Search results for "ESTIMATOR"
showing 10 items of 313 documents
The squared symmetric FastICA estimator
2017
In this paper we study the theoretical properties of the deflation-based FastICA method, the original symmetric FastICA method, and a modified symmetric FastICA method, here called the squared symmetric FastICA. This modification is obtained by replacing the absolute values in the FastICA objective function by their squares. In the deflation-based case this replacement has no effect on the estimate since the maximization problem stays the same. However, in the symmetric case we obtain a different estimate which has been mentioned in the literature, but its theoretical properties have not been studied at all. In the paper we review the classic deflation-based and symmetric FastICA approaches…
Simulated Annealing in Bayesian Decision Theory
1992
Since the seminal paper by Kirkpatrick, Gelatt and Vechhi (1983), a number of papers in the scientific literature refer to simulated annealing as a powerful random optimization method which promises to deliver, within reasonable computing times, optimal or nearly optimal solutions to complex decision problems hitherto forbidding. The algorithm, which uses the physical process of annealing as a metaphor, is special in that, at each iteration, one may move with positive probability to solutions with higher values of the function to minimize, rather than directly jumping to the point with the smallest value within the neighborhood, thus drastically reducing the chances of getting trapped in lo…
Real time estimation of photovoltaic modules characteristics and its application to maximum power point operation
2007
In this paper, an approximate curve fitting method for photovoltaic modules is presented. The operation is based on solving a simple solar cell electrical model by a microcontroller in real time. Only four voltage and current coordinates are needed to obtain the solar module parameters and set its operation at maximum power in any conditions of illumination and temperature. Despite its simplicity, this method is suitable for low cost real time applications, as control loop reference generator in photovoltaic maximum power point circuits. The theory that supports the estimator together with simulations and experimental results are presented.
Parameter optimization for amplify-and-forward relaying with imperfect channel estimation
2009
Cooperative diversity is a promising technology for future wireless networks. In this paper, we consider a cooperative communication system operating in an amplify-and-forward (AF) mode with an imperfectly-known relay fading channel. It is assumed that a pilot symbol assisted modulation (PSAM) scheme with linear minimum mean square estimator (LMMSE) is used for the channel estimation. A simple and easy-to-evaluate asymptotical upper bound (AUB) of the symbol-error-rate (SER) is derived for uncoded AF cooperative systems with quadrature amplitude modulation (QAM) constellations. Based on the AUB, we propose a criterion for the choice of parameters in the PSAM scheme, i.e., the pilot spacing …
H and P Mesh Refinement in the Metal-Forming F.E.M. Analysis
1988
In this paper a comparison between H and P refinement techniques in the metal-forming F.E.M. analysis is carried out in order to evaluate their computational efficiency. The results are compared using a particular error estimator which locally allows determining the workpiece zones where the refinement is necessary.
Graph Topology Learning and Signal Recovery Via Bayesian Inference
2019
The estimation of a meaningful affinity graph has become a crucial task for representation of data, since the underlying structure is not readily available in many applications. In this paper, a topology inference framework, called Bayesian Topology Learning, is proposed to estimate the underlying graph topology from a given set of noisy measurements of signals. It is assumed that the graph signals are generated from Gaussian Markov Random Field processes. First, using a factor analysis model, the noisy measured data is represented in a latent space and its posterior probability density function is found. Thereafter, by utilizing the minimum mean square error estimator and the Expectation M…
Comparing Correlation Matrix Estimators Via Kullback-Leibler Divergence
2011
We use a self-averaging measure called Kullback-Leibler divergence to evaluate the performance of four different correlation estimators: Fourier, Pearson, Maximum Likelihood and Hayashi-Yoshida estimator. The study uses simulated transaction prices for a large number of stocks and different data generating mechanisms, including synchronous and non-synchronous transactions, homogeneous and heterogeneous inter-transaction time. Different distributions of stock returns, i.e. multivariate Normal and multivariate Student's t-distribution, are also considered. We show that Fourier and Pearson estimators are equivalent proxies of the `true' correlation matrix within all the settings under analysis…
State Estimation of a Mobile Manipulator via Non-uniformly Sampled Position Measurements
2011
Abstract We derive an exact deterministic nonlinear estimator to compute the continuous state of a nonlinear time-varying system based on discrete, non uniformly time spaced, state measurements. The system consists of a robot arm mounted on a mobile non holonomic vehicle. The paper also discusses the effect on the estimation error of a bounded input additive noise.
Extended Horizon Adaptive Model Algorithmic Control
1997
Abstract A new, original, robust adaptive control strategy termed Extended Horizon Adaptive Model Algorithmic Control is presented. In EHAMAC, a new, combined, ’single-loop’/’cascade’ adaptive least-squares parameter estimator is coupled with a new, simple but powerful Extended Horizon Model Algorithmic Control so that open-loop stable non-minimum phase systems can be effectively controlled in the time-varying environment. In the new, cascade structure of the ALS estimator, the covariance windup and blowup are totally eliminated. Moreover, the sacramental square-root update of the covariance matrix is no longer needed On the other hand, employing EHMAC facilitates robustness design so that …
Outlier recognition in crystal-structure least-squares modelling by diagnostic techniques based on leverage analysis.
2005
The identification of the actual outliers in a least-squares crystal-structure model refinement and their subsequent elimination from the data set is a non-trivial task that has to be carried out carefully when a high level of accuracy of the estimates is required. One of the most suitable tools for detecting the influence of each data entry on the regression is the identification of ;leverage points'. On the other hand, the recognition of the actual statistical outliers is effectively possible by using some diagnostics as a function of the leverage, such as Cook's distance, DFFITS and FVARATIO. The evaluation of these estimators makes it possible to achieve a reliable identification of the…