CDC2000 System Identification and Confidence Estimation

System Identification and Confidence Estimation
Home Full List of Titles 1: Proceedings of CDC2000 Discrete Event Systems Control in Communication Systems Optimal Control and Applications I Optimisation Approaches and Methods Model Predictive Control Advances in Linear Estimation Stochastic and Uncertain Systems Nonlinear Control and Applications Nonlinear Estimation and Filtering Formation Control and its Applications New Approaches to Fuzzy Control Manufacturing Systems Automotive Applications Stability Issues in Hybrid Control Recent Advances in Stochastic Networks Optimal Control and Applications II Robust Controller Design - mu, L1 and H2 Constrained and Receding Horizon Control Identification and Control around the World Markov Decision Processes Nonlinear Optimisation Observers for Nonlinear Systems Motion Planning Neural / Fuzzy Stability and Control Motor Control Control of Quantum Phenomena I Hybrid Systems Methods Control in Communication Networks Robustness and Optimisation Bumpless Transfer, Antiwindup and Saturation Adaptive Control: Linear Systems Estimation and Closed Loop Identification Control of Markov Processes Nonlinear Filtering and Control Modelling, Identification and Validation of Nonlinear Systems Differential Geometric Control Theory for Mechanical Systems Nonlinear Output Feedback Control Pneumatics and Compression Systems Control of Quantum Phenomena II Stability of Hybrid Systems Performance Analysis in Communication Networks Adaptive Control of Nonlinear Systems LMI Methods in Design Robust Control of Time Delay Systems Subspace Identification Methods Nonlinear Stochastic Filtering and Estimation Bifurcations, Chaos and Control I New Progress in Synthesis of Nonlinear Systems I Implementation Issues of Sliding Mode Control Theory Control of Mixing in Shear Flows Novel Neural Network Control Techniques for Industrial Motion Control Systems Physiological Control Systems Optimal Control of Hybrid Systems Stochastic Models for Communication Networks Control and Stabilisation of Nonlinear Systems New Directions in Robust Control Linear Systems Theory Advanced Topics in Systems Theory Estimation in Action Bifurcations, Chaos and Control II New Progress in Synthesis of Nonlinear Systems II Numerical Design and Analysis Techniques for Nonlinear Systems Analysis and Control of Underactuated Systems Sliding Mode Control I Challenges in the Application of Control to Computer Systems Estimation and Diagnosis of Discrete Event Systems Communications and Games Optimal Control Stochastic Systems Model Reduction Methodologies Identification and Subspace Methods Applications of Nonlinear Adaptive Control Advances in Nonlinear Output Feedback Design The Behavioural Approach to Systems and Control Vision Based Estimation and Control: Recent Advances and Open Problems Agile Control of Military Operations Sliding Mode Control II Model-based Fault Diagnosis of Industrial Processes Discrete Event Systems / Petri Nets System Identification and Confidence Estimation New Approaches to H-Infinity Control I Probabilistic Approaches to Robust Control Time Delay System Stabilisation Identification Methods Controlled Stochastic Processes Output Feedback of Nonlinear Systems Topics in Nonlinear Stabilisation Mobile Robots: Tracking Control Robust Control of Nonlinear Systems Power Systems Stabilisation and Control Disk Drive Control Hybrid Control Applications Discrete Time Systems New Approaches to H-Infinity Control II Linear Systems with Saturating Actuators New Theories in Distributed Parameter Systems Applications of Estimation and Identification Stochastic Control and Tuning Methodologies Control of Nonlinear Systems Iterative Learning and Control Coordinating Robot Systems Nonlinear Time Varying Systems Novel Applications of Neural Networks Aerospace Applications Switched Systems Implicit and Descriptor Systems LQG Periodic Systems and Disturbances New Horizons for Distributed Parameter Systems State Estimation Learning and Neuro-Control Nonlinear Control and Stabilisation I Tracking Vision Servoing Controllability of Nonlinear Systems Control of Flexible Systems Electro-Mechanical Systems Robust Control Methods and Applications Fault Detection and Diagnosis Optimisation and Applications Robust Stability Analysis Numerical Methods in Control Filtering in Continuous Time Stochastic Systems Interplay between Control and Signal Processing Fault Detection and Analysis Nonlinear Dynamical Systems Nonlinear Time Delay Systems Computational Issues in Nonlinear Control Disturbance Rejection Process Control Industry Applications Linear Parameter Varying Systems Linear Control Systems Dynamic and Nonlinear Programming Model Reduction Applications New Techniques for Control and Systems: Numerical Linear Algebra Estimation and Identification using Hidden Markov Models Applications of Stochastic Control Topics in Linear Design Nonlinear Control and Stabilisation II Ambulatory Robot Systems Chaotic and Oscillatory Systems Biomedical System Control Integrated Control and CPU Scheduling Linear Design Techniques Adaptive Disturbance / Noise Compensation Nonlinear Model Predictive Control Sensitivity Design, Analysis and Limitations Analysis of Linear Systems Linear Matrix Inequalities in Design Lyapunov's 2nd Method Robotics: Tracking Control Lagrangian and Hamiltonian Theory Variable Structure Control Machine Vision Signal Processing Methods in Control Applied Nonlinear Control Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	Non-Asymptotic Confidence Ellipsoids For The Least Squares Estimate Authors: Erik Weyer, Marco C. Campi, Volume: 1, Page 2688 Paper number 1382 Abstract: In this paper we consider the finite sample properties of least squares system identification, and we derive non-asymptotic confidence ellipsoids for the estimate. Unlike asymptotic theory, the obtained confidence ellipsoids are valid for a finite number of data points. The probability that the estimate belongs to a certain ellipsoid has a natural dependence on the volume of the ellipsoid, the data generating mechanism, the model order and the number of data points available. CD001382.PDF (From Author) TOP Uncertainty, Information and Complexity in Identification and Control Authors: Le Yi Wang, Volume: 1, Page 2694 Paper number 1509 Abstract: The relationship between information acquisition (identification) and information processing (control) in their capability of dealing with uncertainty is studied. It is revealed that such a relationship can be established rigorously from the viewpoint of complexity. A notion of information-based complexity is hence introduced, first in its generality, and then in its special applications to metric spaces in feedback control systems. CD001509.PDF (From Author) TOP Generalized LQG Design by Filter and Controller Model Selection Authors: Thomas E. Brehm, Peter S. Maybeck, Volume: 1, Page 2700 Paper number 1090 Abstract: This paper investigates a generalization of the conventional approach to LQG control design. First we investigate removing the assumption that the Kalman filter as the observer is necessarily based on the same model as the best plant model. The controller gain matrix design is performed as usual, based on the optimal solution to the deterministic design for the best model of the real-world plant. For the next case, we also remove this controller design restriction to investigate robustness to uncertainties in the plant model. The filter and controller gain matrices are both determined by models possibly other than the plant model. We relate the plant model to the filter and controller design models by a position correlation (mean square error on output) measure in order to determine optimal performance. CD001090.PDF (From Author) TOP Windowed Periodograms And Moving Average Models Authors: Piet M.T. Broersen, Stijn de Waele, Volume: 1, Page 2706 Paper number 1425 Abstract: A windowed and tapered periodogram can be computed as the Fourier transform of an estimated covariance function of tapered data, multiplied by a lag window. Covariances of finite length can also be modeled as moving average (MA) time series models. The direct equivalence between periodograms and MA models is shown in the method of moments for MA estimation. A better MA representation for the covariance and the spectral density is found with Durbin's improved MA method. That uses the parameters of a long autoregressive (AR) model to find MA models, followed by automatic selection of the MA order. A comparison is made between the two MA model types. The best of many MA models from windowed periodograms is compared to the single selected MA model obtained with Durbin's method. The latter typically has a better quality. CD001425.PDF (From Author) TOP Unbiased Identification of Stochastic Linear Systems from Noisy Input and Output Measurements Authors: Wei Xing Zheng, Volume: 1, Page 2710 Paper number 1238 Abstract: This paper is concerned with identification of stochastic linear systems from noisy input and output measurements. A modified scheme that employs extra delayed noisy measurements is derived to estimate the variances of white input and output noises. These estimated noise variances are then applied for removal of the bias from a least-squares parameter estimate via an iterative procedure to achieve estimation consistency. The new identification algorithm incorporated with this modified estimation scheme for the noise variances demonstrates greatly improved performances. Compared with the previously developed method, the new identification algorithm can converge at a much faster rate and produce much more accurate parameter estimates at only a slightly increased numerical cost. The theoretical predictions are confirmed through Monte-Carlo stochastic simulation studies. CD001238.PDF (From Author) TOP On Model Quality Evaluation of Stable LTI Systems Authors: Soosan Beheshti, Munther A. Dahleh, Volume: 1, Page 2716 Paper number 1790 Abstract: The problem of quantifying the error in estimation of low-complexity models for stable linear time-invariant(LTI) systems is investigated. We elaborate on the advantages of implementing a new method for order selection of the model class. CD001790.PDF (From Author) TOP