of surface evolution during film growth, and use the reduced model to compute optimal temperature profiles that minimize surface roughness. Copyright © 2004 John Wiley & Sons, Ltd. [source]

A new framework for data reconciliation and measurement bias identification in generalized linear dynamic systems

AICHE JOURNAL, Issue 7 2010
Hua Xu
Abstract This article describes a new framework for data reconciliation in generalized linear dynamic systems, in which the well-known Kalman filter (KF) is inadequate for filtering. In contrast to the classical formulation, the proposed framework is in a more concise form but still remains the same filtering accuracy. This comes from the properties of linear dynamic systems and the features of the linear equality constrained least squares solution. Meanwhile, the statistical properties of the framework offer new potentials for dynamic measurement bias detection and identification techniques. On the basis of this new framework, a filtering formula is rederived directly and the generalized likelihood ratio method is modified for generalized linear dynamic systems. Simulation studies of a material network present the effects of both the techniques and emphatically demonstrate the characteristics of the identification approach. Moreover, the new framework provides some insights about the connections between linear dynamic data reconciliation, linear steady state data reconciliation, and KF. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [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]

Nonlinear kinetic parameter estimation for batch cooling seeded crystallization

AICHE JOURNAL, Issue 8 2004
Q. Hu
Abstract Kinetic parameter estimation for most batch crystallization processes is necessary because nucleation and crystal growth kinetic parameters are often not available. The existing identification methods are generally based on simplified population balance models such as moment equations, which contain insufficient information on the crystal size distribution (CSD). To deal with these problems, a new optimization-based identification approach for general batch cooling seeded crystallization is proposed in this study. The final-time CSD is directly used for identification. A novel effective method for solving the population balance equation is developed and used to identify nucleation and growth kinetic parameters. Cooling crystallization of ammonium sulfate in water was experimentally investigated, where the concentration was measured by an on-line density meter and the final-time CSD was analyzed by a Malvern Mastersizer 2000. Kinetics for ammonium sulfate are determined based on cooling crystallization experiments. Applying these kinetics in simulation provides a good prediction of the product CSD. © 2004 American Institute of Chemical Engineers AIChE J, 50: 1786,1794, 2004 [source]

Diversity of sulfate-reducing bacteria from an extreme hypersaline sediment, Great Salt Lake (Utah)

FEMS MICROBIOLOGY ECOLOGY, Issue 2 2007
Kasper Urup Kjeldsen
Abstract The diversity of sulfate-reducing bacteria (SRB) inhabiting the extreme hypersaline sediment (270 g L,1 NaCl) of the northern arm of Great Salt Lake was studied by integrating cultivation and genotypic identification approaches involving PCR-based retrieval of 16S rRNA and dsrAB genes, the latter encoding major subunits of dissimilatory (bi) sulfite reductase. The majority (85%) of dsrAB sequences retrieved directly from the sediment formed a lineage of high (micro) diversity affiliated with the genus Desulfohalobium, while others represented novel lineages within the families Desulfohalobiaceae and Desulfobacteraceae or among Gram-positive SRB. Using the same sediment, SRB enrichment cultures were established in parallel at 100 and at 190 g L,1 NaCl using different electron donors. After 5,6 transfers, dsrAB and 16S rRNA gene-based profiling of these enrichment cultures recovered a SRB community composition congruent with the cultivation-independent profiling of the sediment. Pure culture representatives of the predominant Desulfohalobium -related lineage and of one of the Desulfobacteraceae -affilated lineages were successfully obtained. The growth performance of these isolates and of the enrichment cultures suggests that the sediment SRB community of the northern arm of Great Salt Lake consists of moderate halophiles, which are salt-stressed at the in situ salinity of 27%. [source]

Identification of evolutionary sequential systems,part 1: unified approach

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, Issue 9 2001
Claude Baron
Abstract Logical identification covers a wide range of applications dealing with constrained transformation processes between internal and external models of sequential systems. In this paper, we consider the differential identification approach whose purpose is to measure the influence of minor modifications of the internal or external models of an existing system. This class of identification is dedicated to sensitivity analysis: learning, redesign, diagnosis, etc. Thus, it reveals all its interest for the study of systems which have to adapt themselves to an evolving environment. This paper presents an overall view of the different differential identification approaches and their corresponding applications. We will propose a new resolution technique based on genetic simulation. In a second paper, we will focus on some experiments performed with a genetic identification tool. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Online identification of nonlinear multivariable processes using self-generating RBF neural networks

ASIAN JOURNAL OF CONTROL, Issue 5 2010
Karim Salahshoor
Abstract This paper addresses the problem of online model identification for multivariable processes with nonlinear and time-varying dynamic characteristics. For this purpose, two online multivariable identification approaches with self-organizing neural network model structures will be presented. The two adaptive radial basis function (RBF) neural networks are called as the growing and pruning radial basis function (GAP-RBF) and minimal resource allocation network (MRAN). The resulting identification algorithms start without a predefined model structure and the dynamic model is generated autonomously using the sequential input-output data pairs in real-time applications. The extended Kalman filter (EKF) learning algorithm has been extended for both of the adaptive RBF-based neural network approaches to estimate the free parameters of the identified multivariable model. The unscented Kalman filter (UKF) has been proposed as an alternative learning algorithm to enhance the accuracy and robustness of nonlinear multivariable processes in both the GAP-RBF and MRAN based approaches. In addition, this paper intends to study comparatively the general applicability of the particle filter (PF)-based approaches for the case of non-Gaussian noisy environments. For this purpose, the Unscented Particle Filter (UPF) is employed to be used as alternative to the EKF and UKF for online parameter estimation of self-generating RBF neural networks. The performance of the proposed online identification approaches is evaluated on a highly nonlinear time-varying multivariable non-isothermal continuous stirred tank reactor (CSTR) benchmark problem. Simulation results demonstrate the good performances of all identification approaches, especially the GAP-RBF approach incorporated with the UKF and UPF learning algorithms. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]