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identification OF STOCHASTIC TIME-LAG SYSTEMS IN THE PRESENCE OF COLORED NOISE

AUTOMATICA 1990年第4期26卷 769-779页

作者： ZHENG, WX FENG, CB Institute of Automation Southeast University Nanjing 210018 People's Republic of China

This paper is concerned with identification of stochastic time lag systems corrupted by colored noise. Correlation analysis is used for time delay estimation. Some important extensions are made via time series analysis so that the time delay can be estimated accurately even when the input to the identified plant is a common stationary signal and is correlated with the process noise. By applying a new modified least-squares estimation method to eliminating the bias in the system parameters estimates arising from colored noise, an unbiased identification algorithm for jointly estimating the time delay and system parameters can be obtained based on correlation analysis. The proposed identification schemes may be used both in batches and recursively. Numerical examples are given as well.

关键词： correlation analysis identification algorithms parameter estimation system identification time delay estimation Time lag systems

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Determination of the glass fiber freezing point by time-delay estimation

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CONTROL ENGINEERING PRACTICE 1997年第7期5卷 943-950页

作者： Milinkovic, SA Faculty of Technology and Metallurgy University of Belgrade Karnegijeva 4 11000 Belgrade Minor Yugoslavia (emilinko @ ubbg.etf.bg.ac.yu)

The position of the glass fiber freezing point in the process of drawing it from a preform can be determined in a variety of ways, mostly by indirect methods, e.g., by temperature measurements. The cross-correlation method allows determination of the delay without stopping or disturbing the process itself. The transport delay is estimated, based on the moment of the abrupt change of the cross-correlation function absolute value, and is decoupled from the process structure parameters (coefficients of B(q(-1)) polynomial). Experimental results obtained in the open loop by application of both the PRBS and continuous random noise test signals are given. Also, using the same data, comparisons between the parametric and nonparametric methods are made. Copyright (C) 1997 Elsevier Science Ltd.

关键词： cross-correlation functions least squares estimation identification algorithms time-delay estimation

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Auxiliary signal design for rapid multi-model identification using optimization

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AUTOMATICA 2002年第8期38卷 1313-1325页

作者： Campbell, SL Horton, KG Nikoukhah, R N Carolina State Univ Dept Math Raleigh NC 27695 USA Inst Natl Rech Informat & Automat F-78153 Le Chesnay France

Under the assumption that one of several given models is the real underlying model of the system, a proper auxiliary signal is defined as an input signal that allows one to select the correct model. It is assumed that there is no knowledge prior to the beginning of the application of the auxiliary signal and that detection is to be done within a specified detection horizon. Under the assumption that the noise energy is bounded, a method for the computation of the minimal energy auxiliary signal is given. The new algorithm extends previous work in that it can handle more than two models and certain types of nonlinearities. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： linear systems identification algorithms molde-based recognition numerical algorithms

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A generalized numerical method for ferrite inductors analysis in high current ripple operation

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INTEGRATION-THE VLSI JOURNAL 2017年第Jun.期58卷 473-484页

作者： Di Capua, Giulia Femia, Nicola Stoyka, Kateryna Univ Salerno Dept Informat & Elect Engn & Appl Math DIEM I-84084 Fisciano SA Italy

Ferrite Core Power Inductors (FCPIs) operation in partial saturation offers unexplored opportunities in reducing the size of magnetic parts and the power losses in Switching Mode Power Supplies (SMPSs). This paper presents an enhanced numerical method to achieve a reliable prediction of the current ripple of FCPIs, also in partial saturation, for different conversion topologies and in whatever operating conditions. The proposed analysis includes High-Current Ripple (HCR) operations, for synchronous configurations in Continuous Conduction Mode (CCM) and for diode rectification configurations in Discontinues Conduction Mode (DCM). Relevant numerical algorithms for the reliable FCPIs current wave-shape prediction are given. Experimental verifications are performed on two boost converters in CCM and DCM to provide the validation of the proposed method.

关键词： Ferrite inductors Saturation High current ripple Continuous conduction mode Discontinuous conduction mode Numerical methods Differential evolution identification algorithms

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Benchmark problems for continuous-time model identification: Design aspects, results and perspectives

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AUTOMATICA 2019年 107卷 511-517页

作者： Pascu, Valentin Garnier, Hugues Ljung, Lennart Janot, Alexandre ONERA French Aerosp Lab Syst Control & Flight Dynam Dept F-31055 Toulouse France Univ Lorraine CRAN UMR 7039 F-54519 Vandoeuvre Les Nancy France CNRS CRAN UMR 7039 F-54506 Vandoeuvre Les Nancy France Linkoping Univ Div Automat Control SE-58183 Linkoping Sweden

The problem of estimating continuous-time model parameters of linear dynamical systems using sampled time-domain input and output data has received considerable attention over the past decades and has been approached by various methods. The research topic also bears practical importance due to both its close relation to first principles modelling and equally to linear model-based control design techniques, most of them carried in continuous time. Nonetheless, as the performance of the existing algorithms for continuous-time model identification has seldom been assessed and, as thus far, it has not been considered in a comprehensive study, this practical potential of existing methods remains highly questionable. The goal of this brief paper is to bring forward a first study on this issue and to factually highlight the main aspects of interest. As such, an analysis is performed on a benchmark designed to be consistent both from a system identification viewpoint and from a control-theoretic one. It is concluded that robust initialization aspects require further research focus towards reliable algorithm development. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： identification algorithms Output error identification Parameter identification Linear multivariable systems Benchmark examples Monte Carlo simulation

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Optic disc localization in retinal images using histogram matching

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EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2012年第1期2012卷 1-11页

作者： Dehghani, Amin Moghaddam, Hamid Abrishami Moin, Mohammad-Shahram KN Toosi Univ Technol Dept Elect Engn Tehran Iran Res Inst ICT Tehran Iran

In this article, we propose a new method for localizing optic disc in retinal images. Localizing the optic disc and its center is the first step of most vessel segmentation, disease diagnostic, and retinal recognition algorithms. We use optic disc of the first four retinal images in DRIVE dataset to extract the histograms of each color component. Then, we calculate the average of histograms for each color as template for localizing the center of optic disc. The DRIVE, STARE, and a local dataset including 273 retinal images are used to evaluate the proposed algorithm. The success rate was 100, 91.36, and 98.9%, respectively.

关键词： Optic disc' Retinal image identification algorithms Diabetes DRIVE and STARE dataset

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Greenhouse climate hierarchical fuzzy modelling

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CONTROL ENGINEERING PRACTICE 2005年第5期13卷 613-628页

作者： Salgado, P Cunha, JB Univ Tras Os Montes & Alto Douro Dep Engn CETAV P-5000911 Vila Real Portugal

Fuzzy modelling has been widely applied as a powerful methodology for the identification of nonlinear systems from process measurements. Most applications use flat sets of fuzzy rules, which are hardly interpretable black-box approaches. Hierarchical modelling is a promising tool to deal with real world complex systems. A large-scale model can be easily readable if it is partitioned into several independent smaller models to represent functional relations of the processes involved in the system. This article deals with the application of a new fuzzy modelling technique that automatically organizes the sets of fuzzy IF-THEN rules in a Hierarchical Collaborative Structure. This organizational structure makes the fuzzy model interpretable as in the case of the physical model. This new methodology was tested to split the inside greenhouse air temperature and humidity flat fuzzy models into fuzzy sub-models. which have alike counterpart on the physical sub-models. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： hierarchical systems fuzzy models identification algorithms computer simulation agriculture

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Simplified artificial neural network based online adaptive control scheme for nonlinear systems

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NEURAL COMPUTING & APPLICATIONS 2023年第1期35卷 663-679页

作者： Jamsheed, Faisal Iqbal, Sheikh Javed Natl Inst Technol Srinagar Dept Elect Engn Srinagar Jammu & Kashmir India

Complex neural network structures may not be appropriate for adaptive control applications, as large number of variable adaptive parameters have to be updated in each time step, thus increasing the computational burden, which makes the real-time implementation of the controller difficult. In this study, the ability of the neural networks to approximate non-linear dynamic systems is used to derive a neuro-based adaptive control with minimalistic architecture. A linear neural identifier is devised, which emulates a local linear model of the system by online adjustment of its parameters. Stability and optimal rate of convergence is ensured through an adaptive learning rate, determined using Lyapunov stability theorems. The novelty of the control scheme lies in its minimalistic neural structure comprising of a single-linear neuron and, therefore, does not impose excessive computational burden on the system, making it feasible for real-time application. To assert our claims, benchmark examples from different domains are used to illustrate the effectiveness of the proposed controller. The neuro-controller is used in a water-lift plant to control the height of the water in a storage tank. Another example of a moving cart holding an inverted pendulum, an inherently unstable system that forms the basis of the robot-control mechanism, is also used. The controller is also tested on a complex non-linear higher-order power system to enhance stability by effectively damping the electromechanical oscillations. The superior performance of the controller is demonstrated by comparing with other recently reported controllers. Additional advantages of the proposed scheme include model-free control and requirement of only local measurements. The proposed method has potential applications in control problems that require adaptability, computational simplicity, and quick response.

关键词： Adaptive control identification algorithms Neural network-based Online Control Inverted Pendulum Power system oscillations

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A Non-Parametric Approach for identification of Parameter Varying Hammerstein Systems

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IEEE ACCESS 2022年 10卷 6348-6362页

作者： Sobhani Tehrani, Ehsan Kearney, Robert E. McGill Univ Dept Biomed Engn Montreal PQ H3A 2B4 Canada iKinesia Inc Montreal PQ H3E 1L3 Canada

This paper presents a novel method for non-parametric identification of parameter-varying (PV) Hammerstein systems where the parameters of both the static nonlinearity and the linear dynamics change with a scheduling variable. The proposed method estimates the individual elements of the PV Hammerstein system by describing the static nonlinear element using a PV Chebychev basis expansion and the linear dynamic element as a non-parametric PV impulse response function with Laguerre basis expansion. The method was validated using Monte-Carlo simulations of a PV Hammerstein model of ankle reflex stiffness during large movements. Results demonstrated that the method is simple, effective, and robust;it accurately identified the PV Hammerstein system in the presence of relatively large colored, time-varying measurement noise (average SNR of 15dB). These results demonstrate the two main contributions of the method: (1) It accurately and precisely estimates both the linear and nonlinear elements of the PV Hammerstein cascade as they vary with a scheduling variable;and (2) Models identified with the method accurately predict the response of the PV Hammerstein system to novel scheduling variable trajectories. To our knowledge, no other Hammerstein method can achieve these.

关键词： Data models Biological system modeling TV Noise measurement Predictive models Nonlinear dynamical systems Analytical models Biomedical systems Hammerstein systems identification algorithms joint stiffness parameter-varying models

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Improving an adaptive controller for non-minimum phase plants

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AUTOMATICA 2002年第5期38卷 869-873页

作者： Yuan, J Hong Kong Polytech Univ Dept Mech Engn Kowloon Hong Kong Peoples R China

This study improves an adaptive controller for linear discrete-time plants in two aspects: (1) a new adaptation law is proposed to reduce the residual and the parameter errors;(2) a new method is proposed to modify the parameter estimates if the estimated model is not controllable. The second improvement is required when the plant is non-minimum phase. It obtains a controllable model from available parameter estimates while minimizing the modification offset. These features enhance closed-loop tracking performance of the adaptive controller when it is applied to non-minimum phase plants. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： adaptive control identification algorithms non-minimum phase systems

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