A nonlinear Gauss-Seidel type algorithm is proposed for computing the maximum posterior estimates of the random effects in a generalized linear mixed model. We show that the algorithm converges in virtually all typica...
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A nonlinear Gauss-Seidel type algorithm is proposed for computing the maximum posterior estimates of the random effects in a generalized linear mixed model. We show that the algorithm converges in virtually all typical situations of generalized linear mixed models. A numerical example shows the superiority of the proposed algorithm over the standard Newton-Raphson procedure when the number of random effects is large.
This paper presents an efficient recursive learning algorithm for improving generalization performance of radial basis function (RBF) neural networks. The approach combines the rival penalized competitive learning (PR...
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This paper presents an efficient recursive learning algorithm for improving generalization performance of radial basis function (RBF) neural networks. The approach combines the rival penalized competitive learning (PRCL) [Xu, L., Kizyzak, A. & Oja, E. (1993). Rival penalized competitive learning for clustering analysis, RBF net and curve detection, IEEE Transactions on Neural Networks, 4, 636-649] and the regularized least squares (RLS) to provide an efficient and powerful procedure for constructing a minimal RBF network that generalizes very well. The RPCL selects the number of hidden units of network and adjusts centers, while the RLS constructs the parsimonious network and estimates the connection weights. In the RLS we derived a simple recursive algorithm, which needs no matrix calculation, and so largely reduces the computational cost. This combined algorithm significantly enhances the generalization performance and the real-time capability of the RBF networks. Simulation results of three different problems demonstrate much better generalization performance of the present algorithm over other existing similar algorithms. (C) 2000 Elsevier Science Ltd. All rights reserved.
This paper presents a comparison design of comb decimators based on the non-recursive algorithm and the recursive algorithm. Compared with the recursive algorithm, the main advantage of the non-recursive algorithm is ...
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This paper presents a comparison design of comb decimators based on the non-recursive algorithm and the recursive algorithm. Compared with the recursive algorithm, the main advantage of the non-recursive algorithm is its abilities of reducing power consumption and increasing circuit speed especially when the decimation ratio and filter order are high. Based on the non-recursive algorithm, a decimator with programmable filter orders (3rd, 4th and 5th), decimation ratios (8, 16, 32 and 64) and input bits (1 and 2 bits) has been implemented in a 0.6 mu m 3.3 V CMOS process. Its measured core power consumption is 44 mW at the oversampling rate of 25 MHz and its highest input data rate is 110 MHz.
Suppose the X-0, ..., X-n are observations of a one-dimensional stochastic dynamic process described by autoregression equations when the autoregressive parameter is drifted with time, i.e. it is some function of time...
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Suppose the X-0, ..., X-n are observations of a one-dimensional stochastic dynamic process described by autoregression equations when the autoregressive parameter is drifted with time, i.e. it is some function of time: theta (0), ..., theta (n), with theta (k) = theta (k/n). The function theta (t) is assumed to belong a priori to a predetermined nonparametric class of functions satisfying the Lipschitz smoothness condition. At each time point t those observations are accessible which have been obtained during the preceding time interval. A recursive algorithm is proposed to estimate theta (t). Under some conditions on the model, we derive the rate of convergence of the proposed estimator when the frequency of observations n tends to infinity.
On-line tool wear estimation in turning is essential for on-line cutting process optimization. In this paper, an adaptive observer based on cutting force measurement is used for a reliable on-line flank wear estimatio...
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On-line tool wear estimation in turning is essential for on-line cutting process optimization. In this paper, an adaptive observer based on cutting force measurement is used for a reliable on-line flank wear estimation and tool life monitoring. The design of the adaptive observer is realized using a linear observer and a recursive Instrumental Variable method as the adaptation algorithm. A continuous time hybrid identification approach is used. For model validation, the flank wear is estimated using a nonlinear model.
This paper presents two new families of the generalized Ball curves which include the Be′zier curve, the generalized Ball curves defined by Wang and Said independently and some intermediate curves. The relative degre...
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This paper presents two new families of the generalized Ball curves which include the Be′zier curve, the generalized Ball curves defined by Wang and Said independently and some intermediate curves. The relative degree elevation and reduction schemes, recursive algorithms and the Bernstein-Be′zier representation are also given.
作者：
Qu, SCLaboratory of Chromatography
DEPg.Fac.Quimica Universidad Nacional Autonoma de Mexico Circuito interior Cd Universitaria/CP 04510 Mexico D.F.Mexico
This paper presents a new recursive algorithm for calculating end-to-end blocking probability (EEB) with an arbitrary fixed nonhierarchical routing (AFNR) in a circuit-switched network, The new algorithm improves the ...
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This paper presents a new recursive algorithm for calculating end-to-end blocking probability (EEB) with an arbitrary fixed nonhierarchical routing (AFNR) in a circuit-switched network, The new algorithm improves the worst bound of the computation amount (WBCA) for the EEB of a node pair given in Chan [2] 2(n-1) times, where n is the number of the loss paths in the path-loss sequence, The amount of its practical computation may be far lower than WBCA. For an extreme case, sequential office-control (SOC) routing, it is proportional to the number of the completion paths from origination node to destination node.
The optimal reduced-order estimation problem in which the plant model depends on parameters, which are known at the time of operation, is considered. Such cases occur when the parameters are either measurable or their...
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The optimal reduced-order estimation problem in which the plant model depends on parameters, which are known at the time of operation, is considered. Such cases occur when the parameters are either measurable or their changing values are known in advance, A method for approximation of the updated estimator, without complete re-solution of the problem, is given. A similar approach is used to develop a new algorithm for the numerical solution of the nominal reduced-order estimation problem.
A novel type of recursive algorithm for decoding turbo codes with a convolutional interleaver is proposed, based on a symbol-by-symbol maximum a posteriori probability algorithm. It requires only a forward recursion w...
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A novel type of recursive algorithm for decoding turbo codes with a convolutional interleaver is proposed, based on a symbol-by-symbol maximum a posteriori probability algorithm. It requires only a forward recursion which can be performed in parallel, and the number of variables to be stored does not increase with decoding delay. This algorithm can be used in continuous decoding for both recursive and nonrecursive encoders. Simulation results of demonstrating its performance are presented.
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