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Simple Implementation (simple + implementation)
Selected AbstractsSolving, Estimating, and Selecting Nonlinear Dynamic Models Without the Curse of DimensionalityECONOMETRICA, Issue 2 2010Viktor Winschel We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids to overcome the curse of dimensionality for approximations. We apply sparse grids to a global polynomial approximation of the model solution, to the quadrature of integrals arising as rational expectations, and to three new nonlinear state space filters which speed up the sequential importance resampling particle filter. The posterior of the structural parameters is estimated by a new Metropolis,Hastings algorithm with mixing parallel sequences. The parallel extension improves the global maximization property of the algorithm, simplifies the parameterization for an appropriate acceptance ratio, and allows a simple implementation of the estimation on parallel computers. Finally, we provide all algorithms in the open source software JBendge for the solution and estimation of a general class of models. [source] A forward-only recursion algorithm for MAP decoding of linear block codesINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 8 2002Hans-Jürgen Zepernick Abstract The evolution of digital mobile communications along with the increase of integrated circuit complexity has resulted in frequent use of error control coding to protect information against transmission errors. Soft decision decoding offers better error performance compared to hard decision decoding but on the expense of decoding complexity. The maximum a posteriori (MAP) decoder is a decoding algorithm which processes soft information and aims at minimizing bit error probability. In this paper, a matrix approach is presented which analytically describes MAP decoding of linear block codes in an original domain and a corresponding spectral domain. The trellis-based decoding approach belongs to the class of forward-only recursion algorithms. It is applicable to high rate block codes with a moderate number of parity bits and allows a simple implementation in the spectral domain in terms of storage requirements and computational complexity. Especially, the required storage space can be significantly reduced compared to conventional BCJR-based decoding algorithms. Copyright © 2002 John Wiley & Sons, Ltd. [source] Clock synchronization for packet networks using a weighted least-squares error filtering technique and enabling circuit emulation serviceINTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 6 2007James Aweya Abstract Circuit emulation service (CES) allows time-division multiplexing (TDM) services (T1/E1 and T3/E3 circuits) to be transparently extended across a packet network. With circuit emulation over IP, for instance, TDM data received from an external device at the edge of an IP network is converted to IP packets, sent through the IP network, passed out of the IP network to its destination, and reassembled into TDM bit stream. Clock synchronization is very important for CES. This paper presents a clock synchronization scheme based on a double exponential filtering technique and a linear process model. The linear process model is used to describe the behaviour of clock synchronization errors between a transmitter and a receiver. In the clock synchronization scheme, the transmitter periodically sends explicit time indications or timestamps to a receiver to enable the receiver to synchronize its local clock to the transmitter's clock. A phase-locked loop (PLL) at the receiver processes the transmitted timestamps to generate timing signal for the receiver. The PLL has a simple implementation and provides both fast responsiveness (i.e. fast acquisition of transmitter frequency at a receiver) and significant jitter reduction in the locked state. Copyright © 2006 John Wiley & Sons, Ltd. [source] Modeling and predictive control using fuzzy logic: Application for a polymerization systemAICHE JOURNAL, Issue 4 2010Nádson M. N. Lima Abstract In this study, a predictive control system based on type Takagi-Sugeno fuzzy models was developed for a polymerization process. Such processes typically have a highly nonlinear dynamic behavior causing the performance of controllers based on conventional internal models to be poor or to require considerable effort in controller tuning. The copolymerization of methyl methacrylate with vinyl acetate was considered for analysis of the performance of the proposed control system. A nonlinear mathematical model which describes the reaction plant was used for data generation and implementation of the controller. The modeling using the fuzzy approach showed an excellent capacity for output prediction as a function of dynamic data input. The performance of the projected control system and dynamic matrix control for regulatory and servo problems were compared and the obtained results showed that the control system design is robust, of simple implementation and provides a better response than conventional predictive control. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source] Penalized spline models for functional principal component analysisJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2006Fang Yao Summary., We propose an iterative estimation procedure for performing functional principal component analysis. The procedure aims at functional or longitudinal data where the repeated measurements from the same subject are correlated. An increasingly popular smoothing approach, penalized spline regression, is used to represent the mean function. This allows straightforward incorporation of covariates and simple implementation of approximate inference procedures for coefficients. For the handling of the within-subject correlation, we develop an iterative procedure which reduces the dependence between the repeated measurements that are made for the same subject. The resulting data after iteration are theoretically shown to be asymptotically equivalent (in probability) to a set of independent data. This suggests that the general theory of penalized spline regression that has been developed for independent data can also be applied to functional data. The effectiveness of the proposed procedure is demonstrated via a simulation study and an application to yeast cell cycle gene expression data. [source] | ||||||||||