In this paper, we propose a new recursive subspace model identification (RSMI) based on regression and natural power method (NP) which is an array signal processing algorithm with excellent convergence properties. We ...
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In this paper, we propose a new recursive subspace model identification (RSMI) based on regression and natural power method (NP) which is an array signal processing algorithm with excellent convergence properties. We call this new algorithm as ‘R-NP'. The basic idea of the algorithm is to utilize an unstructured least squares linear regression approach at the updating observation vector step and the close relationship between RSMI with NP. This algorithm has simpler procedures than other RSMI algorithms. A numerical example illustrates that R-NP method is efficient and have a better performance in terms of transient behavior with respect to EIVPAST. In this paper, we consider the case where the order of system to be identified is a priori known.
Image segmentation is a key part in image processing field Though traditional threshold methods based on maximum entropy principle could sometimes divide the image into object and background, the size of search space ...
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ISBN:
(纸本)9781424417339
Image segmentation is a key part in image processing field Though traditional threshold methods based on maximum entropy principle could sometimes divide the image into object and background, the size of search space increase very rapidly when the number of parameters needed to determine the membership function increase, so it's time-consuming is often an obstacle. In this paper, based on the traditional threshold methods, the membership function is simplified and a new recursive scheme is introduced which decreases the computation complexity of the traditional method. The approach does not need the calculation of the membership function and increases the calculated speed. The experiments prove this novel approach is not only segment accuracy, but also decrease the computation time. Comparing with the traditional methods, the new method shows better real timing and noise restraining performance.
In this paper we introduce a so called C-Matrix w.r.t a rational interpolation problem and study the relationship between the unattainable points and C-Matrix. Finally, we present a recursive algorithm on rational int...
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In this paper we introduce a so called C-Matrix w.r.t a rational interpolation problem and study the relationship between the unattainable points and C-Matrix. Finally, we present a recursive algorithm on rational interpolation.
A novel fast recursive minimum mean square error, successive interference cancellation (MMSE-SIC) algorithm with optimal detection order for vertical Bell Laboratories layered space-time (V-BLAST) systems is proposed....
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A novel fast recursive minimum mean square error, successive interference cancellation (MMSE-SIC) algorithm with optimal detection order for vertical Bell Laboratories layered space-time (V-BLAST) systems is proposed. In this algorithm, the MMSE filter matrices and the optimal detection order are successively computed from the previously obtained filter matrices according to simple recursive pseudoinverse formulas, so that the algorithmic complexity is reduced significantly, especially for the practical number of transmit/receive antennas.
Among various DNA computing algorithms, it is very important to generate an initial data pool that covers both correct and incorrect answers. However, algorithms based on brute force search are limited to problem size...
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Among various DNA computing algorithms, it is very important to generate an initial data pool that covers both correct and incorrect answers. However, algorithms based on brute force search are limited to problem size, since the bigger the problem, the bigger the initial data pool. In this paper, we propose a recursive molecular sticker algorithm to resolve the maximal clique problem. Some graphs with more vertices can be handled with the proposed algorithm. Particularly, our algorithm is adapted to some graphs with lower edge density. For a given graph with n vertices, the time complexity of our algorithm is O(n(2)). In addition, three kinds of enzymes, two kinds of memory strands, n kinds of stickers, n sticker tubes, n+2 separation tubes and n data tubes are necessary for our recursive molecular sticker algorithm to resolve the maximal clique problem.
The modelling of fractional linear systems through ARMA models is addressed. To perform this study, a new recursive algorithm for impulse response ARMA modelling is presented. This is a general algorithm that allows t...
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The modelling of fractional linear systems through ARMA models is addressed. To perform this study, a new recursive algorithm for impulse response ARMA modelling is presented. This is a general algorithm that allows the recursive construction of ARMA models from the impulse response sequence. This algorithm does not need an exact order specification, as it gives some insights into the correct orders. It is applied to modelling fractional linear systems described by fractional powers of the backward difference and the bilinear transformations. The analysis of the results leads to propose suitable models for those systems.
In this paper, the limit distributions of the recursive M-estimators of scatter parameters in a multivariate linear model setting are studied. Under some mild conditions, the asymptotic normality of the recursive M-es...
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In this paper, the limit distributions of the recursive M-estimators of scatter parameters in a multivariate linear model setting are studied. Under some mild conditions, the asymptotic normality of the recursive M-esimtators is established. Some Monte Carlo simulation results are presented to illustrate the performance of the recursive M-estimators.
The multistate networks under consideration consist of a source node, a sink node, and some independent failure-prone components in between the nodes. The components can work at different levels of capacity For such a...
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The multistate networks under consideration consist of a source node, a sink node, and some independent failure-prone components in between the nodes. The components can work at different levels of capacity For such a network, we are interested in evaluating the probability that the flow from the source node to the sink node is equal to or greater than a demanded flow of d units. A general method for reliability evaluation of such multistate networks is using minimal path (cut) vectors. A minimal path vector to system state d is called a d-MP. Approaches for generating all d-MPs have been reported. Given that all d-MPs have been found, the issue becomes how to evaluate the probability of the union of the events that the component state vector is greater than or equal to at least one of the d-MPs. There is a need for a more efficient method of determining the probability of this union of events. In this paper, we report an efficient recursive algorithm for this union probability evaluation based on the Sum of Disjoint Products (SDP) principle, and name it the recursive Sum of Disjoint Products (RSDP) algorithm. The basic idea is that, based on the SDP principle and a specially defined "maximum" operator, "Phi", the probability of a union with L vectors can be calculated via calculating the probabilities of several unions with L - I vectors or less. The correctness of RSDP is illustrated. The efficiency of this algorithm is investigated by comparing it with an existing algorithm that is generally accepted to be efficient. It is found that RSDP is more efficient than the existing algorithm when the number of components of a. system is not too small. RSDP provides us with an efficient, systematic and simple approach for evaluating multistate network reliability given all d-MPs.
In this paper, a new subspace method for predicting time-varying stochastic systems is proposed. Using the concept of angle between past and present subspaces spanned by the extended observability matrices, the future...
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In this paper, a new subspace method for predicting time-varying stochastic systems is proposed. Using the concept of angle between past and present subspaces spanned by the extended observability matrices, the future signal subspace is predicted by rotating the present subspace in the geometrical sense, and time-varying system matrices are derived from the resultant signal subspace. Proposed algorithm is improved for fast-varying systems. Furthermore, recursive implementation of both algorithms is developed. (c) 2007 Elsevier Ltd. All rights reserved.
The study on epistemic uncertainty due to the lack of knowledge has received increasing attention in risk assessment, reliability analysis, decision making, and design optimization. Different theories have been applie...
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The study on epistemic uncertainty due to the lack of knowledge has received increasing attention in risk assessment, reliability analysis, decision making, and design optimization. Different theories have been applied to model and quantify epistemic uncertainty. Research on sensitivity analysis for epistemic uncertainty has also been initialized. Sensitivity analysis can identify the contributions of individual input variables with epistemic uncertainty to the model output. It then helps guide the collection of more information to reduce the effect of epistemic uncertainty. In this paper, an effective sensitivity analysis method for epistemic uncertainty is proposed when both epistemic and aleatory uncertainties exist in model inputs. This method employs the unified uncertainty analysis framework to calculate the plausibility and belief measures. The gap between belief and plausibility measures is used as an indicator of the effect of epistemic uncertainty on the model output. The Kolmogorov-Smirnov distance between the two measures is used to quantify the main effect and the total effect of each independent variable with epistemic uncertainty. By the Kolmogorov-Smirnov distance, the importance of each variable is ranked. The feasibility and effectiveness of the proposed method is demonstrated with two engineering examples.
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