隱含并行性 的英文怎麼說

中文拼音 [yǐnhánbīnghángxìng]
隱含并行性 英文
implicit parallelism
  • 隱 : Ⅰ動詞（隱瞞; 隱藏） hide; conceal Ⅱ形容詞1 （隱藏不露） hidden from view; concealed 2 （潛伏的; ...
  • 含 : 動詞1 （東西放在嘴裏 不咽下也不吐出） keep in the mouth 2 （藏在裏面; 包含） contain 3 （帶有某種...
  • 行 : 行Ⅰ名詞1 （行列） line; row 2 （排行） seniority among brothers and sisters:你行幾? 我行三。where...
  • 性 : Ⅰ名詞1 （性格） nature; character; disposition 2 （性能; 性質） property; quality 3 （性別） sex ...
  • 隱含 : implication
  1. In this text, we first do some research on the genetic algorithm about clustering, discuss about the way of coding and the construction of fitness function, analyze the influence that different genetic manipulation do to the effect of cluster algorithm. then analyze and research on the way that select the initial value in the k - means algorithm, we propose a mix clustering algorithm to improve the k - means algorithm by using genetic algorithm. first we use k - learning genetic algorithm to identify the number of the clusters, then use the clustering result of the genetic clustering algorithm as the initial cluster center of k - means clustering. these two steps are finished based on small database which equably sampling from the whole database, now we have known the number of the clusters and initial cluster center, finally we use k - means algorithm to finish the clustering on the whole database. because genetic algorithm search for the best solution by simulating the process of evolution, the most distinct trait of the algorithm is connotative parallelism and the ability to take advantage of the global information, so the algorithm take on strong steadiness, avoid getting into the local

    本文首先對聚類分析的遺傳演算法進行了研究，討論了聚類問題的編碼方式和適應度函數的構造方案與計算方法，分析了不同遺傳操作對聚類演算法的性能和聚類效果的影響意義。然後對k - means演算法中初值的選取方法進行了分析和研究，提出了一種基於遺傳演算法的k - means聚類改進（混合聚類演算法） ，在基於均勻采樣的小樣本集上用k值學習遺傳演算法確定聚類數k ，用遺傳聚類演算法的聚類結果作為k - means聚類的初始聚類中心，最後在已知初始聚類數和初始聚類中心的情況下用k - means演算法對完整數據集進行聚類。由於遺傳演算法是一種通過模擬自然進化過程搜索最優解的方法，其顯著特點是隱含并行性和對全局信息的有效利用的能力，所以新的改進演算法具有較強的穩健性，可避免陷入局部最優，大大提高聚類效果。
  2. Thirdly, genetic algorithm is a kind of search and optimization method simulating the life evolution mechanism, which has the advantages of global optimization and implicit concurrency

    遺傳演算法是一種模擬生命進化機制的搜索和優化方法，與常規優化演算法相比，具有隱含并行性和全局搜索特性，因此選擇遺傳演算法進行尋優計算。
  3. Genetic algorithm is a random searching method which simulates natural selection and evolution. this method has some advantages that other usual methods do n ' t have because of its two characters - - - - - - implicit parallelism and global searching

    遺傳演算法是模仿自然選擇與進化的隨機搜索方法，由於其隱含并行性和全局搜索特性，使其具有其他常規優化演算法無法擁有的優點。
  4. Recently years, there is a new optimization method named genetic algorithms ( ga ) which is based on the numbers of genus groups. this method is a kind of random searching method which simulated natural selection and evolution. compared with traditional optimization method, genetic algorithms has two notable characters. one character is latent parallel and the other is seaching in the whole area. and genetic algorithms has some advantage which traditional method do n ' t have, for example, in genetic algorithms we did n ' t need the calculation of grade

    遺傳演算法[ geneticalgorithms ，簡稱ga ]是近些年來出現的一種模仿自然選擇與進化的基於種群數目的隨機搜索演算法，是優化領域的一個新成員。與常規優化演算法相比，遺傳演算法具有隱含并行性和全局搜索特性這兩大顯著特徵，並具有一些常規優化演算法所無法擁有的優點，如不需梯度運算等。
  5. And because of its independence, global optimization and implicit parallelism, ga is developed and applied by more and more people

    由於其具有不依賴于問題模型的特性、全局最優性、隱含并行性等特點，正越來越激起人們研究與應用的興趣。
  6. Ga is a computational models of the human evolution, with implicit parallelism and capacity of using effectively global information

    遺傳演算法（ ga ）是一種通過模擬自然進化過程搜索最優解的方法，其顯著特點是隱含并行性和對全局信息的有效利用能力。
  7. The paper discusses the basic theory of genetic algorithms including schemate theorem, building block hypothesis, implicit parallelism, the analysis of astringency and so on, as the theoretical base of application

    在對遺傳演算法的闡述中，討論了遺傳演算法的基本原理，包括模式定理、積木塊假設、隱含并行性和遺傳演算法的收斂性分析等，作為後面遺傳演算法應用的理論依據。
  8. Compared with traditional optimization methods, genetic algorithm has two notable characters. one is the latent parallelism and the other is searching in the whole area. genetic algorithm has some advantages which traditional methods do n ' t have

    與常規的優化演算法相比，遺傳演算法具有隱含并行性和全局收斂性兩大顯著特徵，並且具有常規優化方法所沒有的優點，如不需要梯度計算等。
  9. Genetic algorithms are search algorithms based on the mechanics of natural selection and natural genetics. it is widely used in many kinds of fields because of its less - dependency of optimization problem, simplicity, robustness and implicit parallelism

    遺傳演算法是模擬遺傳學和自然選擇機理構造的一種搜索演算法，因其對優化問題的弱依賴性、求解的簡單性和魯棒性、隱含并行性等特點被廣泛應用於當前的各個領域。
  10. Genetic algorithm is an fresh subject in recent years, it is a search algorithm based on the mechanics of natural selection and natural genetics. it is widely used in many kinds of fields because of its less - dependency of optimization problem, simplicity robustness and implicit parallelism

    遺傳演算法是近年來新興的一門學科，是模擬遺傳學和自然選擇機理構造的一種搜索演算法，因其對優化問題的弱依賴性、求解的非線性和魯棒性、隱含并行性等特點被廣泛應用於當前的各個領域。
  11. The portable, extensible toolkit for scientific computation, petsc, has become a model of the high performance numerical software which gains huge attention and wide use in commuting community throughout the world. our selection of petsc as the target model of studying is based on following reasons : petsc uses mpi for all parallel communication, which is most fittable for general scalable computing, especially on our pc - cluster platform. petsc is a general purpose suite : of tools for the scalable solution of partial differential equations and related problems, which is much in accord with our research focus

    可移植、可擴展科學計算軟體包petsc是近來在國際上很受關注、應用廣泛的高性能并行數值軟體開發典範之一，我們對它的重點學習與研究主要出於以下考慮： petsc基於mpi并行程序設計平臺，適合於我們常用的并行計算機尤其是機群系統；它以偏微分方程、代數方程求解功能為實現重點，非常切合於我們的主要研究方向和應用需求；尤為重要的是，在其良好的軟體使用模式和執行性能下隱含的先進軟體設計思想和程序實現方案，對我們今後的數值軟體開發很有借鑒意義。
  12. Gasa is characteristic of many advantages, such as the calculating robustness, implied inherent parallelism, global searching and local convergence. these advantages are integrated in gcs in our method and make the constraint problems solved robustly and efficiently. based on such approach, the ecological niche ideal is further integrated with gasa

    由於遺傳模擬退火演算法本身具有很多優點：很強的計算魯棒性、隱含的內在并行性、全局搜索與局部快速收斂能力，因此將遺傳模擬退火演算法與約束求解相結合大大提高了約束求解的魯棒性和效率。
  13. Control systems in modern automatic engineering are nonlinear, time - changed and indefinite. lt is difficult to model by traditional method, even sometime impossible. under these circumstances we should apply model identification to gain the approximate model of object for effective control, there are many models to be chosen, fuzzy model is one of them, it is put forward with the development of fuzzy control. fuzzy model has characteristics of general approximation and strong nonlinear, it is fit for describing complex, nonlinear systems. to avoid rules expansion when the number of input values are very big. in this paper we apply hierarchical fuzzy model to resolve this problem, we also illustrate it has general approximation to any nonlinear systems. genetic algorithm is a algorithm to help find the best parameters of process. lt has abilities of global optimizing and implicit parallel, it can be generally used for all applications. in our paper we use fuzzy model as predictive model and apply ga to identify fuzzy model ( including hierarchical fuzzy model ), we made experiments to nonlinear predictive systems and got very good results. the paper contains chapters as below : chapter 1 preface

    現代控制工程中的系統多表現為非線性、時變和不確定性，採用傳統的建模方法比較困難，或者根本無法實現，在這種情況下，要實現有效的控制，必須採用模型辨識的方法來獲取對象的近似模型，並加以控制，目前用於系統辨識的模型種類很多，模糊模型是其中的一種，它隨著模糊控制的發展而被人提出，模糊模型具有萬能逼近和強非線性的特點，比較適合於描述復雜非線性系統，為了解決模糊模型在輸入變量較多時規則數膨脹的問題，文中引入遞階型模糊模型，並引證這種結構的通用逼近特性。遺傳演算法是模擬自然界生物進化「優勝劣汰」原理的一種參數尋優演算法，它具有隱含并行性和全局最優化的能力，並且對尋優對象的要求比較低，在工程應用和科學研究中，得到了廣泛的應用，本文將遺傳演算法引入模糊模型的辨識，取得了很好的效果。
  14. Finally, genetic optimization research is summarized on several typical production scheduling problems. after expounding the general idea of genetic algorithm, the comparative advantages in contrast to the traditional algorithm, the basic characteristics of genetic algorithm and its theoretical base, the paper puts emphasis on the efficiency of genetic algorithm in the scheduling of flow shop, and puts forward an improving genetic algorithm : the ordinal genetic algorithm based on the heuristic rules. the new algorithm introduces into the initial group the solution of heuristic algorithm, and in the group structure adopts a strategy of first ordering according to the priority of the adaptive solution, and then defining a new way of choosing probability by segments, which provides more hybridizing opportunity for optimized individuals, and designs variation - control rule to prevent single population and partial optimal solution

    在論述了遺傳演算法的思想、與傳統搜索演算法的比較優勢、遺傳演算法的基本特徵和遺傳演算法的理論基礎（包括模式定理、隱含并行性、基因塊假設、欺騙問題和收斂性定理）后，重點探討了遺傳演算法在flowshop調度問題中的潛力和有效性；結合啟發式規則，提出了一個改進的遺傳演算法?基於啟發式規則的有序遺傳演算法，新演算法在初始種群中引入了啟發式演算法的解，在種群結構上採用了先按適應值優劣排序再分段確定選擇概率的新策略，使優質個體有更多的雜交機會，在變異中設計了變異控制規則，以防種群單一化，而陷入局部優化解。
分享友人
分享到 Line

分享到臉書