Studying the block of center-of-sets (COS) type-reduction (TR) for Takagi Sugeno Kang (TSK) inference-based general type-2 fuzzy logic systems (GT2 FLSs) is meaningful for applying the systems. Blocks of fuzzy reasoni...
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Studying the block of center-of-sets (COS) type-reduction (TR) for Takagi Sugeno Kang (TSK) inference-based general type-2 fuzzy logic systems (GT2 FLSs) is meaningful for applying the systems. Blocks of fuzzy reasoning, COS type-reduction and defuzzification for TSK type GT2 FLSs are first given. According to three kinds of iterative algorithms for computing the centroids of interval type-2 fuzzy sets (IT2 FSs), the paper extends these types of algorithms for studying the COS type-reduction of TSK type GT2 FLSs. Six computer simulation experiments show the computational costs of proposed three kinds of iterative algorithms by computing the outputs of GT2 FLSs, which affords the potential guidance value designers and users of T2 FLSs.
iterative methods to solve linear large-scale discrete problems are well known in the literature. When the linear system is ill-posed and contaminated by noise, some kind of regularization must be applied in order to ...
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iterative methods to solve linear large-scale discrete problems are well known in the literature. When the linear system is ill-posed and contaminated by noise, some kind of regularization must be applied in order to achieve a feasible solution. In the first part of this paper, we revisit briefly some known methods to solve large-scale ill-posed discrete linear problems which are easy to implement and have low computational cost, formulating them in a unified manner and also proposing simple modifications in order to improve their performances. Matrix forms of iterative algorithms can be formulated depending on certain conditions on the blurring process, and have the advantage of avoiding the formation and storage in memory of the matrix that represents the blurring process, which is generally of very large dimension. As an original contribution, in the final part of this paper we present the matrix forms of the iterative algorithms revisited and test them in the problem of restoration of an image degraded by blurring and noise.
The subject of this paper is to show the high efficiency of asynchronism for parallel iterative algorithms in the context of grid computing, that is to say, with machines scattered on a broad geographical scale. The q...
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The subject of this paper is to show the high efficiency of asynchronism for parallel iterative algorithms in the context of grid computing, that is to say, with machines scattered on a broad geographical scale. The question is: does asynchronism help to reduce the communication penalty and the overall computation time of such a given algorithm? The asynchronous programming model is evaluated on two test problems representing two important classes of scientific applications: a stationary linear problem and a non-stationary non-linear problem. They are implemented with a multi-threaded environment and tested on a set of distant heterogeneous machines. Several experiments have been performed allowing us to compare the performances of such asynchronous algorithms and also to analyze their behavior and extract the main possible optimizations for their use in a grid computing context. (c) 2005 Elsevier B.V. All rights reserved.
In this technical note, implicit iterative algorithms with some tunable parameters are developed to solve the coupled Lyapunov matrix equations associated with continuous-time Markovian jump linear systems. A signific...
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In this technical note, implicit iterative algorithms with some tunable parameters are developed to solve the coupled Lyapunov matrix equations associated with continuous-time Markovian jump linear systems. A significant feature of the proposed algorithms is that the iterative sequences are updated by using not only the information in the last step, but also the information in the current step and the previous steps. Also the convergence rate of the proposed algorithms can be significantly improved by choosing appropriate parameters in the algorithms.
The discussion of the causes of image deterioration in the maximum-likelihood estimator (MLE) method of tomographic image reconstruction, initiated with the publication of a stopping rule for that iterative process (E...
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The discussion of the causes of image deterioration in the maximum-likelihood estimator (MLE) method of tomographic image reconstruction, initiated with the publication of a stopping rule for that iterative process (E. Veklerov and J. Llacer, 1987) is continued. The concept of a feasible image is introduced, which is a result of a reconstruction that, if it were a radiation field, could have generated the initial projection data by the Poisson process that governs radioactive decay. From the premise that the result of a reconstruction should be feasible, the shape and characteristics of the region of feasibility in projection space are examined. With a new rule, reconstructions from real data can be tested for feasibility. Results of the tests and reconstructed images for the Hoffman brain phantom are shown. A comparative examination of the current methods of dealing with MLE image deterioration is included.
This paper is concerned with synthesizing VLSI array processors from iterative algorithms. Our primary objective is to obtain the highest processor efficiency but not the shortest completion time. Unlike most of the p...
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This paper is concerned with synthesizing VLSI array processors from iterative algorithms. Our primary objective is to obtain the highest processor efficiency but not the shortest completion time. Unlike most of the previous work that assumes the index space of the given iterative algorithm to be boundless, the proposed method takes into account the effects of the boundaries of the index space. Due to this consideration, the pseudo-dependence relations are excluded, and most of the independent computations can therefore be uniformly grouped. With the method described in this paper, the index space is partitioned into equal-size blocks and the corresponding computations are systematically and uniformly mapped into processing elements. The synthesized VLSI array processors possess the attractive feature of very high processor efficiency, which, in general, is superior to what is derived from the conventional linear transformation methods.
iterative regularization algorithms, such as the conjugate gradient algorithm for least squares problems (CGLS) and the modified residual norm steepest descent (MRNSD) algorithm, are popular tools for solving large-sc...
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iterative regularization algorithms, such as the conjugate gradient algorithm for least squares problems (CGLS) and the modified residual norm steepest descent (MRNSD) algorithm, are popular tools for solving large-scale linear systems arising from image deblurring problems. These algorithms, however, are hindered by a semi-convergence behavior, in that the quality of the computed solution first increases and then decreases. In this paper, in order to overcome the semi-convergence behavior, we propose two iterative algorithms based on soft-thresholding for image deblurring problems. One of them combines CGLS with a denoising technique like soft-thresholding at each iteration and another combines MRNSD with soft-thresholding in a similar way. We prove the convergence of MRNSD and soft-thresholding based algorithm. Numerical results show that the proposed algorithms overcome the semi-convergence behavior and the restoration results are slightly better than those of CGLS and MRNSD with their optimal stopping iterations. (C) 2014 Elsevier Inc. All rights reserved.
We propose a data and knowledge driven approach for SPECT by combining a classical iterative algorithm of SPECT with a convolutional neural network. The classical iterative algorithm, such as ART and ML-EM, is employe...
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We propose a data and knowledge driven approach for SPECT by combining a classical iterative algorithm of SPECT with a convolutional neural network. The classical iterative algorithm, such as ART and ML-EM, is employed to provide the model knowledge of SPECT. A modified U-net is then connected to exploit further features of reconstructed images and data sinograms of SPECT. We provide mathematical formulations for the architecture of the proposed networks. The networks are trained by supervised learning using the technique of mini-batch optimization. We apply the trained networks to the problems of simulated lung perfusion imaging and simulated myocardial perfusion imaging, and numerical results demonstrate their effectiveness of reconstructing source images from noisy data measurements.
Consider the convergence of the projection methods based on a new iterative algorithm for the approximation-solvability of the following class of nonlinear variational inequality (NVI) problems: find an element x* is ...
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Consider the convergence of the projection methods based on a new iterative algorithm for the approximation-solvability of the following class of nonlinear variational inequality (NVI) problems: find an element x* is an element of K such that [T(x*), x - x*] greater than or equal to 0, for all x is an element of K, where T: K --> H is a mapping from a nonempty closed convex subset K of a real Hilbert space H into H. The new iterative procedure is characterized as a nonlinear variational inequality (for any arbitrarily chosen initial point x(0) is an element of K, and for constants rho > 0 and beta > 0) [rhoT (y(k)) + x(k+1) - y(k), x - x(k+1)] greater than or equal to 0;for all x is an element of K, and for k greater than or equal to 0, where [betaT (x(k)) + y(k) - x(k), x - y(k)] greater than or equal to 0, for all x is an element of K. This nonlinear Variational inequality type algorithm has an equivalent projection formula x(k+1) = P-K [y(k) - rhoT (y(k))], where y(k) = P-K [x(k) - betaT (x(k))], for the projection P-K onto K. (C) 2001 Elsevier Science Ltd. All rights reserved.
In this paper, three iterative forms of the LMS learning algorithm were tested for the calculation of the coefficients of FIR filters used as TV Ghost Cancellers. These computational forms are: the Stochastic Gradient...
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In this paper, three iterative forms of the LMS learning algorithm were tested for the calculation of the coefficients of FIR filters used as TV Ghost Cancellers. These computational forms are: the Stochastic Gradient Fixed-Step (SGLMS) [1] and a Variable Step (SLS-CD), [2] algorithms, as well as the Recursive Modified Gram-Schmidt RMGS algorithm [3]. Because of the iterative nature of the selected algorithms, they are very convenient to be used in on-line LTF filter coefficient adaptations. This makes it possible to compute the coefficient values of the ghost canceller, when the sampling of the signal generates a huge amount of data, which is very hard to be handled with a PC. The aforementioned algorithms are written in a very powerful and flexible matrix oriented software, and all the tests were performed using a very flexible TV System Simulator [4]. During the tests, fast convergence of the ghost canceller coefficients to the theoretical values [4] have been observed.
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