The paper presents a new, modified Newton-Raphson technique for the fast, model-adaptive identification of delays between two unknown stochastic or deterministic signals. The method combines properties of the gradient...
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The paper presents a new, modified Newton-Raphson technique for the fast, model-adaptive identification of delays between two unknown stochastic or deterministic signals. The method combines properties of the gradient, as well as the Newton-Raphson technique. In contrast to the linear convergence of gradient techniques, it has in this special application the same cubic convergence rate as the Newton-Raphson method combined, however, with the larger range of stability of the gradient method. Further properties are a nearly uniform settling time, independent of the bandwidth and the amplitudes of the measured signals, and under certain assumptions a monotone decreasing error. The algorithm leads straightforwardly to a rather simple, discrete hardware implementation, adjusting a parametric model. Possible applications are in the field of velocity and distance measurement. Extensive simulations for the proposed scheme together with results of known methods are presented.
Multi-scale Quantum Harmonic Oscillator algorithm (MQHOA) is a population-based metaheuristic algorithm proposed recently. It has been proved effective and efficient to deal with unimodal and multimodal problems. Alth...
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Multi-scale Quantum Harmonic Oscillator algorithm (MQHOA) is a population-based metaheuristic algorithm proposed recently. It has been proved effective and efficient to deal with unimodal and multimodal problems. Although the mechanism of replacing the worst particle with the fittest individual in MQHOA helps to fasten the iteration process, it can easily lead to premature convergence. Instead of direct replacement, several migration strategies are proposed to maintain the diversity of the population and help to obtain the global optima in difficult function evaluations. The impacts of the migration strategies and individual stabilization on the improvement of the algorithms in their effectiveness, reliability, accuracy and efficiency are well researched. A variety of multi-dimensional unimodal and multimodal benchmark functions are applied to illustrate the optimization performance of the proposed algorithms. Some of the best competitors in MQHOAs with migration strategies are selected to compare with several state-of-the-art stochastic algorithms. Experimental results presented suggest some conclusions: First, the individual stabilization mechanism does not significantly improve the performance of MQHOA. Second, random migration does not obviously help MQHOA perform much better. Third, migration strategies significantly affect the performance of MQHOA, and some of MQHOAs with migration strategies are very competitive to deal with numerical optimization problems. (C) 2019 Published by Elsevier B.V.
In the paper the method of a low pass filter design for a receiver with zero intermediate frequency is presented. Definition of design criteria is performed on the system level instead of specific filter features dete...
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In the paper the method of a low pass filter design for a receiver with zero intermediate frequency is presented. Definition of design criteria is performed on the system level instead of specific filter features determination. The design process uses differential evolution algorithm. Main part of the paper describes a creation of cost function and the method of its application in cooperation with the used algorithm. Conclusion of the paper presents a found solution and its comparison with commonly used filters.
Primal-dual proximal optimization methods have recently gained much interest for dealing with very large-scale data sets encoutered in many application fields such as machine learning, computer vision and inverse prob...
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ISBN:
(纸本)9781467369985
Primal-dual proximal optimization methods have recently gained much interest for dealing with very large-scale data sets encoutered in many application fields such as machine learning, computer vision and inverse problems [1-3]. In this work, we propose a novel random block-coordinate version of such algorithms allowing us to solve a wide array of convex variational problems. One of the main advantages of the proposed algorithm is its ability to solve composite problems involving large-size matrices without requiring any inversion. In addition, the almost sure convergence to an optimal solution to the problem is guaranteed. We illustrate the good performance of our method on a mesh denoising application.
This paper discusses the different types of population-based optimization algorithms. It reviews several works done by a number of authors on these algorithms, highlighting their strengths and weaknesses. Specifically...
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ISBN:
(数字)9781728131269
ISBN:
(纸本)9781728131276
This paper discusses the different types of population-based optimization algorithms. It reviews several works done by a number of authors on these algorithms, highlighting their strengths and weaknesses. Specifically, this paper analyses the main components of a good optimization algorithms which are: Local Search, Global Search, and Randomness and it concludes, that to enjoy a good search, these components must be present in any good stochastic algorithm. Furthermore, the paper asserts that identification of the best solution in every iteration is a necessary criterion. The lack of any of these components, therefore, is the major of reason why some optimizations algorithms have not been as efficient and effective as envisaged at their design phases.
In this paper, we give some results about a multi-drawing urn with random addition matrix. The process that we study is described as: at stage $ n \geq 1 $ n >= 1, we pick out at random m balls, say k white balls a...
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In this paper, we give some results about a multi-drawing urn with random addition matrix. The process that we study is described as: at stage $ n \geq 1 $ n >= 1, we pick out at random m balls, say k white balls and m-k black balls. We inspect the colours and then we return the balls, according to a predefined replacement matrix, together with $ (m-k)\, X_n $ (m-k)Xn white balls and $ k\, Y_n $ kYn black balls. Here, we extend the classical assumption that the random variables $ (X_n,\,Y_n) $ (Xn,Yn) are bounded and i.i.d. We prove a strong law of large numbers and a central limit theorem on the proportion of white balls for the total number of balls after n draws under the following more general assumptions: (i) a finite second-order moment condition in the i.i.d. case;(ii) regular variation type for the first and second moments in the independent case.
The Gibbs Motif Sampler (Gibbs) is a software package for discovering conserved elements in biopolymer sequences. This unit describes the basic operation of the Web-based interface to Gibbs, along with advanced exampl...
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The Gibbs Motif Sampler (Gibbs) is a software package for discovering conserved elements in biopolymer sequences. This unit describes the basic operation of the Web-based interface to Gibbs, along with advanced examples of its use, and the Web interface to dscan, a sequence database search program.
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