monte carlo optimization 中文意思是什麼

monte carlo optimization 解釋
蒙特卡羅優化
  • monte : n. （一種西班牙式）紙牌戲。 three-card monte （起源於墨西哥的）三張牌戲。n. 蒙提〈男子名, Montague 的昵稱〉。
  • carlo : 卡爾羅
  • optimization : n. 最佳化，最優化。
  1. Based on the monte - carlo simulation, optimization of shielding layer material and their thicknesses were obtained for implementing partial shielding of semiconductor devices in space vehicles

    針對我國空間飛行器中對半導體器件較常用的局部屏蔽加固的方法，在m - c計算機模擬的基礎上對屏蔽層材料及其厚度進行了優化設計。
  2. We use genetic programming to optimize the the right hand functions of the ordinary differential equations. adaptive quasi - monte carlo optimization methods are used to optimize the coefficients of the functions

    我們用遺傳程序設計方法優化常微分方程右端的函數，用自適應擬蒙特卡羅優化方法優化函數中的系數。
  3. 1 have studied a couple of topics on monte carlo and quasi - monte carlo methods. this dissertation covers its applications in integration, optimization and simulation

    此論文主要闡述蒙特卡羅和擬蒙特卡羅方法在積分、優化和模擬方面的應用的若干主題。
  4. By comparing these two methods, we show the advantages of quasi - monte carlo method. we also introduce the standard monte carlo random search for optimization. the last but not least application is metropolis algorithms which is the origin of monte carlo method

    第1章介紹了蒙特卡羅和擬蒙特卡羅積分的誤差估計並闡述了擬蒙特卡羅方法的優勢，同時介紹了擬蒙特卡羅的標準優化方法，最後介紹了蒙特卡羅方法的起源? metropolis模擬方法。
  5. Through the integrated applications of design of experiments, response surface model method and monte carlo simulation technique, the paper addresses a 6 - based probabilistic design optimization method

    摘要要將實驗設計、響應表面法和蒙特卡羅模擬技術相結合，提出了基於產品質量工程的6 。
  6. The pheromone - based parameterized probabilistic model for the aco algorithm is presented as the solution construction graph that the combinatorial optimization problem can be mapped on. based on the solution construction graph, the unified framework of the aco algorithm is presented. an iterative update procedure of the solutions distribution in the problem ' s probabilistic model is proposed, that will converge to the optimal solutions with probability one, then the minimum cross - entropy pheromone update rule is proposed to approximate the iterative update procedure by minimizing the cross - entropy distance and monte - carlo sampling

    基於解空間參數化概率分佈模型，首先提出了一個以概率1收斂于最優解的解空間概率分佈的迭代更新過程，然後提出了通過最小化不同分佈間的交互熵距離以及蒙特卡洛采樣來逼近此迭代過程的最小交互熵信息素更新規則，接著分別給出了弧模式以及結點模式信息素分佈模型下的最小交互熵等式。
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