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John B. Moore

Bio: John B. Moore is an academic researcher from Australian National University. The author has contributed to research in topics: Adaptive control & Linear-quadratic-Gaussian control. The author has an hindex of 50, co-authored 352 publications receiving 18573 citations. Previous affiliations of John B. Moore include Akita University & University of Hong Kong.


Papers
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Proceedings ArticleDOI
01 Jul 1997
TL;DR: In this article, a modified Zakai equation is obtained for the risk sensitive information state and an expression for the optimizing risk-sensitive estimate is given for a class of continuous-time nonlinear stochastic signal models.
Abstract: Risk-sensitive filtering results are obtained for a class of continuous-time nonlinear stochastic signal models. A modified Zakai equation is obtained for the risk-sensitive information state and an expression for the optimizing risk-sensitive estimate is given. It is shown that if the drift function in the state space model satisfies a certain partial differential equation involving the risk-sensitive cost-kernel, finite-dimensional risk-sensitive information states and filters can be obtained for quite general nonlinear drift functions. Brief discussions on small noise limit results and possible extensions are also included.

2 citations

Journal ArticleDOI
TL;DR: The Popov criterion for the stability of linear, time-invariant, finite-dimensional systems with a single nonlinearity has been generalized by a number of authors through a relaxation of the single non-linearity and the finite dimension constraints in order to be applicable to a wider range of practical problems.
Abstract: The Popov criterion for the stability of linear, time-invariant, finite-dimensional systems with a single non-linearity has been generalized by a numbers of authors through a relaxation of the single non-linearity and the finite-dimensional constraints in order to be applicable to a wider range of practical problems. This paper extends these results further by relaxing the time-invariant constraint. It is shown that a covariance condition when satisfied is a sufficient condition for the system output to be bounded and square integrable for zero input conditions.

1 citations

Book ChapterDOI
TL;DR: In this article, modified algorithms which involve the introduction of noise into the calculations are proposed and studied by theory and simulations, which can be seen as an approach to adaptive estimation /control when there are jump parameter changes which include order changes.

1 citations

01 Feb 2002
TL;DR: In this paper, the authors propose finite-dimensional parameter estimators that are based on estimates of summed functions of the state, rather than of the states themselves, for fully and partially observed discrete-time linear stochastic systems with known noise characteristics.
Abstract: In this paper we discuss parameter estimators for fully and partially observed discrete-time linear stochastic systems (in state-space form) with known noise characteristics. We propose finite-dimensional parameter estimators that are based on estimates of summed functions of the state, rather than of the states themselves. We limit our investigation to estimation of the state transition matrix and the observation matrix. We establish almost-sure convergence results for our proposed parameter estimators using standard martingale convergence results, the Kronecker lemma and an ordinary differential equation approach. We also provide simulation studies which illustrate the performance of these estimators. Copyright © 2002 John Wiley & Sons, Ltd.

1 citations

Proceedings Article
01 Jan 1996
TL;DR: A new HMM soft-output decoder for QAM signals transmitted through narrow-band flat-fading channels, in the case where the signal constellation has points which are clustered into groups, which results in better robustness to noise.
Abstract: This paper presents a new HMM soft-output decoder for QAM signals transmitted through narrow-band flat-fading channels, in the case where the signal constellation has points which are clustered into groups. Parallel HMM filters are used for QAM detection, with one filter for each group of constellation points. This greatly reduces the number of computations required. A Kalman Filter (KF) is then applied for channel estimation. The resulting adaptive algorithms appear as coupled KF and HMM filters. As well as having reduced computations, this approach does not require so called ‘hard-decoding-decisions’ to be made, which result s in better robustness to noise.

1 citations


Cited by
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Book
01 Jan 1994
TL;DR: In this paper, the authors present a brief history of LMIs in control theory and discuss some of the standard problems involved in LMIs, such as linear matrix inequalities, linear differential inequalities, and matrix problems with analytic solutions.
Abstract: Preface 1. Introduction Overview A Brief History of LMIs in Control Theory Notes on the Style of the Book Origin of the Book 2. Some Standard Problems Involving LMIs. Linear Matrix Inequalities Some Standard Problems Ellipsoid Algorithm Interior-Point Methods Strict and Nonstrict LMIs Miscellaneous Results on Matrix Inequalities Some LMI Problems with Analytic Solutions 3. Some Matrix Problems. Minimizing Condition Number by Scaling Minimizing Condition Number of a Positive-Definite Matrix Minimizing Norm by Scaling Rescaling a Matrix Positive-Definite Matrix Completion Problems Quadratic Approximation of a Polytopic Norm Ellipsoidal Approximation 4. Linear Differential Inclusions. Differential Inclusions Some Specific LDIs Nonlinear System Analysis via LDIs 5. Analysis of LDIs: State Properties. Quadratic Stability Invariant Ellipsoids 6. Analysis of LDIs: Input/Output Properties. Input-to-State Properties State-to-Output Properties Input-to-Output Properties 7. State-Feedback Synthesis for LDIs. Static State-Feedback Controllers State Properties Input-to-State Properties State-to-Output Properties Input-to-Output Properties Observer-Based Controllers for Nonlinear Systems 8. Lure and Multiplier Methods. Analysis of Lure Systems Integral Quadratic Constraints Multipliers for Systems with Unknown Parameters 9. Systems with Multiplicative Noise. Analysis of Systems with Multiplicative Noise State-Feedback Synthesis 10. Miscellaneous Problems. Optimization over an Affine Family of Linear Systems Analysis of Systems with LTI Perturbations Positive Orthant Stabilizability Linear Systems with Delays Interpolation Problems The Inverse Problem of Optimal Control System Realization Problems Multi-Criterion LQG Nonconvex Multi-Criterion Quadratic Problems Notation List of Acronyms Bibliography Index.

11,085 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: A generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph, that computes-either exactly or approximately-various marginal functions derived from the global function.
Abstract: Algorithms that must deal with complicated global functions of many variables often exploit the manner in which the given functions factor as a product of "local" functions, each of which depends on a subset of the variables. Such a factorization can be visualized with a bipartite graph that we call a factor graph, In this tutorial paper, we present a generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph. Following a single, simple computational rule, the sum-product algorithm computes-either exactly or approximately-various marginal functions derived from the global function. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can be derived as specific instances of the sum-product algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's (1988) belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform (FFT) algorithms.

6,637 citations

BookDOI
01 Jan 2001
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Abstract: Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.

6,574 citations

MonographDOI
01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

6,340 citations