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cross-entropy method for Laser Defense Applications

JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2025年第1期22卷 53-58页

作者： Hanak, J. Novak, J. Ben-Asher, J. Z. Chudy, P. Brno Univ Technol Fac Informat Technol Brno 61200 Czech Republic Technion Israel Inst Technol Dept Aerospace Engn IL-32000 Haifa Israel Univ Wurzburg Inst Comp Sci D-97074 Wurzburg Germany

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Certified Dimension Reduction for Bayesian Updating with the cross-entropy method

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SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION 2023年第1期11卷 358-388页

作者： Ehre, Max Flock, Rafael Fusseder, Martin Papaioannou, Iason Straub, Daniel Tech Univ Munich Sch Engn & Design Engn Risk Anal Grp D-80333 Munich Germany Tech Univ Denmark Dept Appl Math & Comp Sci DTU Compute DK-2800 Lyngby Denmark Tech Univ Munich Sch Engn & Design D-80333 Munich Germany

In inverse problems, the parameters of a model are estimated based on observations of the model response. The Bayesian approach is powerful for solving such problems;one formulates a prior distribution for the parameter state that is updated with the observations to compute the posterior parameter distribution. Solving for the posterior distribution can be challenging when, e.g., prior and posterior significantly differ from one another and/or the parameter space is high-dimensional. We use a sequence of importance sampling measures that arise by tempering the likelihood to approach inverse problems exhibiting a significant distance between prior and posterior. Each importance sampling measure is identified by cross-entropy minimization as proposed in the context of Bayesian inverse problems in Engel et al. [J. Comput. Phys., 473 (2023), 111746]. To efficiently address problems with high-dimensional parameter spaces, we set up the minimization procedure in a low -dimensional subspace of the original parameter space. The principal idea is to analyze the spectrum of the second-moment matrix of the gradient of the log-likelihood function to identify a suitable subspace. Following Zahm et al. [Math. Comp., 91 (2022), pp. 1789-1835], an upper bound on the Kullback-Leibler divergence between full-dimensional and subspace posterior is provided, which can be utilized to determine the effective dimension of the inverse problem corresponding to a prescribed approximation error bound. We suggest heuristic criteria for optimally selecting the number of model and model gradient evaluations in each iteration of the importance sampling sequence. We investigate the performance of this approach using examples from engineering mechanics set in various parameter space dimensions.

关键词： Bayesian inverse problems high dimensions cross-entropy method importance sampling certified dimension reduction

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RETRACTED: Modified cross-entropy method for evaluating the higher vocational education management quality under the fuzzy number intuitionistic fuzzy sets (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期45卷 3461-3471页

作者： Zhu, Xiaorong Yiwu Ind & Commercial Coll Jinhua Zhejiang Peoples R China

The quality management of higher vocational education has become an important part of talent training in colleges and universities. With the increasing demand for social talents, the adaptability of traditional teaching management concepts has gradually emerged. In this case, the innovation and practice of education quality management will become the key research content of higher vocational colleges in the new era in combination with the actual situation of higher vocational colleges and from the perspective of the overall development of talents in vocational colleges. The higher vocational education management quality evaluation is viewed as the multi-attribute decision-making (MADM). In this paper, the cross-entropy method under The fuzzy number intuitionistic fuzzy sets (FNIFSs) is built based on the traditional cross-entropy method. Firstly, the FNIFSs is introduced. Then, combine the traditional fuzzy cross-entropy method with FNIFSs information, the cross-entropy method is established for MADM under FNIFSs. Finally, a numerical example for higher vocational education management quality evaluation has been given and some comparisons is used to illustrate advantages of cross-entropy method with FNIFSs.

关键词： Multiple attribute decision making (MADM) problems fuzzy number intuitionistic fuzzy sets (FNIFSs) cross-entropy method higher vocational education management quality evaluation

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An improved cross-entropy method for the water pump scheduling optimization problem 2

An improved cross-entropy method for the water pump scheduli...

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2nd IEEE-Industrial-Electronics-Society Annual On-Line Conference (ONCON)

作者： Zeng, Francesco Wang, Jingcheng Xu, Jiahui Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

ISBN: (纸本)9798350357974

The growing demand for urban freshwater supply often corresponds to rising electrical and labor costs for pump stations. Optimizing the operation of water pumps, ensuring they run at their most efficient working points, and improving their scheduling systems offer effective ways to mitigate costs and conserve water resources. These water pump stations and their associated systems can be mathematically modeled, enabling the use of optimization algorithms to assess optimal operating conditions. Conventional optimization methods often fall short when dealing with such complex models, underscoring the significance of investigating recent algorithms. In this study, we expand the research on the cross-entropy method and introduce an improved version to address water pump scheduling optimization. Notable enhancements include an improved update step for the multivariate normal distribution used in sampling, resulting in faster and more accurate outcomes, as well as reduced susceptibility to local minima. Moreover, our adaptive objective function takes into account various cost factors and penalty functions associated with complex constraints, with the primary aim of further minimizing power consumption costs. To validate our approach, we conducted a series of experiments within a simulated environment and on a real-life-inspired scenario modeled after a water pump station located in Shanghai. The results of these experiments demonstrate the adaptability of our optimization method to complex models and constraints and highlight its satisfactory performance outcomes.

关键词： Water pump scheduling optimization improved cross-entropy method

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Long-Term Operating Reserve Assessment of Power Systems with Variable Renewable Generation via cross-entropy method 17

Long-Term Operating Reserve Assessment of Power Systems with...

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17th International Conference on Probabilistic methods Applied to Power Systems (PMAPS)

作者： Cordeiro Vieira, Pedro Cesar Da Rosa, Mauro Augusto Da Silva, Armando M. Leite IFSC Fed Inst Santa Catarina Acad Dept Electrotech Florianopolis SC Brazil INESC P&D Brasil Inst Syst Engn Santos SP Brazil Univ Fed Santa Catarina Dept Elect & Elect Engn UFSC Florianopolis SC Brazil Pontifical Catholic Univ Rio de Janeiro Dept Elect Engn PUC Rio Rio De Janeiro Brazil

ISBN: (纸本)9781665412117

This paper presents an approach to assess power system reliability indices, from the long-term operating reserve perspective, with intermittent renewable generation via cross-entropy (CE) method. The proposed approach relies on the sequential Monte Carlo simulation (MCS) due to its suitable characteristic to represent time dependent events, and CE method due to its ability to accelerate the convergence of the simulation. The CE optimization process is driven so that proper distortions are applied not only to equipment unavailability, but also to load and wind production forecast uncertainties, bearing in mind operating reserve failure events. The methodology is tested using modified configurations of the IEEE RTS.

关键词： cross-entropy method Monte Carlo simulation operating reserve assessment reliability renewable generation

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Enhancing the performance of a bistable energy harvesting device via the cross-entropy method

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NONLINEAR DYNAMICS 2021年第1期103卷 137-155页

作者： Cunha Jr, Americo Rio de Janeiro State Univ UERJ Inst Math & Stat Rio De Janeiro Brazil

This work deals with the solution of a non-convex optimization problem to enhance the performance of an energy harvesting device, which involves a nonlinear objective function and a discontinuous constraint. This optimization problem, which seeks to find a suitable configuration of parameters that maximize the electrical power recovered by a bistable energy harvesting system, is formulated in terms of the dynamical system response and a binary classifier obtained from 0 to 1 test for chaos. A stochastic solution strategy that combines penalization and the cross-entropy method is proposed and numerically tested. Computational experiments are conducted to address the performance of the proposed optimization approach by comparison with a reference solution, obtained via an exhaustive search in a refined numerical mesh. The obtained results illustrate the effectiveness and robustness of the cross-entropy optimization strategy (even in the presence of noise or in moderately higher dimensions), showing that the proposed framework may be a very useful and powerful tool to solve optimization problems involving nonlinear energy harvesting dynamical systems.

关键词： Energy harvesting Nonlinear dynamics cross-entropy method Evolutionary algorithm Non-convex optimization 0-1 test for chaos

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Combining Soft-Actor Critic with cross-entropy method for Policy Search in Continuous Control

Combining Soft-Actor Critic with Cross-Entropy Method for Po...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Hieu Trung Nguyen Khang Tran Ngoc Hoang Luong Univ Informat Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

In this paper, we propose CEM-SAC - a hybridization between the cross-entropy method (CEM), i.e., an estimation-of-distribution algorithm, and the soft-actor critic (SAC), i.e., a state-of-the-art policy gradient algorithm. Our work extends the evolutionary reinforcement learning (ERL) line of research on integrating the robustness of population-based stochastic black-box optimization, that typically assumes little to no problem-specific knowledge, into the training process of policy gradient algorithms, that exploits the sequential decision making nature for efficient gradient estimation. Our hybrid approach, CEM-SAC, exhibits both the stability of CEM and the efficiency of SAC in training policy neural networks of reinforcement learning agents for solving control problems. Experimental result comparisons with the three baselines CEM, SAC, and CEM-TD3, a recently-introduced ERL method that combines CEM and the twin-delayed deep deterministic policy gradient (TD3) algorithm, on a wide range of control tasks in the MuJoCo benchmarks confirm the enhanced performance of our proposed CEM-SAC. The source code is available at https://***/ELO-Lab/CEM-SAC.

关键词： Reinforcement learning Evolutionary computation cross-entropy method Soft actor-critic Policy search

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A Pervasive Model to Evaluate and Ranking the Best 5G Mobile by cross-entropy and TOPSIS method

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第4期38卷

作者： Dwivedi, Pankaj Prasad Sharma, Dilip Kumar Oriental Inst Sci & Technol Dept Math Bhopal India Jaypee Univ Engn & Technol Dept Math Guna India

The development of the new 5G mobile generation is among the most significant and recent breakthroughs in communications and information technology. Compared to older networks, the most recent wireless cellular technology offers more capacity, dependable connections, and quicker upload and download rates. The newest generation of mobile phones, or 5G phones, have significantly quicker speeds and are more dependable. They have the potential to completely change the way we access social media, websites, and information. This study was carried out to address the issue that many 5G mobile users frequently face when deciding which phone is best for them based on many factors. This study contributes to putting out a strategy for assessing and ranking the various 5G mobile possibilities in light of consumers' order of predilections in multicriteria decision-making (MCDM). In this study, we studied 15 different 5G mobiles based on data from mobile companies with 10 criteria. First, the most appealing criteria that affect a person's decision about a 5G mobile are determined. This is accomplished by a survey of the target audience, the expertise of industry professionals in the communications area, and research in the literature. For assessing the 5G mobile alternatives, the TOPSIS approach is employed, and the relative weights of assessment criteria are calculated by the cross-entropy approach, which is new in terms of MCDM. This study demonstrates the usefulness of the suggested strategy.

关键词： 5G mobile cross-entropy method mobile selection multicriteria decision-making normalization TOPSIS method user preference

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Distributed storage placement policy for minimizing frequency deviations: A combinatorial optimization approach based on enhanced cross-entropy method

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 134卷 107332-107332页

作者： Machlev, R. Chowdhury, N. R. Belikov, J. Levron, Y. Technion Israel Inst Technol Andrew & Erna Viterbi Fac Elect Engn IL-3200003 Haifa Israel Tallinn Univ Technol Dept Software Sci Akad Tee 15a EE-12618 Tallinn Estonia

The paper addresses the well-known challenge of allocating storage units within the grid to minimize frequency deviations during transients. The current research is mainly motivated by a sequence of recent works that focus on location of inertia and storage units, considering the limitations of the transmission network, and its spatial effects. One possible drawback of these algorithms is their numerical complexity. To address this gap, this paper studies the storage distribution problem using the general framework of combinatorial optimization, and proposes algorithms to solve it. Among them, one solution is based on exhaustive search, while the others strongly rely on the cross-entropy method. We show that the latter is especially suitable for this application, since it can efficiently solve high-dimensional combinatorial optimization problems. Since the CE method may converge to sub-optimal solutions if the number of storage units increases significantly, this paper also proposes an enhanced CE method which solves the distribution problem considering the assumption that the locations of potential transients are known. In this regard, a key result is that the enhanced CE method convergences faster to the optimal solution and significantly reduces the number of computations.

关键词： Distributed energy storage Grid supporting inverters Frequency stability Droop control Combinatorial optimization cross-entropy method

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Safe learning-based gradient-free model predictive control based on cross-entropy method

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期110卷 104731-104731页

作者： Zheng, Lei Yang, Rui Wu, Zhixuan Pan, Jiesen Cheng, Hui Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

In this paper, a safe and learning-based control framework for model predictive control (MPC) is proposed to optimize nonlinear systems with a non-differentiable objective function under uncertain environmental disturbances. The control framework integrates a learning-based MPC with an auxiliary controller in a way of minimal intervention. The learning-based MPC augments the prior nominal model with incremental Gaussian Processes to learn the uncertain disturbances. The cross-entropy method (CEM) is utilized as the sampling-based optimizer for the MPC with a non-differentiable objective function. A minimal intervention controller is devised with a control Lyapunov function and a control barrier function to guide the sampling process and endow the system with high probabilistic safety. The proposed algorithm shows a safe and adaptive control performance on a simulated quadrotor in the tasks of trajectory tracking and obstacle avoidance under uncertain wind disturbances.

关键词： Model predictive control Learning-based control cross-entropy method Minimal intervention controller

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