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Conference on Modeling and Simulation for Military Applications

作者： Hong, Seng M. Thiele, Gary A. Penno, Robert P. Air Force Res Lab Wright Patterson AFB OH 45433 USA Univ Dayton Dayton OH 45469 USA

ISBN: (纸本)0819462845

The computation or prediction of plane wave back scattered field is one of the major design considerations of future aircraft and weapon systems. The task of computing the electromagnetic backscattered field of an air-frame structure is by no means a new endeavor. Whereas predicting a minimal backscattered field return under the manipulation of airframe geometry in the context of multidisciplinary design is considered the most prudent approach to obtain the optimal solution and is a new endeavor. The objective of this paper is to develop a mathematical method to couple the backscattered field with the defined aerodynamic performance constraints in the design process of future airframes. This paper will address the coupling of the electromagnetics discipline in a Multidisciplinary Design Optimization (MDO) scheme that includes the mathematical optimization of aerodynamics, or Aero discipline, including the coupling effects of aerodynamic performance (maximum value) with backscattered field return (minimum value) of a Zeroth (0(th)) Order Mode wing platform.

关键词： Multidisciplinary Design Optimization stochastic algorithm genetic algorithm mutation aerodynamics electromagnetic backscattered field wing planform angle of attack aspect ratio tapered ratio

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Asymptotic properties of a stochastic em algorithm for estimating mixing proportions

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Communications in Statistics. stochastic Models 1993年第4期9卷 599-613页

作者： Diebolt, Jean Celeux, Gilles CNRS URA 1321 LSTA Université Paris 6 F75252 Paris Cedex 05 4 Place Jussieu France INRIA Rocquencourt F78153 Le Chesnay Cedex France

The purpose of this paper is to study the asymptotic behavior of the stochastic EM algorithm (SEM) in a simple particular case within the mixture context. We consider the estimation of the mixing proportion p of a two-component mixture of densities assumed to be known. We establish that as the sample size N tends to infinity, the stationary distribution of the ergodic Markov chain generated by SEM converges to a Gaussian distribution whose mean is the consistent maximum likelihood estimate of p. The asymptotic variance is proportional to N^-1. © 1993, Taylor & Francis Group, LLC. All rights reserved.

关键词： Asymptotic Behavior EM Mixing Proportions stochastic algorithm

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A stochastic primal-dual algorithm for composite optimization with a linear operator

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 267卷

作者： Wen, Meng Zhang, Yongqiang Tang, Yuchao Cui, Angang Peng, Jigen Xian Polytech Univ Sch Sci Xian 710048 Peoples R China Guangzhou Univ Sch Math & Informat Sci Guangzhou 510006 Peoples R China Xian Shiyou Univ Coll Mat Sci & Engn Xian 710065 Peoples R China Yulin Univ Sch Math & Stat Yulin 719000 Peoples R China

This paper introduces a stochastic primal-dual algorithm tailored for solving optimization problems involving the sum of three functions with a linear operator. Additionally, we conduct a comprehensive analysis of the convergence of our proposed algorithm within a generally convex framework. Our study includes numerical experiments focusing on fused logistic regression and graph-guided regularized logistic regression problems. The results demonstrate that our algorithm outperforms other state-of-the-art methods in terms of efficiency and consistency.

关键词： Compositely optimization Primal-dual algorithm stochastic algorithm Three-operator splitting

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Improved algorithms for stochastic Pore Network Generation for Porous Materials

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TRANSPORT IN POROUS MEDIA 2025年第5期152卷 1-27页

作者： Shi, Chengnan Janssen, Hans Katholieke Univ Leuven Dept Civil Engn Bldg Phys & Sustainable Design B-3001 Leuven Belgium

Pore network modeling is widely applied to investigate transport phenomena in porous media, as this approach allows for efficient and accurate pore-scale simulation. However, the direct extraction of the pore network (PN) from three-dimensional pore structure images can often not be achieved, due to the conflict between the wide pore size range of many porous materials and the limited image size inherent to many imaging techniques. This obstacle is typically overcome by stochastic PN generation, and this paper proposes and assesses improved stochastic algorithms to generate such statistically similar PNs. Four algorithms for geometry generation as well as two algorithms for topology generation are investigated, both qualitatively and quantitatively, for four porous materials with different degrees of complexity. Particularly, with each algorithm, the materials' unsaturated moisture storage and transport properties are simulated and compared. The results demonstrate that, as the pore structure's complexity increases, the basic stochastic algorithms available in the literature do not suffice for an accurate and dependable PN generation. The improved geometry and topology generation algorithms put forward in this paper, on the other hand, highly enhance the reliability of the generated PNs, by reducing the deviations for specific moisture contents and permeabilities by 67-98% on average. The improved stochastic algorithms also set the stage for generating PNs of porous materials with (very) wide pore size ranges, and future research can build on these algorithms to generate full-scale PNs using multiple 3D image sets with different resolutions.

关键词： Pore network model stochastic algorithm Porous material Moisture retention curve Moisture permeability curve

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stochastic algorithms for Solving the Dirichlet Boundary Value Problem for Certain Second-Order Elliptic Equations with Discontinuous Coefficients

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COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2022年第2期62卷 248-253页

作者： Kuznetsov, A. N. Sipin, A. S. Vologda State Univ Vologda 160000 Russia

stochastic algorithms for solving the Dirichlet boundary value problem for a second-order elliptic equation with coefficients having a discontinuity on a smooth surface are considered. It is assumed that the solution is continuous and its normal derivatives on the opposite sides of the discontinuity surface are consistent. A mean value formula in a ball (or an ellipsoid) is proposed and proved. This formula defines a random walk in the domain and provides statistical estimators (on its trajectories) for finding a Monte Carlo solution of the boundary value problem at the initial point of the walk.

关键词： elliptic operator boundary value problem mean value formula random walk stochastic algorithm unbiased estimator

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stochastic algorithmS FOR INVERSE PROBLEMS INVOLVING PDEs AND MANY MEASUREMENTS

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2014年第5期36卷 S3-S22页

作者： Roosta-Khorasani, Farbod van den Doel, Kees Ascher, Uri Univ British Columbia Dept Comp Sci Vancouver BC V6T 1W5 Canada

Inverse problems involving systems of partial differential equations (PDEs) can be very expensive to solve numerically. This is so especially when many experiments, involving different combinations of sources and receivers, are employed in order to obtain reconstructions of acceptable quality. The mere evaluation of a misfit function (the distance between predicted and observed data) often requires hundreds and thousands of PDE solves. This article develops and assesses dimensionality reduction methods, both stochastic and deterministic, to reduce this computational burden. We assume that all experiments share the same set of receivers and concentrate on methods for reducing the number of combinations of experiments, called simultaneous sources, that are used at each stabilized Gauss-Newton iteration. algorithms for controlling the number of such combined sources are proposed and justified. Evaluating the misfit approximately, except for the final verification for terminating the process, always involves random sampling. Methods for selecting the combined simultaneous sources, involving either random sampling or truncated SVD, are proposed and compared. Highly efficient variants of the resulting algorithms are identified, and their efficacy is demonstrated in the context of the famous DC resistivity and EIT problems. We present in detail our methods for solving such inverse problems. These methods involve incorporation of a priori information such as piecewise smoothness, bounds on the sought conductivity surface, or even a piecewise constant solution.

关键词： inverse problem stochastic algorithm partial differential equation many experiments DC resistivity EIT

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Three stochastic versions of the EM algorithm for estimating longitudinal Rasch model

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2003年第2期32卷 275-295页

作者： Hamon, A Iovleff, S Univ Bretagne Sud Lab SABRES Vannes France

The Rasch model is a latent variable model which is widely used for the analysis of psychological data and recently for the study of quality of life data in medicine. In this article we propose an extension of the Rasch model to the case of repeated measurements of quality of life. The complex form of the likelihood function implies that it is impossible to find directly estimates. The classical EM algorithm is not a practical solution in the present context. So we propose to use three stochastic versions of the EM: the MCEM, SEM algorithms and a new algorithm we call SGEM. Some theoretical properties of these three algorithms are exposed. Illustrations with simulated and real data are given.

关键词： Rasch model stochastic algorithm longitudinal binary data

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A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters

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OPTIK 2020年 223卷 165277-165277页

作者： Kumar, C. Raj, T. Dharma Premkumar, M. Raj, T. Dhanesh M Kumarasamy Coll Engn Dept EEE Thalavapalayam Tamil Nadu India Vins Christian Coll Engn Dept EEE Nagercoil Tamil Nadu India GMR Inst Technol Dept EEE Rajam Andhra Prades India MET Engn Coll Dept EEE Aralvaimozhi Tamil Nadu India

With the help of accurate solar photovoltaic (PV) cell modeling, the PV system's performance can be enhanced. However, PV cell modeling is erroneously caused by inaccurate solar cell parameters. In general, the manufacturers will not provide the required data to model PV cells accurately. Thus, it is essential to get the PV cell parameters effectively. With this primary motivation, this paper presents a new stochastic optimization algorithm for estimating the solar PV cell parameters. Numerous optimization algorithms are discussed in the literature, and nevertheless, due to the convergence towards local minima, the sub-optimal results are produced by most of the algorithms. Thus, in this paper, a new algorithm named as Slime Mould algorithm (SMA) is presented for the solar cell estimation. The proposed algorithm has a new feature called as an exceptional mathematical model with adaptive weights to simulate negative and positive feedback of the propagation wave to find the best path for attaching food with an excellent exploitation tendency and exploratory capacity. The performance of the proposed SMA algorithm is validated by comparing the estimated results with experimental results. The superiority of the SMA algorithm is proved by extensive statistical analysis. In addition, the performance of the proposed algorithm is also compared with the other benchmark meta-heuristics algorithms.

关键词： Convergence Parameter extraction PV cell PV modeling stochastic algorithm

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A stochastic Adaptive Radial Basis Function algorithm for Costly Black-Box Optimization

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Journal of the Operations Research Society of China 2018年第4期6卷 587-609页

作者： Zhe Zhou Fu-Sheng Bai School of Mathematical Sciences Chongqing Normal UniversityChongqing 401331China

In this paper,we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box *** exploration radii in local searches are generated *** iteration point is selected from some randomly generated trial points according to certain criteria.A restarting strategy is adopted to build the restarting version of the *** performance of the presented algorithm and its restarting version are tested on 13 standard numerical *** numerical results suggest that the algorithm and its restarting version are very effective.

关键词： Global optimization Costly black-box optimization Radial basis function stochastic algorithm

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Convergence of a stochastic approximation version of the EM algorithm

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ANNALS OF STATISTICS 1999年第1期27卷 94-128页

作者： Delyon, B Lavielle, V Moulines, E IRISA INRIA F-35042 Rennes France Univ Paris 05 UFR Math & Informat F-75270 Paris 06 France Univ Paris Sud CNRS URA 743 Orsay France Ecole Natl Super Telecommun CNRS URA 820 F-75634 Paris France

The expectation-maximization (EM) algorithm isa powerful computational technique for locating maxima of functions. It is widely used in statistics for maximum likelihood or maximum a posteriori estimation in incomplete data models. In certain situations, however, this method is not applicable because the expectation step cannot be performed in closed form. To deal with these problems, a novel method is introduced, the stochastic approximation EM (SAEM), which replaces the expectation step of the EM algorithm by one iteration of a stochastic approximation procedure. The convergence of the SAEM algorithm is established under conditions that are applicable to many practical situations. Moreover,it is proved that, under mild additional conditions, the attractive stationary points of the SAEM algorithm correspond to the local maxima of the function. presented to support our findings.

关键词： incomplete data optimization maximum likelihood missing data Monte Carlo algorithm EM algorithm simulation stochastic algorithm

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