Although many successful industrial and semi-industrial applications of the adaptive systems have been reported in the literature, the gap between theory and practice still exists. More specifically it is easy to get ...
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Although many successful industrial and semi-industrial applications of the adaptive systems have been reported in the literature, the gap between theory and practice still exists. More specifically it is easy to get adaptive algorithm to perform well under an idealized simulation framework. In the real world life the adaptive controller should be able to handle non-linearities, non-minimum phase behaviour, load-disturbance, and unmodeled and time-varying dynamics over all the range of operating conditions. In this paper, some practical implications of recent theoretical results in robustness of adaptive controllers are discussed from a design engineer point of view using an adaptive LQ controller. Simulation studies involving a realistic mathematical model of an electrical furnace are given to show the applicability of the considered adaptive controller.
Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by anten...
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ISBN:
(纸本)0780375769
Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the so-called, second order statistics techniques. An adaptive blind channel identification technique based on an off-line least-squares approach has been proposed but this method assuming noise-free case. The method resorts to an adaptive filter with a linear constraint. In this paper, a new approach is proposed that is based on eigenvalue decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contain the channel impulse response. And we present an adaptive algorithm to solve this problem. The performance of the proposed technique is evaluated over a real measured channel and is compared to existing algorithms.
This paper describes an adaptive filter structure which may be used in multi-channel noise-cancelling applications. The proposed structure differs from those presented previously in that it incorporates a lattice filt...
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This paper describes an adaptive filter structure which may be used in multi-channel noise-cancelling applications. The proposed structure differs from those presented previously in that it incorporates a lattice filter framework, rather than tapped-delay-lines. The successive orthogonalization provided by the lattice offers advantages in adaptive convergence rate which cannot be achieved with tapped-delay-lines. In the sections below, we present an explicit description of the general noise-cancelling lattice structure, together with the appropriate adaptive algorithms.
We propose a family of novel affine projection algorithms (APA) with adaptive regularization matrix. Conventional regularized APA (R-APA) uses a fixed and weighted identity matrix for regularization. The proposed algo...
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We propose a family of novel affine projection algorithms (APA) with adaptive regularization matrix. Conventional regularized APA (R-APA) uses a fixed and weighted identity matrix for regularization. The proposed algorithms incorporate a non-identity regularization matrix which is also dynamically updated. The matrix adaptation is based on the normalized stochastic-gradient of mean-square error. As a result, the efficient and robust algorithms are derived. Throughout experiments, we illustrate that the proposed algorithms show better performance than the conventional R-APA and comparable to the RLS algorithm in terms of the convergence rate and the misadjustment error
The paper establishes several robustness, optimality, and convergence properties of the widely used class of instantaneous-gradient adaptive algorithms. The analysis is carried out in a purely deterministic framework ...
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The paper establishes several robustness, optimality, and convergence properties of the widely used class of instantaneous-gradient adaptive algorithms. The analysis is carried out in a purely deterministic framework and assumes no apriori statistical information. It starts with a simple Cauchy-Schwarz inequality for vectors in an Euclidean space and proceeds to derive local and global energy bounds that are shown here to highlight, as well as explain, several relevant aspects of this important class of algorithms.< >
In this paper, we propose a new nonlinear principal component analysis based on a generalized correlation function which we call correntropy. The data is nonlinearly transformed to a feature space, and the principal d...
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In this paper, we propose a new nonlinear principal component analysis based on a generalized correlation function which we call correntropy. The data is nonlinearly transformed to a feature space, and the principal directions are found by eigen-decomposition of the correntropy matrix, which has the same dimension as the standard covariance matrix for the original input data. The correntropy matrix characterizes the nonlinear correlations between the data. With the correntropy function, one can efficiently compute the principal components in the feature space by projecting the transformed data onto those principal directions. We give the derivation of the new method and present simulation results.
We consider a low-complexity adaptive MIMO transmission approach for spatially correlated channels. The proposed scheme adaptively switches between different transmission modes depending on the changing channel condit...
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We consider a low-complexity adaptive MIMO transmission approach for spatially correlated channels. The proposed scheme adaptively switches between different transmission modes depending on the changing channel conditions, as a means to enhance system capacity. Each mode is a combination of a transmission technique (i.e. statistical beamforming, double space-time transmit diversity and spatial multiplexing) and a modulation/coding scheme. We first motivate our adaptive algorithm by deriving new closed-form capacity expressions, and demonstrating significant information theoretic improvements over non-adaptive transmission. We then present a practical method to switch between different modes, based on the channel statistics. Our approach is shown to yield significant improvements in spectral efficiency for typical channel scenarios
In this paper, a novel adaptive soft morphological gradient (ASMG) filter is proposed, based on a combination of the idea of the soft morphological filtering and the adaptive technique. ASMG filtering is an efficient ...
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In this paper, a novel adaptive soft morphological gradient (ASMG) filter is proposed, based on a combination of the idea of the soft morphological filtering and the adaptive technique. ASMG filtering is an efficient nonlinear sharpening method, which can be applied for edge detection. By employing four directional structuring elements, ASMG filtering has the capability of selecting the directional structuring element with the maximum response, whose direction varies depending on the change of directional edges. In addition, by comparing the variance of the moving structuring window with the base variance, the ASMG filter provides an adaptive algorithm for morphological operations. The experimental results show that the ASMG filter is better than the traditional operators in edge detection and noise suppression.
This paper is a first attempt to give formalism to non-linear system design and in which context, related with similar linear processing techniques, they are located. A summary on the relation-ship of linear objective...
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This paper is a first attempt to give formalism to non-linear system design and in which context, related with similar linear processing techniques, they are located. A summary on the relation-ship of linear objectives and classical adaptive algorithms, in non-linear design problems, introduces the paper; giving the potential of random search techniques in order to open the different problems in non-linear objectives that could be handled with them. After, the similarity between probability distribution functions and power spectral density in linear processing is shown. This is supported by a nice example of non-linear system design. Finally, some prospective work is reported in the problem of adaptive companding design.
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