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Filtering Problem (filtering + problem)
Selected AbstractsHIGH-DIMENSIONAL LEARNING FRAMEWORK FOR ADAPTIVE DOCUMENT FILTERING,COMPUTATIONAL INTELLIGENCE, Issue 1 2003Wai Lam We investigate the unique requirements of the adaptive textual document filtering problem and propose a new high-dimensional on-line learning framework, known as the REPGER (relevant feature pool with good training example retrieval rule) algorithm to tackle this problem. Our algorithm possesses three characteristics. First, it maintains a pool of selective features with potentially high predictive power to predict document relevance. Second, besides retrieving documents according to their predicted relevance, it also retrieves incoming documents that are considered good training examples. Third, it can dynamically adjust the dissemination threshold throughout the filtering process so as to maintain a good filtering performance in a fully interactive environment. We have conducted experiments on three document corpora, namely, Associated Press, Foreign Broadcast Information Service, and Wall Street Journal to compare the performance of our REPGER algorithm with two existing on-line learning algorithms. The results demonstrate that our REPGER algorithm gives better performance most of the time. Comparison with the TREC (Text Retrieval Conference) adaptive text filtering track participants was also made. The result shows that our REPGER algorithm is comparable to them. [source] Spatio-temporal point process filtering methods with an applicationENVIRONMETRICS, Issue 3-4 2010ena Frcalová Abstract The paper deals with point processes in space and time and the problem of filtering. Real data monitoring the spiking activity of a place cell of hippocampus of a rat moving in an environment are evaluated. Two approaches to the modelling and methodology are discussed. The first one (known from literature) is based on recursive equations which enable to describe an adaptive system. Sequential Monte Carlo methods including particle filter algorithm are available for the solution. The second approach makes use of a continuous time shot-noise Cox point process model. The inference of the driving intensity leads to a nonlinear filtering problem. Parametric models support the solution by means of the Bayesian Markov chain Monte Carlo methods, moreover the Cox model enables to detect adaptivness. Model selection is discussed, numerical results are presented and interpreted. Copyright © 2009 John Wiley & Sons, Ltd. [source] Central suboptimal H, filter design for nonlinear polynomial systemsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2009Michael Basin Abstract This paper presents the central finite-dimensional H, filter for nonlinear polynomial systems, which is suboptimal for a given threshold , with respect to a modified Bolza,Meyer quadratic criterion including the attenuation control term with the opposite sign. In contrast to the previously obtained results, the paper reduces the original H, filtering problem to the corresponding optimal H2 filtering problem, using the technique proposed in (IEEE Trans. Automat. Control 1989; 34:831,847). The paper presents the central suboptimal H, filter for the general case of nonlinear polynomial systems based on the optimal H2 filter given in (Int. J. Robust Nonlinear Control 2006; 16:287,298). The central suboptimal H, filter is also derived in a closed finite-dimensional form for third (and less) degree polynomial system states. Numerical simulations are conducted to verify performance of the designed central suboptimal filter for nonlinear polynomial systems against the central suboptimal H, filter available for the corresponding linearized system. Copyright © 2008 John Wiley & Sons, Ltd. [source] Optimal filtering for incompletely measured polynomial states over linear observationsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 5 2008Michael Basin Abstract In this paper, the optimal filtering problem for polynomial system states over linear observations with an arbitrary, not necessarily invertible, observation matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the observation equation is introduced to reduce the original problem to the previously solved one with an invertible observation matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular case of a third-order state equation. In the example, performance of the designed optimal filter is verified against a conventional extended Kalman,Bucy filter. Copyright © 2007 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] Mixed ,2/,, nonlinear filteringINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 4 2009M. D. S. Aliyu Abstract In this paper, we consider the mixed ,2/,, filtering problem for affine nonlinear systems. Sufficient conditions for the solvability of this problem with a finite-dimensional filter are given in terms of a pair of coupled Hamilton,Jacobi,Isaacs equations (HJIEs). For linear systems, it is shown that these conditions reduce to a pair of coupled Riccati equations similar to the ones for the control case. Both the finite-horizon and the infinite-horizon problems are discussed. Simulation results are presented to show the usefulness of the scheme, and the results are generalized to include other classes of nonlinear systems. Copyright © 2008 John Wiley & Sons, Ltd. [source] Analysis, design, and performance limitations of H, optimal filtering in the presence of an additional input with known frequencyINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 16 2007Ali Saberi Abstract A generalized ,-level H, sub-optimal input decoupling (SOID) filtering problem is formulated. It is a generalization of ,-level H, SOID filtering problem when, besides an input with unknown statistical properties but with a finite RMS norm, there exists an additional input to the given plant or system. The additional input is a linear combination of sinusoidal signals each of which has an unknown amplitude and phase but known frequency. The analysis, design, and performance limitations of generalized H, optimal filters are presented. Copyright © 2007 John Wiley & Sons, Ltd. [source] Gain-scheduled H, filtering of parameter-varying systemsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 8 2006Shaosheng Zhou Abstract This paper deals with the gain-scheduled H, filtering problem for a class of parameter-varying systems. A sufficient condition for the existence of a gain-scheduled filter, which guarantees the asymptotic stability with an H, noise attenuation level bound for the filtering error system, is given in terms of a finite number of linear matrix inequalities (LMIs). The filter is designed to be parameter-varying and have a nonlinear fractional transformation structure. A numerical example is presented to demonstrate the application of the proposed method. Copyright © 2006 John Wiley & Sons, Ltd. [source] Optimal filtering for polynomial system states with polynomial multiplicative noiseINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 6 2006Michael Basin Abstract In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of a linear state equation with linear multiplicative noise and a bilinear state equation with bilinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic state with a quadratic multiplicative noise over linear observations against the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman,Bucy filter. Copyright © 2006 John Wiley & Sons, Ltd. [source] PRICING CORPORATE SECURITIES UNDER NOISY ASSET INFORMATIONMATHEMATICAL FINANCE, Issue 3 2009Rüdiger Frey This paper considers the pricing of corporate securities of a given firm, in particular equity, when investors do not have full information on the firm's asset value. We show that under noisy asset information, the pricing of corporate securities leads to a nonlinear filtering problem. This problem is solved by a Markov chain approximation, leading to an efficient finite-dimensional approximative filter for the asset value. We discuss several applications and illustrate our results with a simulation study. [source] A Partially Observed Model for Micromovement of Asset Prices with Bayes Estimation via FilteringMATHEMATICAL FINANCE, Issue 3 2003Yong Zeng A general micromovement model that describes transactional price behavior is proposed. The model ties the sample characteristics of micromovement and macromovement in a consistent manner. An important feature of the model is that it can be transformed to a filtering problem with counting process observations. Consequently, the complete information of price and trading time is captured and then utilized in Bayes estimation via filtering for the parameters. The filtering equations are derived. A theorem on the convergence of conditional expectation of the model is proved. A consistent recursive algorithm is constructed via the Markov chain approximation method to compute the approximate posterior and then the Bayes estimates. A simplified model and its recursive algorithm are presented in detail. Simulations show that the computed Bayes estimates converge to their true values. The algorithm is applied to one month of intraday transaction prices for Microsoft and the Bayes estimates are obtained. [source] Optimal state filtering and parameter identification for linear systemsOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 2 2008Michael Basin Abstract This paper presents the optimal filtering and parameter identification problem for linear stochastic systems with unknown multiplicative and additive parameters over linear observations, where unknown parameters are considered Wiener processes. The original problem is reduced to the filtering problem for an extended state vector that incorporates parameters as additional states. The obtained optimal filter for the extended state vector also serves as the optimal identifier for the unknown parameters. Performance of the designed optimal state filter and parameter identifier is verified for both stable and unstable linear uncertain systems. Copyright © 2007 John Wiley & Sons, Ltd. [source] On the optimality of two-stage Kalman filtering for systems with unknown inputs,ASIAN JOURNAL OF CONTROL, Issue 4 2010Chien-Shu Hsieh Abstract This paper is concerned with the optimal solution of two-stage Kalman filtering for linear discrete-time stochastic time-varying systems with unknown inputs affecting both the system state and the outputs. By means of a newly-presented modified unbiased minimum-variance filter (MUMVF), which appears to be the optimal solution to the addressed problem, the optimality of two-stage Kalman filtering for systems with unknown inputs is defined and explored. Two extended versions of the previously proposed robust two-stage Kalman filter (RTSKF), augmented-unknown-input RTSKF (ARTSKF) and decoupled-unknown-input RTSKF (DRTSKF), are presented to solve the general unknown input filtering problem. It is shown that under less restricted conditions, the proposed ARTSKF and DRTSKF are equivalent to the corresponding MUMVFs. An example is given to illustrate the usefulness of the proposed results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] | ||||||||||