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39
Disentangling Uncertainty and Error: On the Predictability of Nonlinear Systems
 Nonlinear Dynamics and Statistics
, 2000
"... Chaos places no a priori restrictions on predictability: any uncertainty in the initial condition can be evolved and then quanti ed as a function of forecast time. If a speci ed accuracy at a given future time is desired, a perfect model can specify the initial accuracy required to obtain it, and ac ..."
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Cited by 34 (6 self)
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Chaos places no a priori restrictions on predictability: any uncertainty in the initial condition can be evolved and then quanti ed as a function of forecast time. If a speci ed accuracy at a given future time is desired, a perfect model can specify the initial accuracy required to obtain it, and accountable ensemble forecasts can be obtained for each unknown initial condition. Statistics which reect the global properties of in nitesimals, such as Lyapunov exponents which de ne \chaos", limit predictability only in the simplest mathematical examples. Model error, on the other hand, makes forecasting a dubious endeavor. Forecasting with uncertain initial conditions in the perfect model scenario is contrasted with the case where a perfect model is unavailable, perhaps nonexistent. Applications to both low (2 to 400) dimensional models and high (10 7 ) dimensional models are discussed. For real physical systems no perfect model exists; the limitations of nearperfect models are consider...
Extracting dynamical behaviour via Markov models
 PROCEEDINGS, NEWTON INSTITUTE
"... Statistical properties of chaotic dynamical systems are difficult to estimate reliably. Using long trajectories as data sets sometimes produces misleading results. It has been recognised for some time that statistical properties are often stable under the addition of a small amount of noise. Rath ..."
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Cited by 28 (4 self)
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Statistical properties of chaotic dynamical systems are difficult to estimate reliably. Using long trajectories as data sets sometimes produces misleading results. It has been recognised for some time that statistical properties are often stable under the addition of a small amount of noise. Rather than analysing the dynamical system directly, we slightly perturb it to create a Markov model. The analogous statistical properties of the Markov model often have "closed forms" and are easily computed numerically. The Markov construction is observed to provide extremely robust estimates and has the theoretical advantage of allowing one to prove convergence in the noise → 0 limit and produce rigorous error bounds for statistical quantities. We review the latest results and techniques in this area.
The Fundamental Role of General Orthonormal Bases in System Identification
 IEEE Transactions on Automatic Control
, 1997
"... The purpose of this paper is threefold. Firstly, it is to establish that contrary to what might be expected, the accuracy of well known and frequently used asymptotic variance results can depend on choices of fixed poles or zeros in the model structure. Secondly, it is to derive new variance express ..."
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Cited by 21 (11 self)
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The purpose of this paper is threefold. Firstly, it is to establish that contrary to what might be expected, the accuracy of well known and frequently used asymptotic variance results can depend on choices of fixed poles or zeros in the model structure. Secondly, it is to derive new variance expressions that can provide greatly improved accuracy while also making explicit the influence of any fixed poles or zeros. This is achieved by employing certain new results on generalised Fourier series and the asymptotic properties of Toeplitzlike matrices in such a way that the new variance expressions presented here encompass preexisting ones as special cases. Via this latter analysis a new perspective emerges on recent work pertaining to the use of orthonormal basis structures in system identification. Namely, that orthonormal bases are much more than an implementational option offering improved numerical properties. In fact, they are an intrinsic part of estimation since, as shown here, or...
It’s a long way to Monte Carlo: probabilistic display in GPS navigation
 Proc. MobileHCI'06
, 2006
"... We present a mobile, GPSbased multimodal navigation system, equipped with inertial control that allows users to explore and navigate through an augmented physical space, incorporating and displaying the uncertainty resulting from inaccurate sensing and unknown user intentions. The system propagates ..."
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Cited by 13 (7 self)
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We present a mobile, GPSbased multimodal navigation system, equipped with inertial control that allows users to explore and navigate through an augmented physical space, incorporating and displaying the uncertainty resulting from inaccurate sensing and unknown user intentions. The system propagates uncertainty appropriately via Monte Carlo sampling and predicts at a usercontrollable time horizon. Control of the Monte Carlo exploration is entirely tiltbased. The system output is displayed both visually and in audio. Audio is rendered via granular synthesis to accurately display the probability of the user reaching targets in the space. We also demonstrate the use of uncertain prediction in a trajectory following task, where a section of music is modulated according to the changing predictions of user position with respect to the target trajectory. We show that appropriate display of the full distribution of potential future users positions with respect to sitesofinterest can improve the quality of interaction over a simplistic interpretation of the sensed data. H5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous,
Quantifying the Accuracy of Hammerstein Model Estimation
, 1999
"... This paper investigates the accuracy of the linear component that forms part of an overall Hammerstein modelstructure estimate, and a key finding is that the process of estimating the nonlinear element can have a strong effect on the associated estimate of the linear dynamics. Furthermore, this ef ..."
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Cited by 11 (2 self)
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This paper investigates the accuracy of the linear component that forms part of an overall Hammerstein modelstructure estimate, and a key finding is that the process of estimating the nonlinear element can have a strong effect on the associated estimate of the linear dynamics. Furthermore, this effect is not explained simply by way of considering how the input spectrum is changed by the nonlinearity. Instead, it arises that the linear modelestimate variability may be dominated by a term that depends on the frequency response of the linear system itself. Amongst other things, the main results derived here have experiment design implications for Hammerstein system estimation. Technical Report EE9933, Department of Electrical and Computer Engineering, University of Newcastle, AUSTRALIA 1
Towards a definition of climate science
 Int. J. Environment and Pollution
, 2002
"... Abstract: The intrinsic difficulties in building realistic climate models and in providing complete, reliable and meaningful observational datasets, and the conceptual impossibility of testing theories against data imply that the usual Galilean scientific validation criteria do not apply to climate ..."
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Cited by 6 (2 self)
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Abstract: The intrinsic difficulties in building realistic climate models and in providing complete, reliable and meaningful observational datasets, and the conceptual impossibility of testing theories against data imply that the usual Galilean scientific validation criteria do not apply to climate science. The different epistemology pertaining to climate science implies that its answers cannot be singular and deterministic; they must be plural and stated in probabilistic terms. Therefore, in order to extract meaningful estimates of future climate change from a model, it is necessary to explore the model’s uncertainties. In terms of societal impacts of scientific knowledge, it is necessary to accept that any political choice in a matter involving complex systems is made under unavoidable conditions of uncertainty. Nevertheless, detailed probabilistic results in science can provide a baseline for a sensible process of decision making.
The bootstrap and Lyapunov exponents in deterministic chaos
, 1999
"... Inasmuch as Lyapunov exponents provide a necessary condition for chaos in a dynamical system, confidence bounds on estimated Lyapunov exponents are of great interest. Estimates derived either from observations or from numerical integrations are limited to trajectories of finite length, and it is the ..."
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Cited by 6 (2 self)
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Inasmuch as Lyapunov exponents provide a necessary condition for chaos in a dynamical system, confidence bounds on estimated Lyapunov exponents are of great interest. Estimates derived either from observations or from numerical integrations are limited to trajectories of finite length, and it is the uncertainties in (the distribution of) these finite time Lyapunov exponents which are of interest. Within this context a bootstrap algorithm for quantifying sampling uncertainties is shown to be inappropriate for multiplicativeergodic statistics of deterministic chaos. This result remains unchanged in the presence of observational noise. As originally proposed, the algorithm is also inappropriate for general nonlinear stochastic processes, a modified version is presented which may prove of value in the case of stochastic dynamics. A new approach towards quantifying the minimum duration of observations required to estimate global Lyapunov exponents is suggested and is explored in a companio...
An Intelligent Expert System for Decision Analysis and Support in MultiAttribute Layout Optimization
"... I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to public. ___________________ AbdulRahim Ahmad ii Layout Decision Anal ..."
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Cited by 1 (1 self)
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I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to public. ___________________ AbdulRahim Ahmad ii Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and illstructured problem that requires intelligent and sophisticated decision analysis and design support. Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decision support system for layout design that could deal with such challenging issues by providing efficient and effective
Optimally Robust System Identification Of Systems Subject To Amplitude Bounded Stochastic Disturbances
, 1997
"... In this contribution it is shown that log cos(ßx=(2C)) is the optimally robust norm for prediction error methods with respect to amplitude bounded stochastic disturbances. This norm minimizes the maximum asymptotic covariance matrix of the parameter estimates for the family of innovations of the sys ..."
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In this contribution it is shown that log cos(ßx=(2C)) is the optimally robust norm for prediction error methods with respect to amplitude bounded stochastic disturbances. This norm minimizes the maximum asymptotic covariance matrix of the parameter estimates for the family of innovations of the system which are amplitude bounded by the constant C. Furthermore, the stochastic worstcase performance of the estimate corresponding to the norm log cos(ßx=(2C)) is better than the worstcase performance of the leastsquares estimate even if the constant C is chosen larger than the actual amplitude bound on the innovations. In addition to its favorable properties in a stochastic setting this norm also generates estimates which are unfalsified in a deterministic framework.
Modeling of Normalized Coprime Factors with νMetric Uncertainty
, 1999
"... Modeling of uncertain systems with normalized coprime factor description is investigated where the experimental data is given by a finite set of frequency response measurement samples of the open loop plant that is linear, timeinvariant, and possibly infinitedimensional. The objective is not only ..."
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Modeling of uncertain systems with normalized coprime factor description is investigated where the experimental data is given by a finite set of frequency response measurement samples of the open loop plant that is linear, timeinvariant, and possibly infinitedimensional. The objective is not only to identify the nominal model but also to quantify the modeling error with supnorm bounds in frequency domain. The uncertainty to be identified and quantified is chosen as the νmetric, proposed by Vinnicombe [43], because of its compatibility with H∞based robust control. An algorithm is developed to model the normalized coprime factors of the given plant using techniques of discrete Fourier analysis (DFA) and balanced stochastic truncation (BST), and is shown to be robust in presence of the worstcase noise. Upper bounds are derived for the associated modeling error based on the minimum a priori information of the underlying model set and of the noise level in the measurement data. A simul...