The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Richard D Braatz - One of the best experts on this subject based on the ideXlab platform.
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distributional Uncertainty Analysis using power series and polynomial chaos expansions
Journal of Process Control, 2007Co-Authors: Zoltan K Nagy, Richard D BraatzAbstract:Abstract This paper provides an overview of computationally efficient approaches for quantifying the influence of parameter uncertainties on the states and outputs of nonlinear dynamical systems with finite-time control trajectories, focusing primarily on computing probability distributions. The advantages and disadvantages of various Uncertainty Analysis approaches, which use approximate representations of the full nonlinear model using power series or polynomial chaos expansions, are discussed in terms of computational cost and accuracy in computing the shape and tails of the state and output distributions. Application of the Uncertainty Analysis methods to a simulation study is used to provide advice as to which Uncertainty Analysis methods to select for a particular application. In particular, the results indicate that first-order series Analysis can be accurate enough for the design of real-time robust feedback controllers for batch processes, although it is cautioned that the accuracy of such Analysis should be confirmed a posteriori using a more accurate Uncertainty Analysis method. The polynomial chaos expansion is well suited to robust design and control when the objectives are strongly dependent on the shape or tails of the distributions of product quality or economic objectives.
E. Maltby - One of the best experts on this subject based on the ideXlab platform.
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Uncertainty Analysis in a GIS-based multi-criteria Analysis tool for river catchment management
Environmental Modelling & Software, 2011Co-Authors: Hongyan Chen, Michael Wood, C. Linstead, E. MaltbyAbstract:The importance of Uncertainty Analysis has been increasingly recognised, due to the influence of uncertainties in data, models and expert judgements. However, the successful integration of Uncertainty Analysis into multi-criteria Analysis (MCA) has rarely been achieved. This paper analyses Uncertainty sources in MCA. General methods of Uncertainty Analysis in MCA are reviewed, including probabilistic methods, indicator-based methods and fuzzy logic. Building on this review, an Uncertainty Analysis module developed for use within a GIS-based MCA tool for catchment management is presented. In this module, the influence of uncertainties on decision-making can be visually explored using an indicator-based method. The indicator-based method provides a pragmatic approach to communicating areas of Uncertainty to decision-makers without assuming any prior knowledge of Uncertainty Analysis techniques. This enables Uncertainty Analysis to be more effectively operationalised within the decision-making process. An application example in the Tamar catchment, southwest UK, is used to illustrate the capability of the Uncertainty Analysis module when applied in a decision-making context. Research highlights? Multi-criteria Analysis (MCA) can be used in environmental management decision-making. ? Uncertainty Analysis increases confidence in MCA results. ? CEDSS, a GIS-based DSS, has been developed for river catchment management. ? It uses indicator-based Uncertainty Analysis to communicate Uncertainty to decision-makers. ? Coupled with spatial representation of Uncertainty, this promotes robust decision-making.
George Z. Gertner - One of the best experts on this subject based on the ideXlab platform.
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Uncertainty Analysis of transient population dynamics
Ecological Modelling, 2009Co-Authors: George Z. GertnerAbstract:Abstract Two types of demographic analyses, perturbation Analysis and Uncertainty Analysis, can be conducted to gain insights about matrix population models and guide population management. Perturbation Analysis studies how the perturbation of demographic parameters (survival, growth, and reproduction parameters) may affect the population projection, while Uncertainty Analysis evaluates how much Uncertainty there is in population dynamic predictions and where the Uncertainty comes from. Previously, both perturbation Analysis and Uncertainty Analysis were conducted on the long-term population growth rate. However, the population may not reach its equilibrium state, especially when there is management by harvesting or hunting. Recently, there has been an increased interest in short-term transient dynamics, which can differ from asymptotic long-term dynamics. There are currently techniques to conduct perturbation analyses of short-term transient dynamics, but no techniques have been proposed for Uncertainty Analysis of such dynamics. In this study, we introduced an Uncertainty Analysis technique, the general Fourier Amplitude Sensitivity Test (FAST), to study uncertainties in transient population dynamics. The general FAST is able to identify the amount of Uncertainty in transient dynamics and contributions by different demographic parameters. We applied the general FAST to a mountain goat (Oreamnos americanus) matrix population model to give a clear illustration of how Uncertainty Analysis can be conducted for transient dynamics arising from matrix population models.
Adrianus M. H. Meeuwissen - One of the best experts on this subject based on the ideXlab platform.
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Sensitivity and Uncertainty Analysis of Markov-reward models
IEEE Transactions on Reliability, 1995Co-Authors: Boudewijn R. Haverkort, Adrianus M. H. MeeuwissenAbstract:Markov-reward models are often used to analyze the reliability and performability of computer systems. One difficult problem therein is the quantification of the model parameters. If they are available, e.g., from measurement data collected by manufacturers, they are: (a) generally regarded as confidential; and (b) difficult to access. This paper addresses two ways of dealing with uncertain parameters: (1) sensitivity Analysis, and (2) Monte Carlo Uncertainty Analysis. Sensitivity Analysis is relatively fast and cheap but it correctly describes only the local behavior of the model outcome Uncertainty as a result of the model parameter uncertainties. When the uncertain parameters are dependent, sensitivity Analysis is difficult. The authors extend the classical sensitivity Analysis so that the results conform better to those of the Monte Carlo Uncertainty Analysis. Monte Carlo Uncertainty Analysis provides a global view. Since it can include parameter dependencies, it is more accurate than sensitivity Analysis. By two examples they demonstrate both approaches and illustrate the effects Uncertainty and dependence can have. >
Wout Slob - One of the best experts on this subject based on the ideXlab platform.
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Uncertainty Analysis in Multiplicative Models
Risk Analysis, 1994Co-Authors: Wout SlobAbstract:Uncertainties are usually evaluated by Monte Carlo Analysis. However, multiplicative models with lognormal uncertainties, which are ubiquitous in risk assessments, allow for a simple and quick analytical Uncertainty Analysis. The necessary formulae are given, which may be evaluated by a desk calculator. Two examples illustrate the method.
