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Least-squares Algorithm (least-square + algorithm)
Selected AbstractsSemi-blind fast equalization of QAM channels using concurrent gradient-Newton CMA and soft decision-directed schemeINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 6 2010S. Chen Abstract This contribution considers semi-blind adaptive equalization for communication systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the equalizer, are first utilized to provide a rough initial least-squares estimate of the equalizer's weight vector. A novel gradient-Newton concurrent constant modulus algorithm and soft decision-directed scheme are then applied to adapt the equalizer. The proposed semi-blind adaptive algorithm is capable of converging fast and accurately to the optimal minimum mean-square error equalization solution. Simulation results obtained demonstrate that the convergence speed of this semi-blind adaptive algorithm is close to that of the training-based recursive least-square algorithm. Copyright © 2009 John Wiley & Sons, Ltd. [source] Dynamic Wavelet Neural Network for Nonlinear Identification of Highrise BuildingsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2005Xiaomo Jiang Compared with conventional neural networks, training of a dynamic neural network for system identification of large-scale structures is substantially more complicated and time consuming because both input and output of the network are not single valued but involve thousands of time steps. In this article, an adaptive Levenberg,Marquardt least-squares algorithm with a backtracking inexact linear search scheme is presented for training of the dynamic fuzzy WNN model. The approach avoids the second-order differentiation required in the Gauss,Newton algorithm and overcomes the numerical instabilities encountered in the steepest descent algorithm with improved learning convergence rate and high computational efficiency. The model is applied to two highrise moment-resisting building structures, taking into account their geometric nonlinearities. Validation results demonstrate that the new methodology provides an efficient and accurate tool for nonlinear system identification of high-rising buildings. [source] 2D data modelling by electrical resistivity tomography for complex subsurface geologyGEOPHYSICAL PROSPECTING, Issue 2 2006E. Cardarelli ABSTRACT A new tool for two-dimensional apparent-resistivity data modelling and inversion is presented. The study is developed according to the idea that the best way to deal with ill-posedness of geoelectrical inverse problems lies in constructing algorithms which allow a flexible control of the physical and mathematical elements involved in the resolution. The forward problem is solved through a finite-difference algorithm, whose main features are a versatile user-defined discretization of the domain and a new approach to the solution of the inverse Fourier transform. The inversion procedure is based on an iterative smoothness-constrained least-squares algorithm. As mentioned, the code is constructed to ensure flexibility in resolution. This is first achieved by starting the inversion from an arbitrarily defined model. In our approach, a Jacobian matrix is calculated at each iteration, using a generalization of Cohn's network sensitivity theorem. Another versatile feature is the issue of introducing a priori information about the solution. Regions of the domain can be constrained to vary between two limits (the lower and upper bounds) by using inequality constraints. A second possibility is to include the starting model in the objective function used to determine an improved estimate of the unknown parameters and to constrain the solution to the above model. Furthermore, the possibility either of defining a discretization of the domain that exactly fits the underground structures or of refining the mesh of the grid certainly leads to more accurate solutions. Control on the mathematical elements in the inversion algorithm is also allowed. The smoothness matrix can be modified in order to penalize roughness in any one direction. An empirical way of assigning the regularization parameter (damping) is defined, but the user can also decide to assign it manually at each iteration. An appropriate tool was constructed with the purpose of handling the inversion results, for example to correct reconstructed models and to check the effects of such changes on the calculated apparent resistivity. Tests on synthetic and real data, in particular in handling indeterminate cases, show that the flexible approach is a good way to build a detailed picture of the prospected area. [source] Least-squares parameter estimation for systems with irregularly missing dataINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 7 2010Feng Ding Abstract This paper considers the problems of parameter identification and output estimation with possibly irregularly missing output data, using output error models. By means of an auxiliary model (or reference model) approach, we present a recursive least-squares algorithm to estimate the parameters of missing data systems, and establish convergence properties for the parameter and missing output estimation in the stochastic framework. The basic idea is to replace the unmeasurable inner variables with the output of an auxiliary model. Finally, we test the effectiveness of the algorithm with an example system. Copyright © 2009 John Wiley & Sons, Ltd. [source] Exact initialization of the recursive least-squares algorithmINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 3 2002Petre Stoica Abstract We present an initialization procedure for the recursive least-squares (RLS) algorithm that has almost the same form as the RLS algorithm itself and which is exact in the sense that the so-initialized RLS estimate coincides with the batch LS estimate as soon as the latter exists. Copyright © 2002 John Wiley & Sons, Ltd. [source] Experimental modelling and intelligent control of a wood-drying kilnINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 8 2001Givon Chuen Kee Yan Abstract Proper control of the wood-drying kiln is crucial in ensuring satisfactory quality of dried wood and in minimizing drying time. This paper presents the development, implementation, and evaluation of a control system for a lumber drying kiln process incorporating sensory feedback from in-wood moisture content sensors and intelligent control such that the moisture content of lumber will reach and stabilize at the desired set point without operator interference. The drying process is difficult to model and control due to complex dynamic nonlinearities, coupling effects among key variables, and process disturbances caused by the variation of lumber sizes, species, and environmental factors. Through system identification scheme using experimental data and recursive least-squares algorithm for parameter estimation, appropriate models are developed for simulation purpose and controller design. Two different control methodologies are employed and compared: a conventional proportional-integral-derivative (PID) controller and a direct fuzzy logic controller (FLC), and system performance is evaluated through simulations. The developed control system is then implemented in a downscaled industrial kiln located at the Innovation Centre of National Research Council (NRC) of Canada. This experimental set-up is equipped with a variety of sensors, including thermocouples for temperature feedback, an air velocity transmitter for measuring airflow speed in the plenum, relative humidity sensors for measuring the relative humidity inside the kiln, and in-wood moisture content sensors for measuring the moisture content of the wood pieces. For comparison, extensive experimental studies are carried out on-line using the two controllers, and the results are evaluated to tune the controller parameters to achieve good performance in the wood-drying kiln. The combination of conventional control with the intelligent control promises improved performance. The control system developed in this study may be applied in industrial wood-drying kilns, with a clear potential for improved quality and increased speed of drying. Copyright © 2001 John Wiley & Sons, Ltd. [source] Nonmetric multidimensional scaling with clustering of subjectsJAPANESE PSYCHOLOGICAL RESEARCH, Issue 2 2000Kohei Adachi A new nonmetric multidimensional scaling method is devised to analyze three-way data concerning inter-stimulus similarities obtained from many subjects. It is assumed that subjects are classified into a small number of clusters and that the stimulus configuration is specific to each cluster. Under this assumption, the classification of subjects and the scaling used to derive the configurations for clusters are simultaneously performed using an alternating least-squares algorithm. The monotone regression of ordinal similarity data, the scaling of stimuli and the K -means clustering of subjects are iterated in the algorithm. The method is assessed using a simulation and its practical use is illustrated with the analysis of real data. Finally, some extensions are considered. [source] Automatic identification of seasonal transfer function models by means of iterative stepwise and genetic algorithmsJOURNAL OF TIME SERIES ANALYSIS, Issue 1 2008Monica Chiogna Abstract., In this article, we introduce an automatic identification procedure for transfer function models. These models are commonplace in time-series analysis, but their identification can be complex. To tackle this problem, we propose to couple a nonlinear conditional least-squares algorithm with a genetic search over the model space. We illustrate the performances of our proposal by examples on simulated and real data. [source] Time-series photometric spot modelling , VI.ASTRONOMISCHE NACHRICHTEN, Issue 3 2003A new computer code, its application to 23 years of photometry of the active giant IM Pegasi Abstract We present and apply a new computer program named SpotModeL to analyze single and multiple bandpass photometric data of spotted stars. It is based on the standard analytical formulae from Budding and Dorren. The program determines the position, size, and temperature of up to three spots by minimizing the fit residuals with the help of the Marquardt-Levenberg non-linear least-squares algorithm. We also expand this procedure to full time-series analysis of differential data, just as real observations would deliver. If multi-bandpass data are available, all bandpasses can be treated simultaneously and thus the spot temperature is solved for implicitly. The program may be downloaded and used by anyone. In this paper, we apply our code to an ,23 year long photometric dataset of the spotted RS CVn giant IM Peg. We extracted and modelled 33 individual light curves, additionally, we fitted the entire V dataset in one run. The resulting spot parameters reflect the long term light variability and reveal two active longitudes on the substellar point and on the antipode. The radius and longitude of the dominant spot show variations with 29.8 and 10.4 years period, respectively. Our multicolour data suggests that the spot temperature is increasing with the brightening of the star. The average spot temperature from V, IC is 3550 ± 150 K or approximately 900 K below the effective temperature of the star. [source] | ||||||||||