Combination approaches provide an interesting way to improve adaptive filter performance. In this paper, we study the mean-square performance of a convex combination of two transversal filters. The ...individual filters are independently adapted using their own error signals, while the combination is adapted by means of a stochastic gradient algorithm in order to minimize the error of the overall structure. General expressions are derived that show that the method is universal with respect to the component filters, i.e., in steady-state, it performs at least as well as the best component filter. Furthermore, when the correlation between the a priori errors of the components is low enough, their combination is able to outperform both of them. Using energy conservation relations, we specialize the results to a combination of least mean-square filters operating both in stationary and in nonstationary scenarios. We also show how the universality of the scheme can be exploited to design filters with improved tracking performance.
Proportionate adaptive filters, such as those based on the improved proportionate normalized least-mean-square (IPNLMS) algorithm, have been proposed for echo cancellation as an interesting ...alternative to the normalized least-mean-square (NLMS) filter. Proportionate schemes offer improved performance when the echo path is sparse, but are still subject to some compromises regarding their convergence properties and steady-state error. In this paper, we study how combination schemes, where the outputs of two independent adaptive filters are adaptively mixed together, can be used to increase IPNLMS robustness to channels with different degrees of sparsity, as well as to alleviate the rate of convergence versus steady-state misadjustment tradeoff imposed by the selection of the step size. We also introduce a new block-based combination scheme which is specifically designed to further exploit the characteristics of the IPNLMS filter. The advantages of these combined filters are justified theoretically and illustrated in several echo cancellation scenarios.
This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on the support vector method (SVM) for identification applications. A statistical analysis of the ...characteristics of the proposed method is carried out. An analytical relationship between residuals and SVM-ARMA coefficients allows the linking of the fundamentals of SVM with several classical system identification methods. Additionally, the effect of outliers can be cancelled. Application examples show the performance of SVM-ARMA algorithm when it is compared with other system identification methods.
Among all adaptive filtering algorithms, Widrow and Hoff's least mean square (LMS) has probably become the most popular because of its robustness, good tracking properties and simplicity. A drawback ...of LMS is that the step size implies a compromise between speed of convergence and final misadjustment. To combine different speed LMS filters serves to alleviate this compromise, as it was demonstrated by our studies on a two filter combination that we call combination of LMS filters (CLMS). Here, we extend this scheme in two directions. First, we propose a generalization to combine multiple LMS filters with different steps that provides the combination with better tracking capabilities. Second, we use a different mixing parameter for each weight of the filter in order to make independent their adaption speeds. Some simulation examples in plant identification and noise cancellation applications show the validity of the new schemes when compared to the CLMS filter and to other previous variable step approaches.
Using functional weights in a conventional linear combination architecture is a way of obtaining expressive power and represents an alternative to classical trainable and implicit nonlinear ...transformations. In this brief, we explore this way of constructing binary classifiers, taking advantage of the possibility of generating functional weights by means of a gate with fixed radial basis functions. This particular form of the gate permits training the machine directly with maximal margin algorithms. We call the resulting scheme "feature combiners with gate generated weights for classification." Experimental results show that these architectures outperform support vector machines (SVMs) and Real AdaBoost ensembles in most considered benchmark examples. An increase in the computational design effort due to cross-validation demands is the price to be paid to obtain this advantage. Nevertheless, the operational effort is usually lower than that needed by SVMs.
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear ...autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA 2K ) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based system identification nonlinear models is presented, based on the use of composite Mercer's kernels. This general class can improve model flexibility by emphasizing the input-output cross information (SVM-ARMA 4K ), which leads to straightforward and natural combinations of implicit and explicit ARMA models (SVR-ARMA 2K and SVR-ARMA 4K ). Capabilities of these different SVM-based system identification schemes are illustrated with two benchmark problems
The broadcast scheduling problem (BSP) arises in frame design for packet radio networks (PRNs). The frame structure determines the main communication parameters: communication delay and throughput. ...The BSP is a combinatorial optimization problem which is known to be NP-hard. To solve it, we propose an algorithm with two main steps which naturally arise from the problem structure: the first one tackles the hardest contraints and the second one carries out the throughput optimization. This algorithm combines a Hopfield neural network for the constraints satisfaction and a genetic algorithm for achieving a maximal throughput. The algorithm performance is compared with that of existing algorithms in several benchmark cases; in all of them, our algorithm finds the optimum frame length and outperforms previous algorithms in the resulting throughput.
This paper presents a novel scheme for nonlinear acoustic echo cancellation based on adaptive Volterra Filters with linear and quadratic kernels, which automatically prefers those diagonals ...contributing most to the output of the quadratic kernel with the goal of minimizing the overall mean-square error. In typical echo cancellation scenarios, not all coefficients will be equally relevant for the modeling of the nonlinear echo, but coefficients close to the main diagonal of the second-order kernel will describe most of the nonlinear echo distortions, such that not all diagonals need to be implemented. However, it is difficult to decide the most appropriate number of diagonals a priori, since there are many factors that influence this decision, such as the energy of the nonlinear echo, the shape of the room impulse response, or the step size used for the adaptation of kernel coefficients. Our proposed scheme incorporates adaptive scaling factors that control the influence of each group of adjacent diagonals contributing to the quadratic kernel output. An appropriate selection of these factors serves to emphasize or neglect diagonals of the model as required by the present situation. We provide adaptation rules for these factors based on previous works on combination of adaptive filters, and comprehensive simulations showing the reduced gradient noise reached by the new echo canceller.
Real AdaBoost With Gate Controlled Fusion Mayhua-Lopez, E.; Gomez-Verdejo, V.; Figueiras-Vidal, A. R.
IEEE transaction on neural networks and learning systems,
12/2012, Letnik:
23, Številka:
12
Journal Article
In this brief, we propose to increase the capabilities of standard real AdaBoost (RAB) architectures by replacing their linear combinations with a fusion controlled by a gate with fixed kernels. ...Experimental results in a series of well-known benchmark problems support the effectiveness of this approach in improving classification performance. Although the need for cross-validation processes obviously leads to higher training requirements and more computational effort, the operation load is never much higher; in many cases it is even lower than that of competitive RAB schemes.
A Support Vector Machine MUSIC Algorithm El Gonnouni, A.; Martinez-Ramon, Manel; Rojo-Alvarez, J. L. ...
IEEE transactions on antennas and propagation,
10/2012, Letnik:
60, Številka:
10
Journal Article
Recenzirano
This paper introduces a new Support Vector Machine (SVM) formulation for the direction of arrival (DOA) estimation problem. We establish a theoretical relationship between the Minimum Variance ...Distortionless Response (MVDR) and the MUltiple SIgnal Characterization (MUSIC) methods. This leads naturally to the derivation of an SVM-MUSIC algorithm, which combines the benefits of subspace methods with those of SVM. Spatially smoothed versions and a recursive form of the algorithms exhibit good performance against coherent signals. We test the method's performance in scenarios with noncoherent and coherent signals, and in small-sample size-situations obtaining an improved performance in comparison with existing standard approaches.
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