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Estimation Algorithms (estimation + algorithms)
Selected AbstractsScales of association: hierarchical linear models and the measurement of ecological systemsECOLOGY LETTERS, Issue 6 2007Sean M. McMahon Abstract A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance,covariance parameters in hierarchically structured data. Although hierarchical models have occasionally been used in the analysis of ecological data, their full potential to describe scales of association, diagnose variance explained, and to partition uncertainty has not been employed. In this paper we argue that the use of the HLM framework can enable significantly improved inference about ecological processes across levels of organization. After briefly describing the principals behind HLM, we give two examples that demonstrate a protocol for building hierarchical models and answering questions about the relationships between variables at multiple scales. The first example employs maximum likelihood methods to construct a two-level linear model predicting herbivore damage to a perennial plant at the individual- and patch-scale; the second example uses Bayesian estimation techniques to develop a three-level logistic model of plant flowering probability across individual plants, microsites and populations. HLM model development and diagnostics illustrate the importance of incorporating scale when modelling associations in ecological systems and offer a sophisticated yet accessible method for studies of populations, communities and ecosystems. We suggest that a greater coupling of hierarchical study designs and hierarchical analysis will yield significant insights on how ecological processes operate across scales. [source] Joint data detection and estimation of time-varying multipath rayleigh fading channels in asynchronous DS-CDMA systems with long spreading sequences,EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS, Issue 2 2007Pei Xiao In this paper, we present a joint approach to data detection and channel estimation for the asynchronous direct-sequence code-division multiple access (DS-CDMA) systems employing orthogonal signaling formats and long scrambling codes. Our emphasis is placed on different channel estimation algorithms since the performance of a communication system depends largely on its ability to retrieve an accurate measurement of the underlying channel. We investigate channel estimation algorithms under different conditions. The estimated channel information is used to enable coherent data detection to combat the detrimental effect of the multiuser interference and the multipath propagation of the transmitted signal. In the considered multiuser detector, we mainly use interference cancellation techniques, which are suitable for long-code CDMA systems. Interference cancellation and channel estimation using soft estimates of the transmitted signal is also proposed in this paper. Different channel estimation schemes are evaluated and compared in terms of mean square error (MSE) of channel estimation and bit error rate (BER) performance. Based on our analysis and numerical results, some recommendations are made on how to choose appropriate channel estimators in practical systems. Copyright © 2006 AEIT [source] Composite adaptive and input observer-based approaches to the cylinder flow estimation in spark ignition automotive enginesINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 2 2004A. Stotsky Abstract The performance of air charge estimation algorithms in spark ignition automotive engines can be enhanced using advanced estimation techniques available in the controls literature. This paper illustrates two approaches of this kind that can improve the cylinder flow estimation for gasoline engines without external exhaust gas recirculation (EGR). The first approach is based on an input observer, while the second approach relies on an adaptive estimator. Assuming that the cylinder flow is nominally estimated via a speed-density calculation, and that the uncertainty is additive to the volumetric efficiency, the straightforward application of an input observer provides an easy to implement algorithm that corrects the nominal air flow estimate. The experimental results that we report in the paper point to a sufficiently good transient behaviour of the estimator. The signal quality may deteriorate, however, for extremely fast transients. This motivates the development of an adaptive estimator that relies mostly on the feedforward speed-density calculation during transients, while during engine operation close to steady-state conditions, it relies mostly on the adaptation. In our derivation of the adaptive estimator, the uncertainty is modelled as an unknown parameter multiplying the intake manifold temperature. We use the tracking error between the measured and modelled intake manifold pressure together with an appropriately defined prediction error estimate to develop an adaptation algorithm with improved identifiability and convergence rate. A robustness enhancement, via a ,-modification with the ,-factor depending on the prediction error estimate, ensures that in transients the parameter estimate converges to a pre-determined a priori value. In close to steady-state conditions, the ,-modification is rendered inactive and the evolution of the parameter estimate is determined by both tracking error and prediction error estimate. Further enhancements are made by incorporating a functional dependence of the a priori value on the engine operating conditions such as the intake manifold pressure. The coefficients of this function can be learned during engine operation from the values to which the parameter estimate converges in close to steady-state conditions. This feedforward learning functionality improves transient estimation accuracy and reduces the convergence time of the parameter estimate. Copyright © 2004 John Wiley & Sons, Ltd. [source] Exponential convergence of the Kalman filter based parameter estimation algorithmINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2003Liyu Cao Abstract In this paper we shall present a new method to analyse the convergence property of the Kalman filter based parameter estimation algorithms. This method for convergence analysis is mainly based on some matrix inequalities and is more simple than some of the existing approaches in the literature. This method can simultaneously provide both lower and upper bounds on the exponential convergence rate as the functions of bounds of the related matrices, such as the covariance matrices. A simulation example is provided to illustrate the convergence property of the Kalman filter based algorithms. Copyright © 2003 John Wiley & Sons, Ltd. [source] Resource allocation in satellite networks: certainty equivalent approaches versus sensitivity estimation algorithmsINTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 1 2005Franco Davoli Abstract In this paper, we consider a resource allocation problem for a satellite network, where variations of fading conditions are added to those of traffic load. Since the capacity of the system is finite and divided in finite discrete portions, the resource allocation problem reveals to be a discrete stochastic programming one, which is typically NP-hard. We propose a new approach based on the minimization over a discrete constraint set using an estimation of the gradient, obtained through a ,relaxed continuous extension' of the performance measure. The computation of the gradient estimation is based on the infinitesimal perturbation analysis technique, applied on a stochastic fluid model of the network. No closed-forms of the performance measure, nor additional feedback concerning the state of the system, and very mild assumptions on the probabilistic properties about the statistical processes involved in the problem are requested. Such optimization approach is compared with a dynamic programming algorithm that maintains a perfect knowledge about the state of the satellite network (traffic load statistics and fading levels). The comparison shows that the sensitivity estimation capability of the proposed algorithm allows to maintain the optimal resource allocation in dynamic conditions and it is able to provide even better performance than the one reached by employing the dynamic programming approach. Copyright © 2004 John Wiley & Sons, Ltd. [source] Robust ,, filtering for uncertain differential linear repetitive processesINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 3 2008Ligang Wu Abstract The unique characteristic of a repetitive process is a series of sweeps or passes through a set of dynamics defined over a finite duration known as the pass length. At the end of each pass, the process is reset and the next time through the output, or pass profile, produced on the previous pass acts as a forcing function on, and hence contributes to, the dynamics of the new pass profile. They are hence a class of systems where a variable must be expressed in terms of two directions of information propagation (from pass-to-pass and along a pass, respectively) where the dynamics over the finite pass length are described by a matrix linear differential equation and from pass to pass by a discrete updating structure. This means that filtering/estimation theory/algorithms for, in particular, 2D discrete linear systems is not applicable. In this paper, we solve a general robust filtering problem with a view towards use in many applications where such an action will be required. Copyright © 2007 John Wiley & Sons, Ltd. [source] | ||||||||||